Miracle-dz/UniFAR-FTUs huggingface dataset:
Inspecting Unification of Encoding and Matching with Transformer: A Case Study of Machine Reading Comprehension

Most machine reading comprehension (MRC) models separately handle encoding and matching with different network architectures. In contrast, pretrained language models with Transformer layers, such as GPT (Radford et al., 2018) and BERT (Devlin et al., 2018), have achieved competitive performance on MRC. A research question that naturally arises is: apart from the benefits of pre-training, how many performance gain comes from the unified network architecture. In this work, we evaluate and analyze unifying encoding and matching components with Transformer for the MRC task. Experimental results on SQuAD show that the unified model outperforms previous networks that separately treat encoding and matching. We also introduce a metric to inspect whether a Transformer layer tends to perform encoding or matching. The analysis results show that the unified model learns different modeling strategies compared with previous manually-designed models.
R.V. Adam, D. Villarroel-Lamb and F. Muddeen: CubeSat Communication Sub-system Design for Coastal Marine Monitoring Applications

In this paper, we present a design for a CubeSat communication subsystem for a store-and-forward remote monitoring application that will transfer data which originates from an underwater Acoustic Doppler Current Profiler (ADCP) at a remote coastal location to a central hub for processing. For this design we determined the bandwidth requirements of the ADCP; performed the LEO constellation design for different orbital altitudes; determined, with the aid of AGI’s STK simulation tool, the worst-case bit-rate that can be accommodated during the visible period; explored different frequency bands for transmission with the UHF/VHF and the S-band being selected as like candidates; designed the antenna; selected transceivers for the ground station and CubeSat; and performed link budgets for the different altitudes. Additionally, the paper discusses CubeSat design considerations and other terrestrial and non-terrestrial transmission alternatives to CubeSat.
United States Lifeguard Standards Coalition

In most cases, for each question, two independent investigators researched existing evidence, including scientific research and other material, related to the question. Each investigator then completed a Scientific Review Form, listing the evidence and an evidence summary. The level and quality of evidence was rated using a standardized evidence evaluation process. The evidence reviewed included, but was not limited to, the following:
A Frame Work for Communities Detection in Social Media Networks

Enormous participation of users creates new opportunities to study human social behavior along with the capability to analyze large amount of data streams. Community detection in social media helps to reshape business models. Social sciences frames the concept of community, the problem of community detection in the context of social media provides a compact classification of existing algorithms based on their methodological principles. Positive or negative attitudes can be inferred from the discussions it require a formal interpretation of social media logs and unit of information that can spread from person to person through the social network. Once the social media data such as user messages are parsed and network relationships are identified, datamining techniques can be applied to group different types of communities. The Proposed framework introduces the novel task of detecting communities by clustering messages from large streams of social data. This frame work uses clustering algorithm and groups the user communities based on their activities. This approach is optimized and scalable for real-time clustering of social media data.
INVESTIGATING SHEAR TRANSFER ACROSS CRACKS IN HIGH PERFORMANCE STEEL FIBER-REINFORCED CONCRETE

This paper presents a rational shear transfer model for high-strength steel fiber-reinforced concrete (HSSFRC). The model inherently considers shear transfer across smooth cracks, bridged by stiff steel fibers, and the resulting crack slip. When implemented into a nonlinear FE algorithm, the model is shown to perform well in simulating the response of uniaxially reinforced HSSFRC panels in shear, including load-deformation response and inclination of principal stress-strain fields. It is shown that while steel fibers are effective in limiting crack slip, they may also deteriorate shear transfer capacity.
MSR Asia MSM at ActivityNet Challenge 2016

This notebook paper presents overview and comparative analysis of our system designed for untrimmed video classification task in ActivityNet Challenge 2016. We investigate and exploit multiple spatio-temporal clues, i.e., frames, motion (optical flow), and short video clips, using 2D or 3D convolutional neural networks (CNNs). The mechanism of different quantization methods are studied as well. Furthermore, improved dense trajectory with fisher vector encoding on long video clips and MFCC audio features are utilized. All activities are classified by late fusing the predictions of one-versus-rest linear SVMs learnt on each clue. Finally, OCR is employed to refine the prediction scores.
Sequential method for rapid early diagnosis of white spot syndrome virus in crayfish

