bluuebunny/crossref_metadata_2025 huggingface dataset:
Learning Dynamic Generator Model by Alternating Back-Propagation through Time
Authors: Xie Jianwen , Gao Ruiqi , Zheng Zilong , Zhu Song-Chun , Wu Ying Nian
Published: August 2019

This paper studies the dynamic generator model for spatialtemporal processes such as dynamic textures and action sequences in video data. In this model, each time frame of the video sequence is generated by a generator model, which is a non-linear transformation of a latent state vector, where the non-linear transformation is parametrized by a top-down neural network. The sequence of latent state vectors follows a non-linear auto-regressive model, where the state vector of the next frame is a non-linear transformation of the state vector of the current frame as well as an independent noise vector that provides randomness in the transition. The non-linear transformation of this transition model can be parametrized by a feedforward neural network. We show that this model can be learned by an alternating back-propagation through time algorithm that iteratively samples the noise vectors and updates the parameters in the transition model and the generator model. We show that our training method can learn realistic models for dynamic textures and action patterns.
Structured Bayesian Networks: From Inference to Learning with Routes
Authors: Shen Yujia , Goyanka Anchal , Darwiche Adnan , Choi Arthur
Published: August 2019

Structured Bayesian networks (SBNs) are a recently proposed class of probabilistic graphical models which integrate background knowledge in two forms: conditional independence constraints and Boolean domain constraints. In this paper, we propose the first exact inference algorithm for SBNs, based on compiling a given SBN to a Probabilistic Sentential Decision Diagram (PSDD). We further identify a tractable subclass of SBNs, which have PSDDs of polynomial size. These SBNs yield a tractable model of route distributions, whose structure can be learned from GPS data, using a simple algorithm that we propose. Empirically, we demonstrate the utility of our inference algorithm, showing that it can be an order-ofmagnitude more efficient than more traditional approaches to exact inference. We demonstrate the utility of our learning algorithm, showing that it can learn more accurate models and classifiers from GPS data.
Personalized Robot Tutoring Using the Assistive Tutor POMDP (AT-POMDP)
Authors: Ramachandran Aditi , Sebo Sarah Strohkorb , Scassellati Brian
Published: August 2019

Selecting appropriate tutoring help actions that account for both a student’s content mastery and engagement level is essential for effective human tutors, indicating the critical need for these skills in autonomous tutors. In this work, we formulate the robot-student tutoring help action selection problem as the Assistive Tutor partially observable Markov decision process (AT-POMDP). We designed the AT-POMDP and derived its parameters based on data from a prior robot-student tutoring study. The policy that results from solving the AT-POMDP allows a robot tutor to decide upon the optimal tutoring help action to give a student, while maintaining a belief of the student’s mastery of the material and engagement with the task. This approach is validated through a between-subjects field study, which involved 4th grade students (n=28) interacting with a social robot solving long division problems over five sessions. Students who received help from a robot using the AT-POMDP policy demonstrated significantly greater learning gains than students who received help from a robot with a fixed help action selection policy. Our results demonstrate that this robust computational framework can be used effectively to deliver diverse and personalized tutoring support over time for students.
Energy Confused Adversarial Metric Learning for Zero-Shot Image Retrieval and Clustering
Authors: Chen Binghui , Deng Weihong
Published: August 2019

Deep metric learning has been widely applied in many computer vision tasks, and recently, it is more attractive in zeroshot image retrieval and clustering (ZSRC) where a good embedding is requested such that the unseen classes can be distinguished well. Most existing works deem this ’good’ embedding just to be the discriminative one and thus race to devise powerful metric objectives or hard-sample mining strategies for leaning discriminative embedding. However, in this paper, we first emphasize that the generalization ability is a core ingredient of this ’good’ embedding as well and largely affects the metric performance in zero-shot settings as a matter of fact. Then, we propose the Energy Confused Adversarial Metric Learning (ECAML) framework to explicitly optimize a robust metric. It is mainly achieved by introducing an interesting Energy Confusion regularization term, which daringly breaks away from the traditional metric learning idea of discriminative objective devising, and seeks to ’confuse’ the learned model so as to encourage its generalization ability by reducing overfitting on the seen classes. We train this confusion term together with the conventional metric objective in an adversarial manner. Although it seems weird to ’confuse’ the network, we show that our ECAML indeed serves as an efficient regularization technique for metric learning and is applicable to various conventional metric methods. This paper empirically and experimentally demonstrates the importance of learning embedding with good generalization, achieving state-of-theart performances on the popular CUB, CARS, Stanford Online Products and In-Shop datasets for ZSRC tasks. Code available at http://www.bhchen.cn/.
A Robust and Efficient Algorithm for the PnL Problem Using Algebraic Distance to Approximate the Reprojection Distance
Authors: Zhou Lipu , Yang Yi , Abello Montiel , Kaess Michael
Published: August 2019

