Miracle-dz/UniFAR-FTUs huggingface dataset:
DOWNSTREAM MIGRATION OF THE EUROPEAN EEL ( ANGUILLA ANGUILLA ) IN THE ELBE RIVER , GERMANY : MOVEMENT PATTERNS AND THE POTENTIAL IMPACT OF ENVIRONMENTAL FACTORS

Recruitment of European eels (Anguilla anguilla) has declined to the extent that they have been added to the IUCN Red List of Threatened Species. Therefore, it is critical to ensure that eels complete their outward river migration in order to contribute to the available spawning stock. We conducted a 4-year (2007–2011) telemetry study to understand the migratory behaviour and potential impact of environmental factors on the eel during this critical life stage. Out of 399 female eels tagged with acoustic transmitters, only 28% demonstrated clear downstream migratory behaviour. Fifty-five percent were detected exhibiting no downstream migration behaviour and 17% were not detected at any monitoring station. Movement patterns of downstream-migrating (silver) eels were characterized by nocturnal activity and seasonal migration, with distinct peaks in autumn and spring. Migration was often discontinuous and exhibited phases of active locomotion and expanded stopovers. The most important determinants of movement activity were water temperature, cumulative precipitation and moonlight, although the significance varied by season and location in the river basin. Our results evidence a discontinuous, stepwise migration over an extended period. Furthermore, our findings indicate that migration success depends on holding duration prior to tagging and environmental predictors with varying importance depending on the season, as well as the locations of capture, tagging and release. Copyright © 2015 John Wiley & Sons, Ltd. key words: acoustic telemetry; fish migration; environmental trigger; generalized linear model; handling effect; discontinuous migration; Anguilla anguilla; seasonal migration Received 20 June 2014; Revised 9 December 2014; Accepted 21 January 2015
Dactylella coccinella sp . nov . , an anamorphic species

Dactylella coccinella, isolated from the teleomorphic fungus Orbilia coccinella, is described as a new anamorphic species. Diagnostic characters that distinguish this anamorphic fungus from other Dactylella species are a simple conidiophore that bears a single, cylindrical, 1-7-septate conidium with a round distal end, a monoblastic conidiogenous cell, cylindrical and absence of nematode trapping capability. Key words—anamorph species, ITS sequence
Cost-Sensitive Approach for Batch Size Optimization

In this paper we propose a novel approach to automatically determine the batch size in stochastic gradient descent methods. The choice of the batch size induces a trade-off between the accuracy of the gradient estimate and the cost in terms of samples of each update. We propose to determine the batch size by optimizing the ratio between a lower bound to the first-order Taylor approximation of the expected improvement and the number of samples used to estimate the gradient. The performance of the proposed approach is empirically compared with related methods on an image classification problem using the popular MINST dataset.
Supervised and Unsupervised Cell-Nuclei Detection in Immunohistology

We introduce a simulation model for cell-nuclei in immunohistology that is enhanced by a cycle-consistent adversarial network to construct realistic virtual ground-truth data. This model is applied to the task of cell-nuclei detection in immunohistologically stained whole-slide images (WSI) and achieves a standalone median performance of 83.1% F1-score learning purely from synthetic annotations. We thoroughly evaluate different training scenarios with varying contributions of manual labels. It is shown that through the simulation model, the amount of required annotations can significantly be reduced without major performance losses. If only limited amounts of annotations are available, the simulation can lead to a stabilization in the detection of immune-cells.
Electrochemically Mediated Atom Transfer Radical Polymerization

Atom transfer radical polymerization (ATRP) is an important living radical polymerization method. It is widely used industrially to polymerize acrylates, styrenes, pyridines, methacrylates, and acrylonitriles [1]. ATRP can produce coatings, surfactants, foamers, and many other products in a variety of markets [2]. The “living” descriptor of ATRP indicates that the growing polymer chains are all growing at the same rate, leading to very narrow molecular weight dispersity. ATRP achieves this by reversibly terminating growing chains through transferring a halogen atom between the polymer chain and a catalytic metal complex. To activate polymerization, an activator molecule (MtL) removes the halogen, creating a deactivator molecule (MtL) and a radical on the polymer chain. After a short period of polymerization, the deactivator transfers the halogen back to the polymer chain and regenerates the activator molecule. ATRP controls polymer chain growth and limits radical termination through having a higher deactivation rate than activation rate. This leads to short spurts of growth with longer periods of deactivation [1–3]. Figure 1 show the overall mechanism for ATRP and the equation for polymerization rate.
HotStuff: BFT Consensus with Linearity and Responsiveness

