Miracle-dz/UniFAR-FTUs huggingface dataset:
Archipelagos and meta-archipelagos

Author(s): Whittaker, Robert J.; Fernandez-Palacios, Jose M. ; Matthews, Thomas J.; Rigal, Francois; Triantis, Kostas A. | Abstract: The term meta-archipelago has been in use in cultural studies for some time, to refer to certain complex island areas in which the boundaries between conventionally recognised archipelagos are indistinct, although the concept also carries additional connotations. Use of the term in biogeography appears more recent and without effort to prescribe its meaning. We outline, from a biogeographical perspective, distinctions between meta-archipelagos and archipelagos and those islands not occurring within either collective grouping, highlighting that network analysis tools provide metrics for formal analytical purposes.
Bayesian Incentive-Compatible Bandit Exploration

As self-interested individuals (”agents”) make decisions over time, they utilize information revealed by other agents in the past, and produce information that may help agents in the future. This phenomenon is common in a wide range of scenarios in the Internet economy, as well as in medical decisions. Each agent would like to exploit : select the best action given the current information, but would prefer the previous agents to explore: try out various alternatives to collect information. A social planner, by means of a carefully designed recommendation policy, can incentivize the agents to balance the exploration and exploitation so as to maximize social welfare. We model the planner’s recommendation policy as a multi-arm bandit algorithm under incentivecompatibility constraints induced by agents’ Bayesian priors. We design a bandit algorithm which is incentivecompatible and has asymptotically optimal performance, as expressed by regret. Further, we provide a black-box reduction from an arbitrary multi-arm bandit algorithm to an incentive-compatible one, with only a constant multiplicative increase in regret. This reduction works for very general bandit setting that incorporate contexts and arbitrary partial feedback.
The Threat of Multi‐Year Drought in Western Amazonia

Recent “once‐in‐a‐century” Amazonian droughts highlight the impacts of drought and climate change on this region's vegetation, carbon storage, water cycling, biodiversity, land use, and economy. The latest climate model simulations suggest this region will experience worsening future drought. However, the instrumental record is too short to quantify the range of background drought variability, or to evaluate extended drought risk in climate models. To overcome these limitations, we generated a new, highly resolved lake record of hydroclimatic variability within the western Amazon Basin. We find that Amazonia has regularly experienced multi‐year droughts over the last millennium. Our results indicate that current climate model simulations likely underestimate the background risk of multi‐year Amazonian drought. These findings illustrate that the future sustainability of the Amazonian forest and its many services may require management strategies that consider the likelihood of multi‐year droughts superimposed on a continued warming trend.
The Mass Index and Total Mass of the Geminid Meteoroid Stream as Found with Radar, Optical, and Lunar Impact Data

The Geminid meteor shower was observed in 2015 using the Western Meteor Physics Groups Canadian Meteor Orbit Radar (CMOR), Marshall Space Flight Centers (MSFC) eight wide-field optical cameras, and MSFCs lunar impact monitoring. These observations allowed Geminid fluxes to be calculated in three unique mass-ranges, from 1.8 (exponent -4) grams to 30 grams. From these fluxes, a mass index of 1.68 plus or minus 0.04 is found, which is in excellent agreement with past Geminid mass indices such as 1.69 found by Blaauw et al using only radar data and 1.7 found by Arlt and Rendtel using visual data. This mass index, however, is found over five orders of magnitude of mass, which allows a higher level of confidence that this mass index holds over a large portion of the stream. Mass indices are an important quantity to be accurately measured for a shower, indicating the distribution of mass in a well-studied stream in which we know the parent body (3200 Phaethon), improving forecasts of the shower activity, and allow fluxes to be scaled to high and low masses. The quantities derived here, along with a profile of the Geminid meteor shower activity in 2015 from CMOR, permit the total Geminid mass the Earth encountered in 2015 to be found, along with a minimum total mass of the Geminid meteoroid stream. Attempts have been made in the past to measure the mass of meteoroid streams using Zenithal Hourly Rate (ZHR) profiles, but here this new and improved treatment uses empirically derived fluxes and measured mass indices for the 2015 encounter with the meteoroid stream. This is to be compared with other meteoroid stream mass estimates including that of the Perseids, caused by comet Swift Tuttle.
Promise and Paradox : Measuring Students ’ Non-Cognitive Skills and the Impact of Schooling

Recent evidence from economics and psychology highlights the importance of traits other than general intelligence for success in school and in life (Almlund, Duckworth, Heckman, & Kautz, 2011; Borghans, Duckworth, Heckman, & ter Weel, 2008; Moffitt et al., 2011). Disparities in so-called non-cognitive skills appear to contribute to the academic achievement gap separating wealthy from disadvantaged students (Evans & Rosenbaum, 2008). Furthermore, non-cognitive skills may be more amenable to direct intervention than cognitive ability, particularly beyond infancy and early childhood (Cunha & Heckman, 2008; Dee & West, 2011; Heckman & Kautz, 2013). Understandably, popular interest in measuring and developing students’ non-cognitive skills has escalated (see, for example, Tough, 2012) 597298 EPAXXX10.3102/0162373715597298West et al.Measuring Students’ Non-Cognitive Skills research-article2015
AIS Electronic