This paper proposes a novel algorithm to solve the pose estimation problem from 2D/3D line correspondences, known as the Perspective-n-Line (PnL) problem. It is widely known that minimizing the geometric distance generally results in more accurate results than minimizing an algebraic distance. However, the rational form of the reprojection distance of the line yields a complicated cost function, which makes solving the first-order optimality conditions infeasible. Furthermore, iterative algorithms based on the reprojection distance are time-consuming for a large-scale problem. In contrast to previous works which minimize a cost function based on an algebraic distance that may not approximate the reprojection distance of the line, we design two simple algebraic distances to gradually approximate the reprojection distance. This speeds up the computation, and maintains the robustness of the geometric distance. The two algebraic distances result in two polynomial cost functions, which can be efficiently solved. We directly solve the first-order optimality conditions of the first problem with a novel hidden variable method. This algorithm makes use of the specific structure of the resulting polynomial system, therefore it is more stable than the general Gröbner basis polynomial solver. Then, we minimize the second polynomial cost function by the damped Newton iteration, starting from the solution of the first cost function. Experimental results show that the first step of our algorithm is already superior to the state-of-the-art algorithms in terms of accuracy and applicability, and faster than the algorithms based on Gröbner basis polynomial solver. The second step yields comparable results to the results from minimizing the reprojection distance, but is much more efficient. For speed, our algorithm is applicable to real-time applications.
Free VQA Models from Knowledge Inertia by Pairwise Inconformity Learning
Authors: Zhou Yiyi , Ji Rongrong , Su Jinsong , Li Xiangming , Sun Xiaoshuai
Published: August 2019

In this paper, we uncover the issue of knowledge inertia in visual question answering (VQA), which commonly exists in most VQA models and forces the models to mainly rely on the question content to “guess” answer, without regard to the visual information. Such an issue not only impairs the performance of VQA models, but also greatly reduces the credibility of the answer prediction. To this end, simply highlighting the visual features in the model is undoable, since the prediction is built upon the joint modeling of two modalities and largely influenced by the data distribution. In this paper, we propose a Pairwise Inconformity Learning (PIL) to tackle the issue of knowledge inertia. In particular, PIL takes full advantage of the similar image pairs with diverse answers to an identical question provided in VQA2.0 dataset. It builds a multi-modal embedding space to project pos./neg. feature pairs, upon which word vectors of answers are modeled as anchors. By doing so, PIL strengthens the importance of visual features in prediction with a novel dynamic-margin based triplet loss that efficiently increases the semantic discrepancies between pos./neg. image pairs. To verify the proposed PIL, we plug it on a baseline VQA model as well as a set of recent VQA models, and conduct extensive experiments on two benchmark datasets, i.e., VQA1.0 and VQA2.0. Experimental results show that PIL can boost the accuracy of the existing VQA models (1.56%-2.93% gain) with a negligible increase in parameters (0.85%-5.4% parameters). Qualitative results also reveal the elimination of knowledge inertia in the existing VQA models after implementing our PIL.
Singe Image Rain Removal with Unpaired Information: A Differentiable Programming Perspective
Authors: Zhu Hongyuan , Peng Xi , Zhou Joey Tianyi , Yang Songfan , Chanderasekh Vijay , Li Liyuan , Lim Joo-Hwee
Published: August 2019

Single image rain-streak removal is an extremely challenging problem due to the presence of non-uniform rain densities in images. Previous works solve this problem using various hand-designed priors or by explicitly mapping synthetic rain to paired clean image in a supervised way. In practice, however, the pre-defined priors are easily violated and the paired training data are hard to collect. To overcome these limitations, in this work, we propose RainRemoval-GAN (RRGAN), the first end-to-end adversarial model that generates realistic rain-free images using only unpaired supervision. Our approach alleviates the paired training constraints by introducing a physical-model which explicitly learns a recovered images and corresponding rain-streaks from the differentiable programming perspective. The proposed network consists of a novel multiscale attention memory generator and a novel multiscale deeply supervised discriminator. The multiscale attention memory generator uses a memory with attention mechanism to capture the latent rain streaks context at different stages to recover the clean images. The deeply supervised multiscale discriminator imposes constraints at the recovered output in terms of local details and global appearance to the clean image set. Together with the learned rainstreaks, a reconstruction constraint is employed to ensure the appearance consistent with the input image. Experimental results on public benchmark demonstrates our promising performance compared with nine state-of-the-art methods in terms of PSNR, SSIM, visual qualities and running time.
Deep Embedding Features for Salient Object Detection
Authors: Zhuge Yunzhi , Zeng Yu , Lu Huchuan
Published: August 2019

