Miracle-dz/UniFAR-FTUs huggingface dataset:
Conference on Empirical Methods in Natural Language Processing Proceedings of the Fourth International Workshop on Natural Language Processing for Social Media Socialnlp@emnlp2016 Chairs' Welcome Identifying and Categorizing Disaster-related Tweets Why Do They Leave: Modeling Participation in Online

It is our great pleasure to welcome you to the Fourth ACM Workshop on Natural Language Processing for SocialMedia – SocialNLP'16, associated with EMNLP 2016. SocialNLP is an inter-disciplinary area of natural language processing (NLP) and social computing. We hold SocialNLP twice a year: one in the NLP venue, the other in the associated venue such as those for web technology or artificial intelligence. There are three plausible directions of SocialNLP: (1) addressing issues in social computing using NLP techniques; (2) solving NLP problems using information from social media; and (3) handling new problems related to both social computing and natural language processing. Through this workshop, we anticipate to provide a platform for research outcome presentation and head-to-head discussion in the area of SocialNLP, with the hope to combine the insight and experience of prominent researchers from both NLP and social computing domains to contribute to the area of SocialNLP jointly. The submissions to this year's workshop were again of high quality and we had a competitive selection process. We received 32 submissions from Asia, Europe, and the United States, and due to a rigorous review process, we only accepted 6 as long oral papers and 7 as short oral papers. Thus the acceptance rate was 40 percent. We are delighted to have Prof. Cristian Danescu-Niculescu-Mizil, from Cornell University, as our keynote speaker. We also encourage attendees to attend the keynote talk presentation. The valuable and insightful talk can and will guide us to a better understanding of the future. Putting together SocialNLP 2016 was a team effort. We first thank the authors for providing the quality content of the program. We are grateful to the program committee members, who worked very hard in reviewing papers and providing feedback for authors. Finally, we especially thank the Workshop Committee Chairs Prof. We hope you join our community and enjoy the workshop! Abstract This paper presents a system for classifying disaster-related tweets. The focus is on Twit-ter data generated before, during, and after Hurricane Sandy, which impacted New York in the fall of 2012. We propose an annotation schema for identifying relevant tweets as well as the more fine-grained categories they represent , and develop feature-rich classifiers for relevance and fine-grained categorization.
Synthesis, Molecular Docking and In Vitro Screening of Some Newly Synthesized Triazolopyridine, Pyridotriazine and Pyridine–Pyrazole Hybrid Derivatives

A series of novel pyridine and fused pyridine derivatives have been prepared starting from 6-(3,4-dimethylphenyl)-2-hydrazinyl-4-(thiophen-2-yl)-pyridine-3-carbonitrile 1 which on treatment with appropriate formic acid, acetic acid/acetic anhydride, benzoyl chloride and/or carbon disulfide afforded the corresponding triazolopyridine derivatives 2–5. Also, treatment of hydrazide 1 with diethyloxalate, chloroacetyl chloride, chloroacetic acid and/or 1,2-dichloroethane yielded the corresponding pyridotriazine derivatives 7–10. Further transformation of compound 1 with a different active methylene group, namely acetyl acetone, diethylmalonate, ethyl cyanoacetate, ethyl benzoylacetate and/or ethyl acetoacetate, produced the pyridine–pyrazole hybrid derivatives 11–15. These newly synthesized compounds (1–15) were subjected to in silico molecular docking screenings towards GlcN-6-P synthase as the target protein. The results revealed moderate to good binding energies of the ligands on the target protein. All the newly prepared products exhibited antimicrobial and antioxidant activity.
Indoor high-precision three-dimensional positioning algorithm based on visible light communication and fingerprinting using K-means and random forest

