Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Particle Shape on Bedload Transport

The effects of particle shape on bedload transport in gravel-bed streams are examined using a similarity approach and fractional transport analysis for data from Piceance Creek, Colorado, which contains flat, low density, shale particles. The reference transport critical shear stress for the median surface grain size, τ r50s , for the flat shale particles in Piceance Creek is approximately 2.5 times higher than those for more spherical particles. This indicates a lower susceptibility of disc-like particles to initial entrainment and lower transport rates for given flow conditions than more rounded particles.
Fundamental Factors Rate A foreign exchange market PredictionPerception Adaptation Trend Factors Strategy MakingPredictionPerception

In this study we used a new agent-based approach, an artificial market approach, to support decision-making of exchange rate policies. We first interviewed a dealer and found that the features of dealer interaction in learning were similar to features of genetic operations in biology. Next we constructed an artificial market model using a Genetic Algorithm by regarding the market as a multi-agent system. Finally, using the computer simulation of the model, several strategic scenarios of rate policies were compared. As a result, it was found that intervention and the control of interest rates were effective on the stabilization of yen-dollar rates in 1998.
DeepIS: deep learning framework for three-dimensional label-free tracking of immunological synapses

The immunological synapse (IS) is a cell-cell junction between T cells and professional antigen presenting cells. Since the IS formation is a critical step for the initiation of an antigen-specific immune response, various live-cell imaging techniques, most of which rely on fluorescence microscopy, have been used to study the dynamics of IS. However, the inherent limitations associated with the fluorescence-based imaging, such as photo-bleaching and photo-toxicity, prevent the long-term assessment of dynamic changes of IS with high frequency. Here, we propose and experimentally validate a label-free, volumetric, and automated assessment method for IS dynamics using a combinational approach of optical diffraction tomography and deep learning-based segmentation. The proposed method enables an automatic and quantitative spatiotemporal analysis of IS kinetics of morphological and biochemical parameters associated with IS dynamics, providing a new option for immunological research. CC-BY 4.0 International license available under a not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (which was this version posted April 27, 2019. ; https://doi.org/10.1101/539858 doi: bioRxiv preprint
A Hierarchical Statistical Framework for the Extraction of Semantically Related Words in Textual Documents

A Hierarchical Statistical Framework for the Extraction of Semantically Related Words in Textual Documents Weijia Su Nowadays there exist a lot of documents in electronic format on the Internet, such as daily news, blog articles, messages posted online, even books and magazines. The information that can be extracted from these documents is of particular importance to several agencies and companies (e.g. security agencies, insurance companies, advertising and marketing companies, etc.). In the case of security, for instance, recent studies have shown that cyber criminals generally exchange their experiences and knowledge via media such as forums and blogs. These exchanged data, if well extracted and modeled, can provide significant clues to agencies operating in the security field. However, managing and processing the huge quantity of multimodal (i.e. image, video, text, audio) information present on the Web is a challenging task. In this thesis, we focus on textual data for which many statistical language modeling frameworks have been developed to facilitate the management of digitized texts. Many of these approaches have achieved great performances on various applications. However, most of them have focused on modeling documents individually, while in real world most documents are related, organized and archived into categories according to their themes. The main goal of this thesis is to propose a hierarchical statistical model to analyze documents collections, characterized by a hierarchical structure, to find hidden information and detect potential threats according to them. The proposed model is part of a large cyber security forensics system that we are designing to discover and capture potential security threats by retrieving and analyzing data gathered from the Web. Our approach models each node in a given textual collection using advanced statistical techniques and allows capturing the semantic information hidden inside it. In particular, a log-bilinear model is adopted to describe words in vector space in such a way that their correlations can be discovered and derived, from their representations, at each level
LIG-CRIStAL Submission for the WMT 2017 Automatic Post-Editing Task

This paper presents the LIG-CRIStAL submission to the shared Automatic PostEditing task of WMT 2017. We propose two neural post-editing models: a monosource model with a task-specific attention mechanism, which performs particularly well in a low-resource scenario; and a chained architecture which makes use of the source sentence to provide extra context. This latter architecture manages to slightly improve our results when more training data is available. We present and discuss our results on two datasets (en-de and de-en) that are made available for the task.
Standard CMMI Appraisal Method for Process Improvement (SCAMPI), Version 1.1: Method Definition Document