We developed a practical method to rapidly detect and diagnose latent white spot syndrome virus (WSSV) in infected crayfish that were non-symptomatic for WSSV. This method included a simplified extraction of DNA template, optimized loop-mediated isothermal amplification (LAMP), and final visualization of the product by means of staining with SYBR green I. Using this method, WSSV was detected in crayfish that had been artificially infected in two ways: at 5 h after injection, and 24 h after feeding with tissue from WSSV-infected crayfish (at a stage when such infected crayfish were nonsymptomatic), and a thousand times or more dilution can omit fluorescent background when SYBR green I was used. Results indicate that this was a rapid, convenient, and highly sensitive method for the early diagnosis and detection of WSSV. The whole detection procedure took less than one hour to complete.
IMPLEMENTING A SOA USING SCRUM: ACHIEVING IMPROVED PRODUCTIVITY USING SERVICE TREES

Service Oriented Architecture (SOA) is spreading widely today in a variety of development environments. Many organizations pursing a SOA, however, are unaware of the impact that this approach has on their development methodology and implementation strategies, which may be inappropriate and inefficient. Some issues caused by an inappropriately managed SOA implementation include dependency conflicts, inefficient time utilization, and ineffective utilization of resources resulting in a backlog of tasks. The individuality of services creates a gap that makes service implementation a relevant topic to pursue which can be addressed through the granular development of these services. Consequently, this research-in-progress examines SOA-based services development by focusing on service generation and proposing the concept of service trees whose branches serve as nodes of SOA-based services. Further, implementing the tree’s branches utilizing the scrum development methodology produces an approach where multiple services can be developed in parallel, offering a means to improve the productivity of a SOA implementation.
Assessment of maturity of vineyard pruning compost by Fourier Transform Infrared Spectroscopy, biological and chemical analyses

Maturity and organic matter transformation of vineyard pruning residues characteristics composted in open wooden rotting boxes (V = 0.25 m(3)) for 122 days at initial C:N ratios of 60 (origin), 52, 47 and 29, adjusted by urea addition, were assessed by combinative means of FT-IR spectroscopy, chemical and biological analyses. The self-heating test (22 degrees C), the pH (7.7 to 7.9) and the germination index (93 to 105) indicate a mature compost, the C:N ratio (16 to 32) is higher than the recommended value in literature. Decreases in the C:N ratio, organic matter and phytotoxicity, an increase of the pH value, rotting degree, a strong decrease of the aliphatic compounds and an increase of aromatic carbon occurred during composting. The FT-IR spectroscopy showed a new peak at 1265 cm(-1) appeared as P=O groups relative to phosphatic salts in treatments with urea. The addition of urea as a N source can speed the rotting process of vineyard pruning residues to get mature compost, but results in higher degradation of organic matter and losses of gaseous nitrogen in form of ammonia. FT-IR spectroscopy combined with other chemical and biological analyses could be a valuable toot for testing organic matter conversion and compost maturity of vineyard pruning residues. Ultimately, only the utilisation of compost and its effect on the plant growth can give the final answer about the maturity and quality of compost, even the effect of the compost depends on several criteria as time of utilisation and kind of crop.
Option Implied Dependence

We propose a novel model-free approach to infer a joint risk-neutral dependence among several assets. The dependence can be estimated when traded options are available on individual assets as well as on their index. In the empirical application, we implement our approach using options on the S&P 500 index and its nine sectors. We find that option-implied dependence is highly non-normal and time-varying. Using the estimated dependence, we then study the correlation risk conditional on the market going down or up. We find that the risk premium for the down correlation is strongly negative, whereas it is positive for the up correlation. These findings are consistent with the economic intuition that the investors are particularly concerned with the loss of diversification when financial markets fall. As a result, they are willing to pay a considerable premium to hedge against increases in correlation during turbulent times. However, the investors actually prefer high correlation when markets rally. Key-words: Model-free dependence, implied correlations, forward-looking dependence, down and up correlation.
An Improved Method for the Transmission of Large Image over Internet

We have proposed a wavelet-based image transmission scheme in this paper which has used discrete wavelet transform to transform a digital image from spatial domain into frequency domain. Haar wavelet transformation has been chosen and used for wavelet transformation phase. Use of concurrent computing has significantly reduced computation time overhead as well as the transmission time up to a great extent as compared to the existing sequential discrete wavelet based transmission. The experimental result shows that the proposed scheme maintains the accuracy of reconstructed image.
Achieving Accurate Room-Level Indoor Location Estimation with Emerging IoT Networks