Abstract. Recently, visible light communication (VLC) has been widely used in indoor positioning. Considering that conventional VLC-based positioning (VLP) algorithms are susceptible to interference and cannot adapt well to the complex indoor location environment, we propose a high-precision indoor three-dimensional positioning algorithm based on VLC and fingerprinting using fusion of K-means and random forest algorithms. Unlike the trilateration techniques based on received signal strength, the mentioned system does not depend on the model parameters and is robust to external interference. Besides, the proposed system introduces the concept of VLP kernel to compress the fingerprint database and shorten the training time. And we have validated the feasibility of our proposed VLC-based positioning system using fingerprinting in the real indoor VLP environment. Both simulation and experiment results verify that our proposed algorithm delivers satisfactory performance in terms of real-time ability and positioning accuracy, both of which are crucial for the performance of an indoor positioning system. Therefore, our proposed positioning system with strong interference immunity is a promising candidate for future indoor positioning applications.
Value at Risk and Expected Shortfall estimation with Generative Adversarial Networks

In recent years, the field of Quantitative Finance has witnessed significant advancements in the application of Machine Learning (ML) modelling techniques, as they offer greater expressive power than traditional methods, reduce the need for domain experts and generally rely on less rigid assumptions. Generative models such as Generative Adversarial Networks (GANs) learn the underlying distribution of financial time series in an unsupervised fashion, generating meaningful synthetic data from noise and could greatly enrich the tools of financial engineers. We give an overview of two widely adopted methods to compute Value at Risk (VaR) and Expected Shortfall (ES), namely Historical Simulation (HS) and Peaks Over Threshold (POT) and propose a simple algorithm to compute such estimates using GANs, qualitatively comparing the methods on data simulated from two well known stochastic processes with promising results, especially for yearly time horizons.
Scale interactions and diabatic processes during the development of an Atlantic blocking event

Results of a case study of the development of a major blocking event over the North Atlantic Ocean are reported with emphasis on the potential roles of interactions between synoptic-scale systems and diabatic processes in the large-scale development. The approach used here involves the analysis of the evolution of the potential temperature on a constant potential vorticity (PV) surface, where the value of PV is chosen to be representative of that of the tropopause. The approach is shown to provide a simple way to qualitatively identify significant features in the development of the block, including scale interactions and diabatic processes.
International Reserves: Precautionary vs. Mercantilist Views, Theory and Evidence

This paper compares the importance of precautionary and mercantilist motives in the hoarding of international reserves by developing countries. Overall, empirical results support precautionary motives; in particular, a more liberal capital account regime increases international reserves. Theoretically, large precautionary demand for international reserves arises as a self-insurance to avoid costly liquidation of long-term projects when the economy is susceptible to sudden stops. The welfare gain from the optimal management of international reserves is of a first-order magnitude, reducing the welfare cost of liquidity shocks from a first-order to a second-order magnitude.
The Dynamics of Market Entry: The Effects of Mergers and Acquisitions on De Novo Entry and Small Bus

The brake pads of a disc brake are retained in the housing by a pair of pins, each engaging its own pad. The pads are prevented from tilting about their pins by other means which can be further pins. If these further pins fit into slots in the ends of the pads, it is not necessary to remove them during pad removal. A detent can be provided to hold the brake pistons in their cylinders during pad removal.
AutoHLS: Learning to Accelerate Design Space Exploration for HLS Designs

High-level synthesis (HLS) is a design flow that leverages modern language features and flexibility, such as complex data structures, inheritance, templates, etc., to prototype hardware designs rapidly. However, exploring various design space parameters can take much time and effort for hardware engineers to meet specific design specifications. This paper proposes a novel framework called AutoHLS, which integrates a deep neural network (DNN) with Bayesian optimization (BO) to accelerate HLS hardware design optimization. Our tool focuses on HLS pragma exploration and operation transformation. It utilizes integrated DNNs to predict synthesizability within a given FPGA resource budget. We also investigate the potential of emerging quantum neural networks (QNNs) instead of classical DNNs for the AutoHLS pipeline. Our experimental results demonstrate up to a 70-fold speedup in exploration time.
Stakeholder process within limestonequarrying and potential impacts onbiodiversity and ecosystem services. Case study of Bunge Ducker,Northern Gotland, Sweden