We present HotStuff, a leader-based Byzantine fault-tolerant replication protocol for the partially synchronous model. Once network communication becomes synchronous, HotStuff enables a correct leader to drive the protocol to consensus at the pace of actual (vs. maximum) network delay--a property called responsiveness---and with communication complexity that is linear in the number of replicas. To our knowledge, HotStuff is the first partially synchronous BFT replication protocol exhibiting these combined properties. Its simplicity enables it to be further pipelined and simplified into a practical, concise protocol for building large-scale replication services.
Research on Model of Real Concept Lattice for Grid Resources and Algorithms

It is a pivotal task how to organize and manage resources for Grid research and applications,for which various theories and prototype systems have been developed.This paper first proposes Grid resource management strategies by employing real concept lattice and real concept Trie and combines FCA theory with Grid resource management technology.The formal concept definitions of Grid and resource are given and the Grid resource real concept lattice model is proposed.The extent Trie and intent Trie of real concept for Grid resource are constructed and the algorithms are developed for Trie creation and retrieval to resource and service by FCA method.Experiments and application examples show that our method of resource organization is feasible and effective.
PHI FROM FREE TEXT IN MEDICAL RECORDS

Medical health records often contain clinical investigations results and critical information regarding patient health conditions. In these medical records, along with patient health information, patient Protected Health Information (PHI) such as names, locations and date information can co-exist. As per Health Insurance Portability and Accountability Act (HIPAA), before sharing the medical records with researchers and others, all types of PHI information needs to be de-identified. Manual de-identification through human annotators is laborious and error prone, hence, a reliable automated de-identification system is need of the hour. In this work, various state of the art techniques for de-identification of patient notes in electronic health records were analyzed for their performance, based on the performance quoted in the literature, NeuroNER was selected to de-identify Indian Radiology reports. NeuroNER is a named-entity recognition text de-identification tool developed by Massachusetts Institute of Technology (MIT). This tool is based on the Artificial Neural Networks written in Python and uses Tensorflow machine-learning framework and it comes with five pre-trained models. To test the NeuroNER models on Indian context data such as name of the person and place, 3300 medical records were simulated. Medical records were simulated by extracting clinical findings, remarks from MIMIC-III data set. For collection of all the relevant Indian data, various websites were scraped to include Indian names, Indian locations (all towns and cities), and Indian Hospital and unit names. During the testing of NeuroNER system, we observed that some of the Indian data such as name, location, etc. were not de-identified satisfactorily. To improve the performance of NeuroNER on Indian context data, along with the existing NeuroNER pre-trained model, a new pre-trained model was added to handle Indian medical reports. Medical dictionary lookup was used to reduce number of misclassifications. Results from all four pre-trained models and the model trained on Indian simulated data were concatenated and final PHI token list was generated to anonymize the medical records to obtain de-identified records. Using this approach, we improved the applicability of the NeuroNER system to Indian data and improved its efficiency and reliability. 2000 simulated reports were used for transfer learning as training set, 1000 reports were used for test set and 300 reports were used for validation (unseen) set.
Edge detection based on joint iteration ghost imaging

Imaging and edge detection have been widely applied and played an important role in security checking and medical diagnosis. However, as we know, most edge detection based on ghost imaging system require a large measurement times and the target object image cannot be provided directly. In this work, a new edge detection based on joint iteration of projected Landweber iteration regularization and guided filter ghost imaging method have been proposed which can be improved the feature detection quality in ghost imaging. This method can also achieve high quality imaging. Simulation and experiment results show that the spatial information and edge information of target object are successfully recovered from the random speckle patterns without special coding under a low measurement times, and the edge image quality is improved remarkably. This approach improves the the applicability of ghost imaging, and can satisfy the practical application fields of imaging and edge detection at the same time. © 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Achieving Greater SPEED with Iterations between Object and K-space

Purpose: MRI can be accelerated even with a single coil by using signal sparsity. Examples include Compressed Sensing [1] and Skipped Phase Encoding and Edge Deghosting (SPEED) [2]. The original SPEED acquired 3 skipped datasets along PE with interleaved offsets, and resolved aliasing ghosts by solving linear equations for individual pixels [2]. In this study, a significant enhancement of SPEED is achieved, with a greater acceleration power and a more straightforward processing that takes advantage of intrinsic data properties between object and K-space.
Modeling Delay Discounting using Gaussian Process with Active Learning

We explore a nonparametric approach to cognitive modeling. Traditionally, models in cognitive science have been parametric. As such, the model relies on the assumption that the data distribution can be defined by a finite set of parameters. However, there is no guarantee that such an assumption will hold, and it may introduce undesirable biases. For these reasons, a nonparametric approach to model building is appealing. We propose a novel framework that combines Gaussian Processes with active learning (GPAL), and evaluate it in the context of delay discounting (DD), a well-studied task in decision making. We evaluate GPAL in a simulation and a behavioral experiment, and compare it against a traditional parametric model. The results show that GPAL is a suitable modeling framework that is robust, reliable, and efficient, exhibiting high sensitivity to individual differences.
Fault Detection and Classification of Time Series Using Localized Matrix Profiles