Benefiting from the rapid development of Convolutional Neural Networks (CNNs), some salient object detection methods have achieved remarkable results by utilizing multi-level convolutional features. However, the saliency training datasets is of limited scale due to the high cost of pixel-level labeling, which leads to a limited generalization of the trained model on new scenarios during testing. Besides, some FCN-based methods directly integrate multi-level features, ignoring the fact that the noise in some features are harmful to saliency detection. In this paper, we propose a novel approach that transforms prior information into an embedding space to select attentive features and filter out outliers for salient object detection. Our network firstly generates a coarse prediction map through an encorder-decorder structure. Then a Feature Embedding Network (FEN) is trained to embed each pixel of the coarse map into a metric space, which incorporates much attentive features that highlight salient regions and suppress the response of non-salient regions. Further, the embedded features are refined through a deep-to-shallow Recursive Feature Integration Network (RFIN) to improve the details of prediction maps. Moreover, to alleviate the blurred boundaries, we propose a Guided Filter Refinement Network (GFRN) to jointly optimize the predicted results and the learnable guidance maps. Extensive experiments on five benchmark datasets demonstrate that our method outperforms state-of-the-art results. Our proposed method is end-to-end and achieves a realtime speed of 38 FPS.
Large Scale Personalized Categorization of Financial Transactions
Authors: Lesner Christopher , Ran Alexander , Rukonic Marko , Wang Wei
Published: August 2019

A major part of financial accounting involves tracking and organizing business transactions over and over each month and hence automation of this task is of significant value to the users of accounting software. In this paper we present a large-scale recommendation system that successfully recommends company specific categories for several million small businesses in US, UK, Australia, Canada, India and France and handles billions of financial transactions each year. Our system uses machine learning to combine fragments of information from millions of users in a manner that allows us to accurately recommend user-specific Chart of Accounts categories. Accounts are handled even if named using abbreviations or in a foreign language. Transactions are handled even if a given user has never categorized a transaction like that before. The development of such a system and testing it at scale over billions of transactions is a first in the financial industry.
Transforming Underwriting in the Life Insurance Industry
Authors: Maier Marc , Carlotto Hayley , Sanchez Freddie , Balogun Sherriff , Merritt Sears
Published: August 2019

Life insurance provides trillions of dollars of financial security for hundreds of millions of individuals and families worldwide. Life insurance companies must accurately assess individual-level mortality risk to simultaneously maintain financial strength and price their products competitively. The traditional underwriting process used to assess this risk is based on manually examining an applicant’s health, behavioral, and financial profile. The existence of large historical data sets provides an unprecedented opportunity for artificial intelligence and machine learning to transform underwriting in the life insurance industry. We present an overview of how a rich application data set and survival modeling were combined to develop a life score that has been deployed in an algorithmic underwriting system at MassMutual, an American mutual life insurance company serving millions of clients. Through a novel evaluation framework, we show that the life score outperforms traditional underwriting by 6% on the basis of claims. We describe how engagement with actuaries, medical doctors, underwriters, and reinsurers was paramount to building an algorithmic underwriting system with a predictive model at its core. Finally, we provide details of the deployed system and highlight its value, which includes saving millions of dollars in operational efficiency while driving the decisions behind tens of billions of dollars of benefits.
Grading Uncompilable Programs
Authors: Takhar Rohit , Aggarwal Varun
Published: August 2019

Evaluators wish to test candidates on their ability to propose the correct algorithmic approach to solve programming problems. Recently, several automated systems for grading programs have been proposed, but none of them address uncompilable codes. We present the first approach to grade uncompilable codes and provide semantic feedback on them using machine learning. We propose two methods that allow us to derive informative semantic features from programs. One of this approach makes the program compilable by correcting errors, while the other relaxes syntax/grammar rules to help parse uncompilable codes. We compare the relative efficacy of these approaches towards grading. We finally combine them to build an algorithm which rivals the accuracy of experts in grading programs. Additionally, we show that the models learned for compilable codes can be reused for uncompilable codes. We present case studies, where companies are able to hire more efficiently by deploying our technology.
Remote Management of Boundary Protection Devices with Information Restrictions
Authors: Adler Aaron , Samouelian Peter , Atighetchi Michael , Fu Yat
Published: August 2019

Boundary Protection Devices (BPDs) are used by US Government mission partners to regulate the flow of information across networks of differing security levels. BPDs provide several critical functions, including preventing unauthorized sharing, sanitizing information, and preventing cyber attacks. Their application in national security and critical infrastructure environments (e.g., military missions, nuclear power plants, clean water distribution systems) calls for a comprehensive load monitoring system that provides resilience and scalability, as well as an automated and vendor neutral configuration management system that can efficiently respond to security threats at machine speed. Their design as one-way traffic control systems, however, presents challenges for dynamic load adaptation techniques that require access to application server performance metrics across network boundaries. Moreover, the structured review and approval process that regulates their configuration and use presents two significant challenges: (1) Adaptation techniques that alter the configuration of BPDs must be predictable, understandable, and pre-approved by administrators, and (2) Software can be installed on BPDs only after completing a stringent accreditation process. These challenges often lead to manual configuration management practices, which are inefficient or ineffective in many cases. The Hammerhead prototype, developed as part of the SHARC project, addresses these challenges using knowledge representation, a rule-oriented adaptation bundle format, and an extensible, open-source constraint solver.
Linking Educational Resources on Data Science
Authors: Ambite José Luis , Gordon Jonathan , Fierro Lily , Burns Gully , Mathew Joel
Published: August 2019