The Standard CMMI Appraisal Method for Process Improvement (SCAMPI) is designed to provide benchmark quality ratings relative to Capability Maturity Model Integration (CMMI) models. It is applicable to a wide range of appraisal usage modes, including both internal process improvement and external capability determinations. SCAMPI satisfies all of the Appraisal Requirements for CMMI (ARC) requirements for a Class A appraisal method and supports the conduct of ISO/IEC 15504 assessments. The SCAMPI Method Definition Document describes the requirements, activities, and practices associated with each of the processes that compose the SCAMPI method. It is intended to be one of the elements of the infrastructure within which SCAMPI Lead Appraisers conduct a SCAMPI appraisal. Precise listings of required practices, parameters, and variation limits, as well as optional practices and guidance for enacting the method, are covered. An overview of the method's context, concepts, and architecture is also provided.
Data Assessment and Prioritization in Mobile Networks for Real-Time Prediction of Spatial Information with Machine Learning

A new framework of data assessment and prioritization for real-time prediction of spatial information is presented. In next generation mobile networks, the real-time prediction of spatial information will be a promising application. Recent developments of the machine learning technology have enabled prediction of spatial information, which would be quite useful for smart mobility services including navigation, driving assistance, and self-driving. Image sensors in mobile devices like smartphones and tablets and in vehicles such as cars and drones and real-time cognitive computing like automatic number/license plate recognition systems and object recognition systems are also key enablers for forming spatial information. However, since image data collected by mobile devices and vehicles need to be delivered to the server in real time to extract input data for real-time prediction, the uplink transmission speed of mobile networks is a major impediment. The framework of data assessment and prioritization proposed in this paper reduces the uplink traffic volume while maintaining the prediction accuracy of spatial information. In our framework, machine learning is used to estimate the importance of each data element and to predict spatial information under the limitation of available data. Numerical evaluation using actual vehicle mobility dataset demonstrated the validity of this approach.
Bayesian Networks, Rule Induction and Logistic Regression in the Prediction of Women Survival Suuering from Breast Cancer

In this paper we present an empirical comparison between several paradigms coming from Statistics and Artiicial Intelligence for solving a supervised classiication problem. The empirically compared paradigms are Bayesian Networks, Rule Induction and Logistic Regression. The problem to tackle is the prediction of women survival diagnosed with breast cancer taking into account four predictor variables gathered at the moment of the diagnosis. The data le includes 1000 diagnosed cases at the Oncological Institute of Gipuzkoa (Basque Country). The validation of the paradigms was carried out using the 10-fold cross-validation method.
Multiple Representations of Perceptual Features for Texture Classification

Texture Classification plays a vital role in medical image, remote sensing image, pattern analysis for the past three decades. Eventhough it is three decades problem, still having a lot of scope in pattern analysis. Textural features corresponding to visual properties of texture are highly desirable for two reasons; they will be optimum in terms of feature selection and will be applicable to all kinds of textures. Some of the perceptual features are coarseness, contrast, direction and busyness. The aim of this paper is to present a new method to estimate these perceptual features. The proposal based on two representations: Original Image Representation and Autocorrelation Function Representation. These estimated perceptual features measures are applied to classification on large image data set, the well-known Brodatz database using k-nearest neighborhood classifier.
Inspection of Multilingual Neural Machine Translation

In this paper we inspect the intermediate sentence representation in the multilingual attention-based NMT system proposed by Ha et al. (2016). We ask the question of how well the NMT system learns a shared representation across multiple languages, as such a shared representation is an important prerequisite for zero-shot translation. To this end we examine whether the sentence representation is independent of the individual languages involved in translation. Having found the sentence representation in our multilingual NMT system to be language dependent, we further inspect the sentence representation for the cause of this dependence. We isolated the language dependent features, and found present a linear correlation between the sentence representation and its source language. Using these isolated features, we describe a method to manipulate these features, and provide a way to eliminate the language specific differences between the sentence representations. This could potentially help to remove noise, which is particularly harmful for zero-shot translation.
Consequences of implementing a reservoir operation algorithm in a global hydrological model under multiple meteorological forcing

ABSTRACT We investigated dam behaviours during high-flow events and their robustness against perturbations in meteorological conditions using the H08 global hydrological model. Differences in these behaviours were examined by comparing simulation runs, with and without dams and using multiple meteorological datasets, at a case-study site, Fort Peck Dam on the Missouri River, USA. The results demonstrated that dam-regulated river flow reduced temporal variability over large time periods and also dampened inter-forcing discrepancies in river discharge (smoothing effects). However, during wet years, differences in peak flow were accentuated downstream of the dam, resulting in divergence in simulated peak flow across the meteorological forcing (pulsing effect). The pulsing effect was detected at other major dams in global simulations. Depending upon the meteorological forcing, the dams act as a selective filter against high-flow events. Synergy between a generic dam scheme and differences in meteorological forcing data might introduce additional uncertainties in global hydrological simulations.
Ensemble air quality forecasts over the Lower Fraser Valley, British Columbia: a summer 2004 case study