Mining has always played an important role in human development. The growing demand for miningnowadays puts under threat biodiversity and ecosystem services, which leads to many conflicts based on choicebetween environment protection and social and economical development. Studying a case of a limestone quarryon Northern Gotland as one of the most representative cases of such conflict, gave an opportunity to investigateand analyze the cost of the decision-making in mining as well as interaction between stakeholders affecting andbeing affected by the decision from the perspective of the group opposing to mining. The research showedweakness of communication between the stakeholders; the weakness of the argument supporting social andeconomical development and giving it higher priority than the environment; and the best possible solution whichwould be turning the whole area of interest into a national park, even though such solution would not fulfilltheinterests of the mining company.
A computer program for glacier-surface plain-strain analysis

concerning the entrenchment of the term "pro-talus rampart" in the literature. My advocacy of Daly's (1912) term "winter-talus ridge" was based primarily on its primacy over the later terms "nivation ridge" and "pro-talus rampart". In the light of Porter's examples from the historical literature, this is no longer an issue. Ballantyne's (in press) forthcoming paper, as well as the process studies cited by Porter, clearly shed doubt on the genetic accuracy of Daly's term. Finally, I completely agree with Ballantyne (1987) that the traditional definition of the term "pro-talus rampart" will eventually require revision and, in the light of Porter's comments, that the term "pro-talus rampart" remains a viable and preferred one. The definition I presented (Butler, 1986) was simply a summary of currently utilized working definitions . The works of Harris (1986) and Ono and Watanabe (1986), works not published at the time my previous letter was written, indeed illustrate the problems with a morphogenetic definition based solely on one form of genesis. I am not, however, at this time prepared to adopt Ballantyne's (1987) "more general definition", particularly in the light of recent studies which attribute glacial origins to features previously described as owing their genesis to pro-talus processes (e.g. Gardner and others, 1983, p. 171). As pointed out by Madole (1972, p. 122), "the polygene tic origin of talus makes it both the most complex and ill-defined facies", and as Porter states, much work obviously remains to be done before a thoroughly accurate definition will be available.
Rapid Adaptive Climate Change Model: Application of a Probabilistic Centred Approach to the Minas Passage Bay of Fundy datasets

We are interested in modelling smaller climate datasets in order to generate more accurate forecasts. Our approach would be to develop a fractal-based system around the mathematical attractor for a particular dataset, building on the ideas developed by the authors of this paper, to "measure" the distance between the model and the subsequently observed dataset. The goal of the research would be to develop a model where climate data could be iterated comparatively between models and a meaningful comparison made from which forecasts can be drawn. This is expected to be particularly useful given the increasing rapidity of climate change. This paper presents the initial findings of this research focused around discoveries related to the number of Intrinsic Mode Functions (IMFs) required for the decomposition of the signal, obtained by the Empirical Mode Decomposition (EMD), independent from the number of observations.
Explaining Agent Preferences & Behavior: Integrating Reward-Decomposition & Contrastive-Highlights

Explainable reinforcement learning methods aim to help elucidate agent policies and their underlying decision-making processes. One such method is reward decomposition, which aims to reveal an agent’s preferences in a specific world-state by presenting its expected utility decomposed to different components of the reward function. While this approach quantifies the expected decomposed rewards for alternative actions, it does not demonstrate the outcomes of these alternative actions in terms of the behavior of the agent. This work introduces “Contrastive Highlights”, a novel local explanation method that visually compares the agent’s chosen behavior to an alternative choice of action in a contrastive manner. We conducted user studies comparing participants’ understanding of agents’ preferences based on either reward decomposition, contrastive highlights, or a combination of both approaches. Our results show that integrating reward decomposition with contrastive highlights significantly improved participants’ performance compared to using each of the approaches separately.
Ground-truthing AVIRIS mineral mapping at Cuprite, Nevada

Mineral abundance maps of 18 minerals were made of the Cuprite Mining District using 1990 AVIRIS data and the Multiple Spectral Feature Mapping Algorithm (MSFMA) as discussed in Clark et al. This technique uses least-squares fitting between a scaled laboratory reference spectrum and ground calibrated AVIRIS data for each pixel. Multiple spectral features can be fitted for each mineral and an unlimited number of minerals can be mapped simultaneously. Quality of fit and depth from continuum numbers for each mineral are calculated for each pixel and the results displayed as a multicolor image.
Some insights into shea tree (Vitellaria paradoxa C.F. Gaertn) history in West Africa