The availability of massive datasets in genetics, neuroimaging, mobile health, and other subfields of biology and medicine promises new insights but also poses significant challenges. To realize the potential of big data in biomedicine, the National Institutes of Health launched the Big Data to Knowledge (BD2K) initiative, funding several centers of excellence in biomedical data analysis and a Training Coordinating Center (TCC) tasked with facilitating online and inperson training of biomedical researchers in data science. A major initiative of the BD2K TCC is to automatically identify, describe, and organize data science training resources available on the Web and provide personalized training paths for users. In this paper, we describe the construction of ERuDIte, the Educational Resource Discovery Index for Data Science, and its release as linked data. ERuDIte contains over 11,000 training resources including courses, video tutorials, conference talks, and other materials. The metadata for these resources is described uniformly using Schema.org. We use machine learning techniques to tag each resource with concepts from the Data Science Education Ontology, which we developed to further describe resource content. Finally, we map references to people and organizations in learning resources to entities in DBpedia, DBLP, and ORCID, embedding our collection in the web of linked data. We hope that ERuDIte will provide a framework to foster open linked educational resources on the Web.
Expert Guided Rule Based Prioritization of Scientifically Relevant Images for Downlinking over Limited Bandwidth from Planetary Orbiters
Authors: Chakraborty Srija , Das Subhasish , Banerjee Ayan , Gupta Sandeep K. S. , Christensen Philip R.
Published: August 2019

Instruments onboard spacecraft acquire large amounts of data which is to be transmitted over a very low bandwidth. Consequently for some missions, the volume of data collected greatly exceeds the volume that can be downlinked before the next orbit. This necessitates the introduction of an intelligent autonomous decision making module that maximizes the return of the most scientifically relevant dataset over the low bandwidth for experts to analyze further. We propose an iterative rule based approach, guided by expert knowledge, to represent scientifically interesting geological landforms with respect to expert selected attributes. The rules are utilized to assign a priority based on how novel a test instance is with respect to its rule. High priority instances from the test set are used to iteratively update the learned rules. We then determine the effectiveness of the proposed approach on images acquired by a Mars orbiter and observe an expert-acceptable prioritization order generated by the rules that can potentially increase the return of scientifically relevant observations.
Satellite Detection of Moving Vessels in Marine Environments
Authors: Fridman Natalie , Amir Doron , Douchan Yinon , Agmon Noa
Published: August 2019

There is a growing need for coverage of large maritime areas, mainly in the exclusive economic zone (EEZ). Due to the difficulty of accessing such large areas, the use of satellite based sensors is the most efficient and cost-effective way to perform this task. Vessel behavior prediction is a necessary ability for detection of moving vessels with satellite imagery. In this paper we present an algorithm for selection of the best satellite observation window to detect a moving object. First, we describe a model for vessel behavior prediction and compare its performance to two base models. We use real marine traffic data (AIS) to compare their ability to predict vessel behavior in a time frame of between 1–24 hours. Then, we present a KINGFISHER, maritime intelligence system which uses our algorithm to track suspected vessels with satellite sensor. We also present the results of the algorithm in operational scenarios of the KINGFISHER.
Logistic Regression on Homomorphic Encrypted Data at Scale
Authors: Han Kyoohyung , Hong Seungwan , Cheon Jung Hee , Park Daejun
Published: August 2019

Machine learning on (homomorphic) encrypted data is a cryptographic method for analyzing private and/or sensitive data while keeping privacy. In the training phase, it takes as input an encrypted training data and outputs an encrypted model without ever decrypting. In the prediction phase, it uses the encrypted model to predict results on new encrypted data. In each phase, no decryption key is needed, and thus the data privacy is ultimately guaranteed. It has many applications in various areas such as finance, education, genomics, and medical field that have sensitive private data. While several studies have been reported on the prediction phase, few studies have been conducted on the training phase.In this paper, we present an efficient algorithm for logistic regression on homomorphic encrypted data, and evaluate our algorithm on real financial data consisting of 422,108 samples over 200 features. Our experiment shows that an encrypted model with a sufficient Kolmogorov Smirnow statistic value can be obtained in ∼17 hours in a single machine. We also evaluate our algorithm on the public MNIST dataset, and it takes ∼2 hours to learn an encrypted model with 96.4% accuracy. Considering the inefficiency of homomorphic encryption, our result is encouraging and demonstrates the practical feasibility of the logistic regression training on large encrypted data, for the first time to the best of our knowledge.
Leveraging Textual Specifications for Grammar-Based Fuzzing of Network Protocols
Authors: Jero Samuel , Pacheco Maria Leonor , Goldwasser Dan , Nita-Rotaru Cristina
Published: August 2019