We introduce a new primitive, called the Localized Matrix Profile (LMP), for time series data mining. We devise fast algorithms for LMP computation, and propose a fault detector and a fault classifier based on the LMP. A case study using synthetic sensor data generated from a physical model of an electrical motor is provided to demonstrate the effectiveness and efficiency of our approach.
ADC: Concept Discovery and Learning of Actions for Unknown Environments

How to represent adequate knowledge for solving a problem is a challenging task in the design of artiﬁcial intelligent agents. Ideally we would like an agent to create its own representations. In this paper, we describe the ADC (Automatic Discovery of Concepts) algorithm for learning relational concepts and actions to represent states of the environment and solve tasks, using inductive logic programming and reinforcement learning. Our experiments show that an intrinsic motivation function and automatically learning new representations can reduce the learning time and produce useful concepts for solving a task.
Potential on the Soil Surface Generated by a Lightning Based on Transmission Line Modeling Method

This paper presents a practical application of a formulation for estimating the potential on the soil surface, originated by an electric current calculated in a grounding conductor through the Transmission Line Modeling Method (TLM). This study discusses the use of such formulation, which can increase the applicability of one-dimensional numerical methods that are not able to determine such potentials directly and it is focused on the evaluation of the transient generated by a lightning surge taking into account the frequency dependence of the soil properties.
Konva: Power and Network Aware Framework and Protocols for Multiplayer Mobile Games

Multiplayer mobile games are an increasingly important class of mobile application. While device features and application quality are rapidly growing, the battery technologies are not growing at the same pace. Battery lifetime is one of the key factors that hinder the usability of the mobile devices for resource-intensive applications. In this work, we design a framework for power management that adapts its behavior to the intent of the user and the game, the characteristics of the network and the network interface. Our system is being designed to reduce the overall device power usage without sacrificing the end-user game experience.
CUIS at the NTCIR-14 STC-3 DQ Subtask ?

We present our model participated in the NTCIR-14 Short Text Conversation-3 [13] Dialogue Quality (DQ) subtask. The DQ subtask is to assign quality scores to each customer-helpdesk dialogue in terms of three criteria: task accomplishment (A), customer satisfaction (S), and efficiency (E). Each dialogue is composed of posts by customer or helpdesk and each post consists of turns of sentences, which naturally forms a hierarchical structure. In this work, we consider this problem as a document classification task and have attempted various ways until we finalize our model into a hierarchical attention network with bidirectional GRUs and Google’s BERT sentence embedding, which stands for Bidirectional Encoder Representations from Transformers. Also, our model is augmented with a sender-aware encoding to differentiate the contributions from the customer side and the helpdesk side. According to the official STC evaluation in the test dataset, our proposed system achieves among the top performance teams in the English dataset.
M-HIN: Complex Embeddings for Heterogeneous Information Networks via Metagraphs

To represent a complex network, paths are often employed for capturing relationships among node: random walks for (homogeneous) networks and metapaths for heterogeneous information networks (HINs). However, there is structural (and possibly semantic) information loss when using paths to represent the subgraph between two nodes, since a path is a linear structure and a subgraph often is not. Can we find a better alternative for network embeddings? We offer a novel mechanism to capture the features of HIN nodes via metagraphs, which retains more structural and semantic information than path-oriented models. Inspired by developments in knowledge graph embedding, we propose to construct HIN triplets using nodes and metagraphs between them. Metagraphs are generated by harnessing the GRAMI algorithm, which enumerates frequent subgraph patterns in a HIN. Subsequently, the Hadamard function is applied to encode relationships between nodes and metagraphs, and the probability whether a HIN triplet can be evaluated. Further, to better distinguish between symmetric and asymmetric cases of metagraphs, we introduce a complex embedding scheme that is able to precisely express fine-grained features of HIN nodes. We evaluate the proposed model, M-HIN, on real-life datasets and demonstrate that it significantly and consistently outperforms state-of-the-art models.
FUNCTIONAL GAIT PARAMETERS AS OBJECTIVE ASSESSMENT TOOLS IN PATIENTS WITH CHRONIC LOW BACK PAIN : A CASE STUDY