Pons O, Petit RJ (1996) Measuring and testing genetic differentiation with ordered vs. Unordered alleles. Genetics, 144, 1237–1245. While the genetic structure of many rainforest plants species of West Africa is mainly explained by climatic oscillations, very little is know about savannah species. Savannahs are major biomes both in term of range area and of biodiversity and there is an urgent need to better understand them to propose efficient conservation strategies. Shea tree is of particular interest as being a major component of savannahs in West Africa. It also plays an important social and economical role in these regions as the butter extracted from its kernels is widely used both for food and cosmetics. In this study, we bring new insight into the evolutionary processes affecting West Africa savannah by investigating the genetic diversity and structure of Shea tree (Vitellaria paradoxa C. F. Gaertn) in relation to past climate changes.
Software for Extracting 3D - MSSTs

The deep structure of an image is investigated, and a Multi-Scale Singularity Tree (MSST) is constructed based on the pair-wise annihilations of critical points. This report contains two main contributions. Firstly, we describe a fast, simple, and robust method of extracting feature lines from data sets of up to four dimensions, which we apply in order to extract critical paths from (cid:0)(cid:2)(cid:1)(cid:4)(cid:3)(cid:6)(cid:5) scale-spaces. Secondly, we investigate the extracting of MSSTs using either support regions or extrema partitions. Given an image, both methods produce a binary tree that mathematically represents the topological structures of the image. The software described in this report is available through the EU-project, Deep Structure, Singularities, and Computer Vision
Risk Prediction of Cardioembolic Stroke using Clinical Data and Non-contrast CT

Cardioembolic stroke is a dangerous subtype of ischemic stroke. Patients with this subtype need special treatments to prevent recurrent events that might be fatal. Thus, identifying underlying stroke categories between cardioembolic and non-cardioembolic subtypes is of great importance. We propose a multimodal machine learning model that takes into account basic clinical information and non-contrast computed tomography (CT) images to predict the risk of cardioembolic stroke. The clinical information is not only used to provide additional information for the classification model but also to guide the attention module to extract better image features. Our model achieves a score of 0.840 using the area under the receiver operating characteristic curve (ROC-AUC) metric. Besides the capability to classify the stroke subtypes, the method can provide a heatmap for large infarct localization, which is crucial for stroke diagnosis.
Simulation and Experimental Study of Static Torque Characteristics of a 12/8 Three-Phase SRM

This study presents a simulation and experimental investigation of static torque characteristics of a 12/8 three-phase switched reluctance motor (SRM). The experimental study was carried out by developing an experimental setup to measure the static torque of the machine for different magnitudes of phase current and rotor positions. The simulation results for the magnitude of the static torque are obtained using the finite element analysis method FEM and the software simulator (Infolytica MotorSolve). The simulation and experimental results were compared with a relative error of 5%. The obtained characteristics can be used to design a nonlinear motor model for operation in the MATLAB/Simulink environment.
Design Hydrology and Sedimentology for Small Catchments

Introduction. Hydrologic Frequency Analysis. Rainfall-Runoff Estimation in Stormwater Computations. Open Channel Hydraulics. Hydraulics of Structures. Channel Flow Routing and Reservoir Hydraulics. Sediment Properties and Sediment Transport. Erosion and Sediment Yield. Sediment Control Structures. Fluvial Geomorphology: Fluvial Channel Analysis and Design. Ground Water. Monitoring Hydrologic Systems. Hydrologic Modeling. Chapter Problems, References, and Appendixes. General Appendix. Subject Index.
THE FLOTATION OF HIGH TALC-CONTAINING ORE FROM THE GREAT DYKE OF ZIMBABWE