Grammar-based fuzzing is a technique used to find software vulnerabilities by injecting well-formed inputs generated following rules that encode application semantics. Most grammar-based fuzzers for network protocols rely on human experts to manually specify these rules. In this work we study automated learning of protocol rules from textual specifications (i.e. RFCs). We evaluate the automatically extracted protocol rules by applying them to a state-of-the-art fuzzer for transport protocols and show that it leads to a smaller number of test cases while finding the same attacks as the system that uses manually specified rules.
VPDS: An AI-Based Automated Vehicle Occupancy and Violation Detection System
Authors: Kumar Abhinav , Gupta Aishwarya , Santra Bishal , Lalitha KS , Kolla Manasa , Gupta Mayank , Singh Rishabh
Published: August 2019

High Occupancy Vehicle/High Occupancy Tolling (HOV/HOT) lanes are operated based on voluntary HOV declarations by drivers. A majority of these declarations are wrong to leverage faster HOV lane speeds illegally. It is a herculean task to manually regulate HOV lanes and identify these violators. Therefore, an automated way of counting the number of people in a car is prudent for fair tolling and for violator detection.In this paper, we propose a Vehicle Passenger Detection System (VPDS) which works by capturing images through Near Infrared (NIR) cameras on the toll lanes and processing them using deep Convolutional Neural Networks (CNN) models. Our system has been deployed in 3 cities over a span of two years and has served roughly 30 million vehicles with an accuracy of 97% which is a remarkable improvement over manual review which is 37% accurate. Our system can generate an accurate report of HOV lane usage which helps policy makers pave the way towards de-congestion.
Profiles, Proxies, and Assumptions: Decentralized, Communications-Resilient Planning, Allocation, and Scheduling
Authors: Kuter Ugur , Kettler Brian , Guo Katherine , Hofmann Martin , Champagne Valerie , Lachevet Kurt , Lautenschlager Jennifer , Goldman Robert P. , Asencios Luis , Hamell Josh
Published: August 2019

Degraded communications are expected in large-scale disaster response and military operations, which nevertheless require rapid, concerted actions by distributed decision makers, each with limited visibility into the changing situation and in charge of a limited set of resources. We describe LAPLATA, a novel architecture that addresses these challenges by separating mission planning from allocation/scheduling for scalability but at the cost of some negotiation. We describe formal algorithms that achieve near-optimal performance according to mission completion percentage and subject matter expert review: assumption-based planning and replanning, profileassisted cooperative allocation, and schedule negotiation. We validate our approach on a realistic problem specification and compare results against subject matter expert solutions.
Bootstrapping Conversational Agents with Weak Supervision
Authors: Mallinar Neil , Shah Abhishek , Ugrani Rajendra , Gupta Ayush , Gurusankar Manikandan , Ho Tin Kam , Liao Q. Vera , Zhang Yunfeng , Bellamy Rachel K.E. , Yates Robert , Desmarais Chris , McGregor Blake
Published: August 2019

Many conversational agents in the market today follow a standard bot development framework which requires training intent classifiers to recognize user input. The need to create a proper set of training examples is often the bottleneck in the development process. In many occasions agent developers have access to historical chat logs that can provide a good quantity as well as coverage of training examples. However, the cost of labeling them with tens to hundreds of intents often prohibits taking full advantage of these chat logs. In this paper, we present a framework called search, label, and propagate (SLP) for bootstrapping intents from existing chat logs using weak supervision. The framework reduces hours to days of labeling effort down to minutes of work by using a search engine to find examples, then relies on a data programming approach to automatically expand the labels. We report on a user study that shows positive user feedback for this new approach to build conversational agents, and demonstrates the effectiveness of using data programming for autolabeling. While the system is developed for training conversational agents, the framework has broader application in significantly reducing labeling effort for training text classifiers.
Amsterdam to Dublin Eventually Delayed? LSTM and Transfer Learning for Predicting Delays of Low Cost Airlines
Authors: McCarthy Nicholas , Karzand Mohammad , Lecue Freddy
Published: August 2019

Flight delays impact airlines, airports and passengers. Delay prediction is crucial during the decision-making process for all players in commercial aviation, and in particular for airlines to meet their on-time performance objectives. Although many machine learning approaches have been experimented with, they fail in (i) predicting delays in minutes with low errors (less than 15 minutes), (ii) being applied to small carriers i.e., low cost companies characterized by a small amount of data. This work presents a Long Short-Term Memory (LSTM) approach to predicting flight delay, modeled as a sequence of flights across multiple airports for a particular aircraft throughout the day. We then suggest a transfer learning approach between heterogeneous feature spaces to train a prediction model for a given smaller airline using the data from another larger airline. Our approach is demonstrated to be robust and accurate for low cost airlines in Europe.
Identifying Semantics in Clinical Reports Using Neural Machine Translation
Authors: Mujjiga Srikanth , Krishna Vamsi , Chakravarthi Kalyan , J Vijayananda
Published: August 2019