A motivating, core capability of most smart, Internet of Things enabled spaces (e.g., home, office, hospital, factory) is the ability to leverage context of use. Location is a key context element; particularly indoor location. Recent advances in radio ranging technologies, such as 802.11-2016 FTM, promise the availability of low-cost, near-ubiquitous time-of-flight-based ranging estimates. In this paper, we build on prior work to enhance the technology's ability to provide useful location estimates. We demonstrate meaningful improvements in coordinate-based estimation accuracy and substantial increases in room-level estimation accuracy. Furthermore, insights gained in our real-world deployment provides important implications for future Internet of Things context applications and their supporting technology deployments such as workflow management, inventory control, or healthcare information tools.
Learning from Trajectories via Subgoal Discovery /Author=Paul, S.; van Baar, J.; Roy Chowdhury, A.K. /CreationDate=October 31, 2019 /Subject=Applied Physics, Computer Vision, Machine Learning

Learning to solve complex goal-oriented tasks with sparse terminal-only rewards often requires an enormous number of samples. In such cases, using a set of expert trajectories could help to learn faster. However, Imitation Learning (IL) via supervised pre-training with these trajectories may not perform as well and generally requires additional finetuning with expertin-the-loop. In this paper, we propose an approach which uses the expert trajectories and learns to decompose the complex main task into smaller sub-goals. We learn a function which partitions the state-space into sub-goals, which can then be used to design an extrinsic reward function. We follow a strategy where the agent first learns from the trajectories using IL and then switches to Reinforcement Learning (RL) using the identified sub-goals, to alleviate the errors in the IL step. To deal with states which are underrepresented by the trajectory set, we also learn a function to modulate the sub-goal predictions. We show that our method is able to solve complex goal-oriented tasks, which other RL, IL or their combinations in literature are not able to solve. Advances in Neural Information Processing Systems (NeurIPS) This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c © Mitsubishi Electric Research Laboratories, Inc., 2019 201 Broadway, Cambridge, Massachusetts 02139 Learning from Trajectories via Subgoal Discovery Sujoy Paul supaul@ece.ucr. edu Jeroen van Baar jeroen@merl.com Amit K. Roy-Chowdhury amitrc@ece.ucr. edu University of California-Riverside Mitsubishi Electric Research Laboratories (MERL)
Automated insights: verbal cues to deception in real-life high-stakes lies

This study differentiated between the language of deceptive and genuine pleaders who were pleading for the return of a missing loved one during a televised press conference. The Wmatrix linguistic analysis tool was used to examine the language of 78 pleaders. Approximately half (n = 35) of these individuals were deceptive and were responsible for the disappearance. Transcripts of the pleas were analyzed for various linguistic cues, and a separate analysis was conducted across gender. Results revealed that deceptive pleaders used the word ‘they,’ singular indefinite pronouns (e.g., ‘anybody,’ ‘somebody’), and exclusivizers/particularizers (e.g., ‘just’) significantly more than genuine pleaders, while genuine pleaders used more temporal words (e.g., ‘days,’ ‘weeks’), and the word ‘we’ more frequently in their pleas. Specific gender differences were also revealed across credible and deceptive pleaders. Our analysis of linguistic differences across pleader veracity provides an enhanced understanding of the verbal elements of high-stakes deception and what differentiates truths from deceptions in high-stakes cases. It also provides further validation of the use of automated linguistic tools like the Wmatrix in forensic contexts.
Detecção de Anomalias em Redes de Computadores através de Transformadas Wavelet

Attacks on computer networks compromise the security of the system and degrade the performance of the network causing problems to users and org anizations. Network-based Intru- sion Detection Systems are used to detect attacks or malicio us activity by analyzing the network traffic. The anomaly-based detection approach is used for in trusion detection. It is assumed that the presence of traffic anomalies, deviations from standardbehavior, is indicative of an attack or malfunction. A major difficulty of an anomaly-based Intrusi on Detection System is the construc- tion of the profile due to the complexity of network traffic. Methods derived from Signal Analysis, among which, the Wavelet Transform, have recently demonstr ated applicability in detecting anoma- lies in network. This paper presents the basic concepts of in trusion detection and recent concepts related to intrusion detection by wavelet transforms, a mul tiresolution analysis technique with low computational complexity.
Multi-stakeholder Analytics for Learning Design: A Case Study of Location-based Tools