We report a 38 yrs old patient with a previously diagnosed Chronic Low Back Pain who became severely functionally impaired as evidenced by the Oswestry Disability Questionnaire (56%). The aim of this study was to introduce changes in functional gait parameters as an assessment tool before and after a single session of Kinesiotape application in a patient with chronic LBP. We introduced, for the first time, gait parameters including the 10-meter walk test and the 6-minute walk test as the functional gait parameters of interest. There was (1) a 25% & 36% increase in normal walking speed and fast walking speed, respectively as evidenced by the 10MWT with 72% decrease in pain and (2) a 86% increase in walking tolerance as evidenced by the 6MWT with 88% decrease in pain. Our data demonstrated for the first time the possible effective use of gait assessment as objective motor performance measures to assess the therapy-induced improvement following therapeutic intervention in patient with chronic low back pain. We hope that these data will act as a starting point for further research to test the potential gait assessment measures to provide a more in-depth objective assessment in response to rehabilitation therapies in chronic low back pain patients.
1 3 Multi ‐ Task Learning for Abstractive and Extractive Summarization

The abstractive method and extractive method are two main approaches for automatic document summarization. In this paper, to fully integrate the relatedness and advantages of both approaches, we propose a general unified framework for abstractive summarization which incorporates extractive summarization as an auxiliary task. In particular, our framework is composed of a shared hierarchical document encoder, a hierarchical attention mechanism-based decoder, and an extractor. We adopt multi-task learning method to train these two tasks jointly, which enables the shared encoder to better capture the semantics of the document. Moreover, as our main task is abstractive summarization, we constrain the attention learned in the abstractive task with the labels of the extractive task to strengthen the consistency between the two tasks. Experiments on the CNN/DailyMail dataset demonstrate that both the auxiliary task and the attention constraint contribute to improve the performance significantly, and our model is comparable to the state-of-the-art abstractive models. In addition, we cut half number of labels of the extractive task, pretrain the extractor, and jointly train the two tasks using the estimated sentence salience of the extractive task to constrain the attention of the abstractive task. The results do not decrease much compared with using full-labeled data of the auxiliary task.
Development of the Chemistry Teaching Material Using the Cooperative Learning Model with STAD Type Based on Multiple Representation to Improve the Students Learning Outcomes on the Molecular Geometry Topic

This research aims to obtain the feasible chemistry teaching material using the cooperative learning model with STAD type based on multiple representation to improve students learning outcomes on the molecular geometry topic. The teaching materials consist of lesson plans, student book, worksheet, and concept mastery test were developed using the four D models. The subjects of the research were twenty four students of science class at 10 th grade of SMA Trimurti Surabaya, Indonesia, at academic year of 2018/2019. The tryout of the teaching materials used one-group pretest-posttest design. The data were analyzed descriptively. The results showed : (1) the validity of the lesson plans, student book, worksheet, concept mastery test were very valid category (2) the readability of student book and worksheet were good category (3) learning performance was good category; (4) students activity refered to students centered learning; (5) majority of students gave the positive response to learning process; (6) students achieved mastery of learning of 83.33 % with high gain scores (0.78). Based on the results of research it could be concluded that the chemistry teaching materials developed were feasible to improve the students learning outcomes on molecular geometry topic.
of Group Composition Partner Interdependence Model to Study the Effects − Using the Actor

We extend the actor–partner interdependence model (APIM), a model originally proposed for the analysis of dyadic data, to the study of groups. We call this extended model the group actor–partner interdependence model or GAPIM. For individual outcomes (e.g., satisfaction with the group), we propose a group composition model with four effects; for group-level outcomes (e.g., group productivity), we propose a model with two effects; and for dyad-level outcomes (e.g., liking of each of the other members of the group), a model with seven effects. For instance, for an individual outcome with gender as the group composition variable the effects are gender of the actor, gender of the other group members, actor similarity in gender to the others in the group, and the others’ similarity in gender. For each of these models, we discuss the ways in which different submodels map onto social-psychological processes. We illustrate the GAPIM with two data sets.
A Hybrid Strategy for Auto-scaling of VMs: An Approach Based on Time Series and Thresholds

Demand for performance, availability, and reliability in computational systems has increased lately. Improving these aspects is an important research challenge due to the wide diversity of applications and customers. The elasticity of cloud computing applications plays an important role, and its implementation is based on auto-scaling techniques, which allow to increase and decrease cloud-based application’s capacity. Therefore, it is possible to deal with the workload variation and not interrupt the service. The auto-scaling process can be grouped into two classes: reactive and proactive. In this dissertation, we propose a hybrid auto-scaling approach that uses reactive and proactive solutions. Our proposal is implemented using triggering thresholds and five forecasting models: Drift, Simple Exponential Smoothing, Holt, Holt-Winters and ARIMA. It checks if the CPU utilization has achieved the threshold, if it has, the system is scaled (out/in). On the other hand, if the threshold was not achieved the forecasting phase starts. In this phase, the five forecasting models are trained and tested, so the most suitable one is used to forecast resource utilization. The main goal of this research is to achieve a better QoS related to the cloud computing environment. One of the obtained results shows that our method represents a throughput improvement of 12.11% by using our proposal, instead of only using a threshold-based technique.