Clinical documents are vital resources for radiologists when they have to consult or refer while studying similar cases. In large healthcare facilities where millions of reports are generated, searching for relevant documents is quite challenging. With abundant interchangeable words in clinical domain, understanding the semantics of the words in the clinical documents is vital to improve the search results. This paper details an end to end semantic search application to address the large scale information retrieval problem of clinical reports. The paper specifically focuses on the challenge of identifying semantics in the clinical reports to facilitate search at semantic level. The semantic search works by mapping the documents into the concept space and the search is performed in the concept space. A unique approach of framing the concept mapping problem as a language translation problem is proposed in this paper. The concept mapper is modelled using the Neural machine translation model (NMT) based on encoder-decoder with attention architecture. The regular expression based concept mapper takes approximately 3 seconds to extract UMLS concepts from a single document, where as the trained NMT does the same in approximately 30 milliseconds. NMT based model further enables incorporation of negation detection to identify whether a concept is negated or not, facilitating search for negated queries.
Artificial Counselor System for Stock Investment
Authors: NekoeiQachkanloo Hadi , Ghojogh Benyamin , Pasand Ali Saheb , Crowley Mark
Published: August 2019

This paper proposes a novel trading system which plays the role of an artificial counselor for stock investment. In this paper, the stock future prices (technical features) are predicted using Support Vector Regression. Thereafter, the predicted prices are used to recommend which portions of the budget an investor should invest in different existing stocks to have an optimum expected profit considering their level of risk tolerance. Two different methods are used for suggesting best portions, which are Markowitz portfolio theory and fuzzy investment counselor. The first approach is an optimization-based method which considers merely technical features, while the second approach is based on Fuzzy Logic taking into account both technical and fundamental features of the stock market. The experimental results on New York Stock Exchange (NYSE) show the effectiveness of the proposed system.
Reporter titrations for transfection of Micromonas commoda CCMP2709 v1
Authors: Sudek Lisa , Worden Alexandra , Ares Manny
Published: August 2019

This protocol describes the method used to test various amounts (0, 0.3, 3, 10, 20 and 30 µg) of reporter (H3proMpo-LUC) DNA. The plasmid H3proMpo-LUC carries a codon optimized Nanoluc coding region, whose expression is driven by the histone H3 promoter and 3' mRNA end formation and termination sequences. Lumen per total number of cells (including transfected and non-transfected cells within a culture) is used as a proxy for relative transfection efficiencies. It is determined in each culture based on luminescence measurements performed 72 hrs after transfection using the Nano-Glo® Luciferase Assay System.
Ficoll density gradient centrifugation v1
Authors: Chicaybam Leonardo , Bonamino Martin
Published: August 2019

Protocol for isolation of mononuclear cells using Ficoll density gradient centrifugation.
DASH Protocol v4
Authors: Lyden Amy , Crawford Emily
Published: August 2019

This protocol is for performing Depletion of Abundant Sequences by Hybridization (DASH) after preparing sequencing libraries and pooling together. DASH is most useful for RNA-seq of human metagenomic samples, where abundant species such as human mitochondrial ribosomal RNAs (rRNAs) occupy a majority of the sequencing space available, leaving a minor fraction for regions of interest. DASH treatment is performed after ligation of adapters and unique barcoding of the RNA-seq library. It employs CRISPR-Cas9 complexed to a set of guide RNAs (gRNAs) targeted to the abundant regions to be depleted in a given library. These abundant regions in the library are then cleaved, leaving only the fragments with intact adapters on both ends to be further amplified and sequenced.
“You Have to Control It However You Can”: Type 2 Diabetes Management in a Hispanic Farmworker Community in Rural Florida
Authors: Tyson Dinorah Martinez , Arriola Nora B. , Medina-Ramirez Patricia , Đào Lillie U. , Smith Chrystal A. S. , Livingston Taylor
Published: August 2019

Latino farmworkers carry a disproportionate burden of type 2 diabetes and face numerous challenges to accessing health care. This research explores beliefs about disease causality, self-management practices, and barriers to diabetes care among diabetics in a Mexican farmworking community in Florida. Thirty semi-structured interviews were conducted utilizing free list elicitation, open-ended questions, and closed-ended demographic questions. Participants' most recent hemoglobin A1C blood test results and Body Mass Index were obtained. Most participants were knowledgeable about recommended behavioral modifications and attempted to incorporate them into their daily routine, yet the majority had poor A1C levels and were overweight. Participants held dual cultural belief models about diabetes and used various complementary and alternative medicines to account for the political and economic barriers to diabetes management. Barriers to self-management include high cost of glucometer test strips and difficulty adhering to a diabetic diet. Addressing barriers to care, such as limited income and high cost of diabetic supplies, is integral to improving diabetes self-management and health outcomes. Anthropologically informed research, such as this study, is particularly well-positioned to make visible how social and healthcare policies are embodied.
Assisting the Improvement of a Military Safety System: An Application of Rapid Assessment Procedures to the Automatic Ground Collision Avoidance System
Authors: Sadler Garrett , Ho Nhut , Hoffmann Lauren , Zemlicka Kevin , Lyons Joseph , Wilkins Mark
Published: August 2019