The ensemble-averaging approach is potentially a technique for improving the performance of real-time photochemical airquality modeling. Ensemble photochemical airquality forecasts are tested extensively using the Community Multiscale Air Quality (CMAQ) model-system with mesonet observations from the Emergency Weather Net (EmWxNet) and the Quality-Controlled AQ Data Set over the Lower Fraser Valley (LFV). The CMAQ model is run daily over a 12 km resolution domain (Figure 1 top) covering southern British Columbia, Washington State, and the northern portion of Oregon State. A 4 km resolution grid (Figure 1 bottom) is nested within the 12km grid, and it covers the southern tip of British Columbia (including the LFV) and the northern part of Washington State (including the Seattle area). CMAQ is driven by two different meteorological models: the Mesoscale Compressible Community Model (MC2), and the Fifth-Generation NCAR / Penn State Mesoscale Model (MM5).
Think More Ambiguity Less: A Novel Dual Interactive Model with Local and Global Semantics for Chinese NER

Chinese is a representative East Asian Language. Chinese Named Entity Recognition (CNER) aims to recognize various entities. It is signiicant for other NLP tasks to utilize CNER. Recent research to develop CNER systems has been dedicated to either considering word enhancement or capturing global information to strengthen local composition and alleviate word ambiguity in the meanings of words. However, information on words acquired from external lexicons is often confused, and this has led to incorrect judgments regarding the boundaries of words. Moreover, relevant studies typically use excessively complex models to capture the global semantics of sentences. To solve these two problems, we incorporate a global representation into the procedure of local word enhancement. We propose an intuitive and efective dual-module interactive network that can enhance the boundaries of words and extract the global semantics by using a rethinking mechanism to reine the importance of local composition and global information. The results of experiments on four CNER datasets showed that the proposed model can outperform other baselines in terms of the F1 score.
The Application of Structured Feedforward Neural Networks to the Modelling of Daily Series of Currency in Circulation

One of the most significant factors influencing the liquidity of the financial market is the amount of currency in circulation. Although the central bank is responsible for the distribution of the currency it cannot assess the demand for the currency, as that demand is influenced by the non-banking sector. Therefore, the amount of currency in circulation has to be forecasted. This paper introduces a feedforward structured neural network model and discusses its applicability to the forecasting of currency in circulation. The forecasting performance of the new neural network model is compared with an ARIMA model. The results indicate that the performance of the neural network model is better and that both models might be applied at least as supportive tools for liquidity forecasting. JEL Codes: C45, C53.
The Thisl SDR System at TREC-9

This paper describes our participation in the TREC-9 Spoken Document Retrieval (SDR) track. The THISL SDR system consists of a realtime version of a hybrid connectionist/HMM large vocabulary speech recognition system and a probabilistic text retrieval system. This paper describes the configuration of the speech recognition and text retrieval systems, including segmentation and query expansion. We report our results for development tests using the TREC-8 queries, and for the TREC-9 evaluation.
Representation Quality Of Neural Networks Links To Adversarial Attacks and Defences

Neural networks have been shown vulnerable to a variety of adversarial algorithms. A crucial step to understanding the rationale for this lack of robustness is to assess the potential of the neural networks' representation to encode the existing features. Here, we propose a method to understand the representation quality of the neural networks using a novel test based on Zero-Shot Learning, entitled Raw Zero-Shot. The principal idea is that, if an algorithm learns rich features, such features should be able to interpret "unknown" classes as an aggregate of previously learned features. This is because unknown classes usually share several regular features with recognised classes, given the features learned are general enough. We further introduce two metrics to assess these learned features to interpret unknown classes. One is based on inter-cluster validation technique (Davies-Bouldin Index), and the other is based on the distance to an approximated ground-truth. Experiments suggest that adversarial defences improve the representation of the classifiers, further suggesting that to improve the robustness of the classifiers, one has to improve the representation quality also. Experiments also reveal a strong association (a high Pearson Correlation and low p-value) between the metrics and adversarial attacks. Interestingly, the results indicate that dynamic routing networks such as CapsNet have better representation while current deeper neural networks are trading off representation quality for accuracy. Code available at this http URL.
RETRACTED: A Comparative Analysis of Pre-Processing Time in Summary of Hindi Language using Stanza and Spacy