This project investigates the optimisation of platinum-group metals recovery at the Mimosa Mine in Zimbabwe. The first part of this research investigates how collector performance can be improved by using collector combinations (mixtures) of the standard sodium iso-butyl xanthate with a secondary collector. The synergistic effect of the sodium-iso-butyl-xanthate (SIBX) combined with trithiocarbonates (TTCs) or dithiophosphates (DTP) was investigated. The short chain and the long chain TTCs were investigated. These collector combinations were studied at various molar percent ratios. The SIBX:DTP combination was studied at a 1:1 mass ratio or molar ratio of 1:1.3. Amongst all the collector combinations, it was established that the SIBX/DTP combination improved sulphur recoveries by 10% and subsequently the PGE+Au recovery by 10% compared to when the SIBX was used on its own. The C4-TTC showed poor performance in the sulphur recovery; however an improvement in PGE + Au recovery was noticeable. There was generally no significant metallurgical improvement with respect to final grades and recoveries with C10 and C12TTC
Uncertainty-Aware TSO-DSO Coordination Methodology for Transmission Voltages Control

This paper proposes a new coordination methodology between the Transmission System Operator (TSO) and Distribution System Operators (DSOs), which enables the optimal usage by the TSO of reactive power flexibility from active distribution systems (ADSs), as an additional option to control voltages in the transmission system. The proposed methodology addresses day-ahead operation planning in three successive steps: 1) the quantification by DSOs of the reactive power flexibility range and associated cost at the interfaces with the TSO, 2) voltage control at TSO level through reactive power dispatch, including reactive power flexibility offered by the DSOs, and 3) optimal re-dispatch of flexible resources by DSOs to maintain the committed active/reactive power schedule at their TSO-DSO interface without breaching operation constraints of the ADSs. The methodology goes beyond the state-of-the-art by considering renewable generation uncertainties for both TSO and DSO, N-1 security for TSO, and temporal inter-links of emerging flexible resources, such as flexible loads (FL) and energy storage systems (ESS), for DSO. The proposed methodology solves stochastic multi-period AC security-constrained optimal power flow (S-MP-SCOPF) for TSO and stochastic multi-period AC optimal power flow (S-MP-OPF) for DSO, both problems being formulated as non-linear programs (NLPs). The usefulness of the proposed methodology is demonstrated on a 60-bus transmission system that contains five interfaces to 34-bus ADSs.
Characteristics of negative ion current by control of bias voltage to second anode in Cs-free negative ion source using TPDSheet-U

The volume production of negative hydrogen ions in the produced magnetized sheet plasma is proposed to be developed as a Cs-free negative-ion source using TPDsheet-U. The extracted current density characteristics with respect to changes in the bias applied voltage to the second anode are clarified. When negative ions were extracted at 10 kV by applying a bias voltage of 0 V to + 8 V to the second anode, the extracted negative ion current value increased 1.15 times from 3.27 mA/$cm^{2}$ to 3.75 mA/$cm^{2}$ at a discharge current of 80 A and a gas pressure of 0.3 Pa. On the other hand, the current ratio $J_{EG}(e)/J_{c}(H^{-})$ of the co-extracted electron and negative ion current values could be maintained at about 0.18 to 0.47 by using a soft magnetic material filter (SMF).
An Automotive Communication Bus Using OFDMA

Today’s automotive communication systems use baseband pulse modulation and time-division or carrier-sense multiple access. In future in-vehicle networks, more devices need to be connected in a multipoint-to-multipoint topology. We have developed a new automotive bus system based on orthogonal frequency division multiple access (OFDMA) enabling better channel adaptation and fine-granular multi-user access. We highlight the advantages of OFDMA in automotive and industrial applications, introduce our system concept and demonstrate its feasibility by means of a prototype. Finally, we discuss the use of electrical and optical media.
Multiple-Source Multiple-Sink Maximum Flow in Directed Planar Graphs in $O(n^{1.5} \log n)$ Time

We give an O(n 1:5 logn) algorithm that, given a directed planar graph with arc capacities, a set of source nodes and a single sink node, nds a maximum ow from the sources to the sink . This is the rst subquadratictime strongly polynomial algorithm for the problem.