This article describes an iterative application of Rapid Assessment Procedures (RAP) to study human-machine interaction issues with a recently implemented, highly automatic system on the F-16 fighter aircraft known as the Automatic Ground Collision Avoidance System (Auto-GCAS). Auto-GCAS is a sophisticated technology that has the ability to detect an imminent ground collision threat and automatically execute a last-moment maneuver to avoid a crash. We employed RAP methods at multiple United States Air Force and Air National Guard sites in the United States and abroad. Over a three-year period, we conducted semi-structured interviews with 402 F-16 pilots experienced with the system. The method we employed, termed here iterative RAP, is reviewed in detail and evaluated in the framework of Utarini, Winkvist, and Pelto's (2001) eleven criteria for quality RAP studies. Results from this study include assisting in correcting system misunderstandings and anomalies, improving information flow about Auto-GCAS, and contributing towards the perception of military safety systems as valuable. This article concludes by (1) discussing positive effects that iterative RAP can contribute to the defense community and (2) arguing our method's utility towards the study of complex bureaucracies and multi-sited research, particularly following the introduction of new technology or policy.
Treatment of patients with chronic pain in Russia. Time of change
Authors: Genov Pavel G. , Vostretsova Yu. V.
Published: August 2019

According to WHO, about 20% of population suffers from chronic pain. First pain clinics whose purpose was to provide care to such kind of patients started to appear in 1961. There are reportedly about 15 multidisciplinary pain clinics in Russia nowadays.The development of this medical branch in Russia is restrained by several factors: the absence of legal basis for pain specialists, the lack of educational programs, clinical standards and recommendations, as well as absence of state medical insurance rates for chronic pain care.
The organizations of specialized medical care for patients with headache
Authors: Baiushkina Liudmila I.
Published: August 2019

Primary headaches are common diseases in the clinical practice. Affecting the active, able-bodied population, impairing the quality of life and causing social maladjustment, they are of immense social significance. The medical care for patients with headaches ought to be complex and include a patient’s educational program, rational pharmaceutical therapy, detection and correction of comorbid functional disorders, nonpharmaceutical therapy. Despite an adequate therapy, the treatment satisfaction remains low and stimulates development of individualized treatment algorithms, that take into account the patient’s personal characteristics. They should be applicable both for the self treatment and for the use in the neurological and general practice.
A complex treatment of irreducible displacement of the temporomandibular joint disc and myofascial pain disorders with the use of botulinum toxin
Authors: Ilyin Aleksander A. , Fazylova T. A. , Demchinsky E. A. , Olesova V. N. , Olesov E. E.
Published: August 2019

Background. An irreducible displacement of the articular disc (IDD) is one of the TMJ conditions characterized by complexity of clinical manifestations, severity and is always accompanied by myofascial pain disorders.
Objective. To improve the results of treatment of patients with TMJ IDD and myofascial pain syndromes.
Methods. We performed a clinical examination and treatment of 38 patients with irreducible displacement of the TMJ articular disc and myofascial pain disorders. As additional research methods, we used CBCT, MRI, EMG. A step-by-step treatment was conducted, including a drug therapy, injection of botulinum toxin type A (BTA) in the masticatory, temporal, lateral pterygoid muscles (LPM) bilaterally; repositional splint therapy; arthrocentesis with the lavage of both articular spaces.
Results. The overall effectiveness of the treatment was 97.4%. Out of 38 patients, 34 (89.5%) were treated without arthrocentesis with the complete restoration of the mandibular movement volume and elimination of pain syndrome.
Conclusions. An effective system has been developed for a sequential complex treatment of TMJ IDD; a new technique has been developed for a periarticular injection access to the LPM allowing administration of a drug to its upper and lower heads. The high efficiency of BTA injections in the treatment of TMJ IDD is due to the mandatory drug administration into 3 muscles: masticatory, temporal and lateral pterygoid. In the treatment of TMJ IDD, arthrocentesis is effective with lavage of the lower and upper articular spaces.
Surgical methods of treatment of Eagle syndrome
Authors: Nazaryan D. N. , Karayan A. S. , Fedosov A. V.
Published: August 2019

Background. One of the most difficult in the clinical practice patients groups are patients with orofacial pain. A possible cause of pain in the face and mouth can be stylohyoid syndrome or Eagle syndrome. The clinical manifestations of the syndrome are observed in only 4-10.3% of cases among the total number of cases of elongated hyoid processes.
Objective. To develop criteria for the choice of the surgical access for a safe removal of the hyoid process depending on its anatomy.
Methods. In patients with the symptoms of stylohyoid syndrome, in the case of detecting an extended hyoid process during the clinical study, its removal was performed via an intraoral route (n=3). In the other patients (n=10) the removal was performed via an external access along the first cervical fold, or through the vertical retroaural access.
Results. In patients with stylohyoid syndrome, we observed a high efficiency of the surgical treatment and rather short times of postsurgical rehabilitation, that significantly improved the quality of life.
Conclusion. In our opinion, the surgical access through the first cervical fold is the most convenient way both from the technical viewpoint and from the viewpoint of a minimal cosmetic defect.
Internal hernia as one of the causes of abdominal pain
Authors: Kitaev V. M. , Koshelev E. G. , Sokolova O. V. , Belyaev G. Yu. , Egorov A. A. , Kurzantseva O. O.
Published: August 2019