Text summarization is a method or say way for converting large texts into smaller ones by keeping all important points of the larger text as it is in the smaller ones and giving the output in the modified form called summary of original text document. This task is very difficult for humans as it required large amount of rigorous analysis of the document. In this proposed paper we are comparing pre-processing time for two tools of natural language processing one is STANZA and the other is SPACY both are based on modern technologies and are examine for HINDI language processing. In this paper we are preforming a comparative study of both the tools on the bases of their pre-processing time for processing HINDI language. Now a days, a lot of text summarizer tools are present in the environment and we are keen to use them but here the point of this paper comes into light that how we know which Summarizer is fast enough to get us the same accuracy.
Application of the methodology related to the emission standard to specific railway line in comparison with parallel road transport: a case study

At present, the impact of transport on the environment constitutes a serious problem. This mainly concerns energy consumption and production of greenhouse gases (GHG) that via their participation in greenhouse effect intensification contribute to global warming. In this paper, the calculations provided by the EN 16258: 2012 methodology will be applied to two modes of transport: railway and road transport. Subsequently, the methodology will be applied to a case study of a selected nonelectrified railway line in comparison with parallel road transport. In particular, energy consumption and production of greenhouse gases will be monitored depending on traveling the distance between selected cities in the Czech Republic.
Stress Shadow on the Southwest Portion of the Longmen Shan Fault Impacted the 2008 Wenchuan Earthquake Rupture

The 2008 Wenchuan earthquake (Mw 7.9) unilaterally ruptured the Longmen Shan fault (LMSF) along eastern Tibet. The earthquake rupture propagated about 270 km northeastward, whereas it propagated only about 20 km southwestward along the strike of the fault. Although a significant attention has been paid to the question of predominantly unilateral propagation, the primary reasons for this type of propagation remain unclear. In this research, we examined the change of Coulomb stress along the LMSF caused by the historical earthquakes near and on the LMSF from 1725 to 2008. We found that the 14 preceding large earthquakes (M ≥ 6.5) on the Xianshui He fault cast a stress shadow on the SW segment of the LMSF, which was not activated by the 2008 Wenchuan earthquake rupture. The 1970 Dayi earthquake on the SW segment of the LMSF contributed significantly to this stress shadow. Compared with the segment of the 2008 Wenchuan earthquake rupture, this stress shadow caused strong stress contrasts of −214, −22, and −80 kPa in the seismic gap of the very SW segment of the LMSF, the rupture zone of the 2013 Lushan earthquake, and the seismic gap between the 2008 Wenchuan and 2013 Lushan earthquake ruptures, respectively. Stress contrasts in these values were consistent with tectonic loading over 165, 17, and 62 years, respectively, by integrating an interseismic stress‐loading rate of 1.3 kPa/a. We proposed that this stress shadow might have created a barrier at the SW segment, preventing the earthquake rupture propagating southwestward.
This paper is included in the Proceedings of the 20th USENIX Conference on File and Storage Technologies.

We present NyxCache (Nyx), an access regulation framework for multi-tenant persistent memory (PM) caching that supports light-weight access regulation, per-cache resource usage estimation and inter-cache interference analysis. With these mechanisms and existing admission control and capacity allocation logic, we build important sharing policies such as resource-limiting, QoS-awareness, fair slowdown, and proportional sharing: Nyx resource-limiting can accurately limit PM usage of each cache, providing up to 5 ⇥ better performance isolation than a bandwidth-limiting method. Nyx QoS can provide QoS guarantees to latency-critical caches while providing higher throughput (up to 6 ⇥ vs. previous DRAM-based approaches) to best-effort caches that are not interfering. Finally, we show that Nyx is useful for realistic workloads, isolating write spikes, and ensuring that important caches are not slowed down by increased best-effort trafﬁc.
Hop-by-Hop versus End-to-End Congestion Control in Wireless Multi-Hop UWB Networks

We perform a comparative study of the performance of hop-by-hop and end-to-end congestion control mechanisms for wireless multi hop network. We consider the effect of using different utility functions and study their effect on throughput and peak queue size. The multiple access communication is assumed to be ultra-wide bandwidth (UWB) time-hopping spread-spectrum (TH-SS) impulse radio. The joint congestion and power control is formulated as an optimization problem which is then solved by distributed iterative algorithms for flow rate adaptation and power adaptation. Significant reduction in the buffer size requirement can be achieved by using hop- by-hop approach, under high propagation delays. Use of power control reduces the buffer size requirement, in addition to providing improvement in energy efficiency.