The current access to mobile devices and the emphasis on situated learning are contributing to bring learning beyond the classroom. Taking this opportunity, more and more location-based tools are being used in learning scenarios outside the classroom. Multiple stakeholders could benefit from understanding the learning and teaching processes triggered by these tools. Teachers and instructional designers could not only monitor and regulate the implementation of their learning designs (LDs) but also assess the impact and effectiveness, tasks especially challenging when learning happens outside the classroom. Also, the community around specific mobile learning tools -such as researchers, managers of educational institutions, or developerscould use the feedback from the assessments to better understand how the tools are being adopted and how to improve them further. For this purpose, data analytics could help to collect, analyse and visualise the evidence gathered from learning environments. This paper presents the first steps of the development of a location-based authoring tool that incorporates multi-stakeholder analytics for LD features. We investigate how analytics can support specific LD needs of stakeholders from two existing location-based authoring tools: Avastusrada and Smartzoos. Specifically, we conducted two sets of interviews: contextual inquiry interviews with teachers, as well as semi-structured interviews with managers of educational institutions and researchers. Results emphasise the specific technical implications, as well as the potential of multi-stakeholder analytics for LD in the context of location-based learning tools. Author
ECSS position statement: Exercise and obesity

Abstract This is a review on the effects of physical exercise on weight, body composition and health in prevention and management of obesity. Individuals who maintain or increase their physical activity also show the best weight control over a period of several years. However, physical activity at baseline of prospective studies does not consistently predict weight change, perhaps because of changes in physical activity during the follow-up. Moderate-intensity (e.g. walking) physical activity during dietinduced weight reduction leads to modest improvements in weight, abdominal fat and total fat loss. Moderate exercise 250–300 min/wk is recommended for weight reducing purposes. For maintenance of reduced body weight, more than 60 min moderate exercise per day after weight reduction might be needed. Studies have found a more pronounced training-induced loss of visceral compared to subcutaneous fat in overweight and obese subjects. Physical activity will help to lower risk of insulin resistance, type 2 diabetes, dyslipidemia, hypertension and the metabolic syndrome in overweight and obese individuals and that more pronounced effects can be attained in combination with maintained weight loss. The intensity of physical activity should be at least moderate (>30–40% VO2max) to cause metabolic health benefits. A suitable frequency for moderate-intensity physical activity seems to be daily, and for vigorous activity every other day.
Heisenberg's picture and non commutative geometry of the semi classical limit in quantum mechanics

We propose a new framework based upon non commutative geometry to control the semi classical limit in phase space. It lead in particular to uniform estimates of Nekhoroshev's type, as Planck's constant tends to zero, for the perturbation expansion Nous proposons un cadre nouveau fonde sur la geometrie non commutative, pour controler la limite semi-classique dans l'espace des phases. Cette approche permet d'obtenir des estimations, uniformes par rapport a la constante de Planck, sur la serie de perturbation, semblables a celles de Nekhoroshev en mecanique classique
CERENKOV3: Clustering and molecular network-derived features improve computational prediction of functional noncoding SNPs

Identification of causal noncoding single nucleotide polymorphisms (SNPs) is important for maximizing the knowledge dividend from human genome-wide association studies (GWAS). Recently, diverse machine learning-based methods have been used for functional SNP identification; however, this task remains a fundamental challenge in computational biology. We report CERENKOV3, a machine learning pipeline that leverages clustering-derived and molecular network-derived features to improve prediction accuracy of regulatory SNPs (rSNPs) in the context of post-GWAS analysis. The clustering-derived feature, locus size (number of SNPs in the locus), derives from our locus partitioning procedure and represents the sizes of clusters based on SNP locations. We generated two molecular network-derived features from representation learning on a network representing SNP-gene and gene-gene relations. Based on empirical studies using a ground-truth SNP dataset, CERENKOV3 significantly improves rSNP recognition performance in AUPRC, AUROC, and AVGRANK (a locus-wise rank-based measure of classification accuracy we previously proposed).
Heterogeneous Social Preferences in a Model of Voting on Redistribution

We introduce heterogeneous social preferences in a standard model of voting on a redistributive parameter in a direct democracy. In particular, and in accordance with experimental evidence, we assume that selfish, rawlsian and utilitarian voters coexist with given proportions. We characterize implicitly the unique political equilibrium of this economy, and prove its existence for any positively skewed income distribution. It turns out that the level of redistribution in the heterogeneous economy may be either lower or higher than in the selfish one. Furthermore, we show that slight variations in the relative proportion of a given type may lead to very important changes in the extent of redistribution, and we illustrate the implications this may have in the context of the political economy of border formation. Finally, we investigate the theoretical implications of the model regarding the link between inequality and redistribution, and show that it yields different predictions than the standard model with self-interested voters. In particular, an increase in poverty is very likely to increase redistribution. Furthermore, this is also true for a mean-preserving spread leaving the median income unaffected, although it has no effect whatsoever on redistribution in the traditional selfish economy.