Abdominal pain is often observed in clinical practice, causing diagnostic difficulties for doctors of different specialties. Internal hernias are a surgical pathology and may be the cause for the development of acute small bowel obstruction and the occurrence of pain. A modern classification of internal hernias is presented and their topographic-anatomical description is given. The analysis of the results of computed tomography (CT) scan of the abdominal cavity in 20 patients with an unidentified cause of abdominal pain was performed. According to the CT data, three patients had internal hernias that caused the development of pain syndrome. A pathological displacement of intestinal loops with partial disruption of intestinal permeability and curvature of the mesentery without critical disturbance of blood supply was demonstrated in 4 patients with abdominal pain. The findings suggest that CT may be the method of choice when examining patients with an unknown cause of abdominal pain syndrome.
Long-term results of stenting of long coronary artery stenosis with consecutive implantation of stents of different types with overlapping edges in patients with acute coronary syndrome without ST-segment elevation and multivessel coronary artery disease
Authors: Bocharov Aleksandr V. , Popov Leonid V.
Published: August 2019

Introduction. In urgent interventional cardiology practice, combinations of drug-eluting stents and bare-metal stents are sometimes forced to treat extended stenosis in a clinic-dependent artery.
Objective. A comparison of long-term results of treatment of patients with coronary heart disease and multivessel coronary lesions, which performed stenting of the clinic-dependent artery by two successive partially overlapping stents using stents of the 3rd generation with drug coating or a combination of the 3rd generation stent with drug coating and a bare-metal stent for acute coronary syndrome without St segment elevation and later — complete functional myocardial revascularization by endovascular method.
Methods. The minimum overall length sentiremos section was made 55 mm. In main group included 32 patients for whom revascularization clinic-dependent artery performed endovascular intervention with the use of 2 stents 3-generation drug-coated sirolimus and biodegradable polymer implanted overlap. In 30 patients (control group), clinic-dependent artery revascularization was also performed by a combination of implanted overlap stents, one of which was a 3rd generation stent with sirolimus drug coating and biodegradable polymer, and the other was a bare-metal stent. There were no statistically significant differences between the groups in clinical, demographic and operational characteristics.
Results. The analysis of the results revealed a significant difference between the groups in the frequency of repeated revascularization of the target artery, which were observed more often in the control group.
Conclusion. When performing an extended stenting of the clinic-dependent artery in patients with acute coronary syndrome without ST segment elevation, overlapping of the drug-coated stent and the bare-metal stent should be avoided, since the antirestenotic effect of the drug-coated stent is leveled when overlapping with the bare-metal stent, but this strategy can be used in case of full coverage of the stenotic lesion or closure of the dissection.
Differential diagnosis short-lasting unilateral neuralgiform headache attacks and trigeminal neuralgia
Authors: Parkhomenko E. V. , Nartov S. E.
Published: August 2019

The article presents diagnostic criteria, comparative characteristics of short-lasting unilateral neuralgiform headache attacks, including short-term unilateral neuralgic headaches with conjunctival injection and lacrimation (SUNCT) and short-term unilateral neuralgic headaches with cranial autonomic symptoms (SUNA), differential diagnosis with trigeminal neuralgia.
Pain in amyotrophic lateral sclerosis
Authors: Voitenkov Vladislav B. , Ekusheva E. V.
Published: August 2019

In this review, we discuss different aspects of pain syndrome in patients with amyotrophic lateral sclerosis: etiology, incidence, pathophysiology and main clinical features. Also we review the modern approaches to the treatment of pain in amyotrophic lateral sclerosis. Pain is actually not rare in this condition: it appears in 80% of patients, affecting their quality of life and functional activity, leading to the development of depressive and anxiety disorders. Pain in amyotrophic lateral sclerosis is often overlooked by clinicians, since their attention may focus on the motor symptoms of the disease. Thus, a more careful approach is needed to diagnose and treat pain in amyotrophic lateral sclerosis.
Pain management and stroke prevention in HHV-3-associated infection
Authors: Kolokolov O. V. , Bakulev A. L. , Shuldyakov A. A. , Makarov N. S. , Sitkali I. V. , Kolokolova A. M. , Kravchenya S. S.
Published: August 2019

The modern information on the epidemiology, pathogenesis, clinical manifestations, diagnosis and pain management in HHV-3-associated neuro-infection is presented. The criteria for the phased diagnostics and approaches to the rational therapy for shingles and postherpetic neuralgia are presented. The conditions aimed at the prevention of pain due to the damage to peripheral nervous system and stroke due to HHV-3-associated vasculopathy are discussed.