Financial market prediction is a critically important research topic in financial data mining because of its potential commerce application and attractive profits. Previous studies in financial market prediction mainly focus on financial and economic indicators. Web information, as an information repository, has been used in customer relationship management and recommendation, but it is rarely considered to be useful in financial market prediction. In this paper, a combined web mining and sentiment analysis method is proposed to forecast financial markets using web information. In the proposed method, a spider is firstly employed to crawl tweets from Twitter. Secondly, Opinion Finder is offered to mining the online sentiments hidden in tweets. Thirdly, some new sentiment indicators are suggested and a stochastic time effective function (STEF) is introduced to integrate everyday sentiments. Fourthly, support vector regressions (SVRs) are used to model the relationship between online sentiments and financial market prices. Finally, the selective model can be serviced for financial market prediction. To validate the proposed method, Standard and Poor’s 500 Index (S&P 500) is used for evaluation. The empirical results show that our proposed forecasting method outperforms the traditional forecasting methods, and meanwhile, the proposed method can also capture individual behavior in financial market quickly and easily. These findings imply that the proposed method is a promising approach for financial market prediction.
Integration and Synchronization of Input Modes during Multimodal Human-Computer Interaction

Our ability to develop robust multimodal systems will depend on knowledge of the natural integration patterns that typify people's combined use of different input modes. To provide a foundation for theory and design, the present research analyzed multimodal interaction while people spoke and wrote to a simulated dynamic map system. Task analysis revealed that multimodal interaction occurred most frequently during spatial location commands, and with intermediate frequency during selection commands. In addition, microanalysis of input signals identified sequential, simultaneous, point-and-speak, and compound integration patterns, as well as data on the temporal precedence of modes and on inter-modal lags. In synchronizing input streams, the temporal precedence of writing over speech was a major theme, with pen input conveying location information first in a sentence. Linguistic analysis also revealed that the spoken and written modes consistently supplied complementary semantic information, rather than redundant. One long-term goal of this research is the development of predictive models of natural modality integration to guide the design of emerging multimodal architectures.
Sumformer: A Linear-Complexity Alternative to Self-Attention for Speech Recognition

Modern speech recognition systems rely on self-attention. Unfortunately, token mixing with self-attention takes quadratic time in the length of the speech utterance, slowing down inference as well as training and increasing memory consumption. Cheaper alternatives to self-attention for ASR have been developed, but fail to consistently reach the same level of accuracy. In practice, however, the self-attention weights of trained speech recognizers take the form of a global average over time. This paper, therefore, proposes a linear-time alternative to self-attention for speech recognition. It summarises a whole utterance with the mean over vectors for all time steps. This single summary is then combined with time-speciﬁc information. We call this method “Summary Mixing”. Introducing Summary Mixing in state-of-the-art ASR models makes it feasible to preserve or exceed previous speech recognition performance while lowering the training and inference times by up to 27 % and reducing the memory budget by a factor of two.
Critical differences in sediment delivery and partitioning between marine and lacustrine basins: A comparison of marine and lacustrine aggradational to progradational clinothem pairs

Chenglin Gong1,2,3,†, Orsolya Sztanó4, Ronald J. Steel3, Benzhong Xian1,2, William E. Galloway5, and Gábor Bada6 State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102249, China College of Geosciences, China University of Petroleum (Beijing), Beijing 102249, China Department of Geological Sciences, Jackson School of Geosciences, The University of Texas, Austin, Texas 78712, USA Department of Physical and Applied Geology, Eötvös Loránd University, Pázmány Péter sétány 1/c, 1117 Budapest, Hungary Institute for Geophysics, Jackson School of Geosciences, The University of Texas at Austin, 10100 Burnet Road, Austin, Texas 78758-4445, USA TXM Oil and Gas Exploration Ltd., Infopark Street 1, 1117 Budapest, Hungary
Optical Wireless Communications: System and Channel Modelling with MATLAB®

Detailing a systems approach, Optical Wireless Communications: System and Channel Modelling with MATLAB, is a self-contained volume that concisely and comprehensively covers the theory and technology of optical wireless communications systems (OWC) in a way that is suitable for undergraduate and graduate-level students, as well as researchers and professional engineers. Incorporating MATLAB throughout, the authors highlight past and current research activities to illustrate optical sources, transmitters, detectors, receivers, and other devices used in optical wireless communications. They also discuss both indoor and outdoor environments, discussing how different factorsincluding various channel modelsaffect system performance and mitigation techniques. In addition, this book broadly covers crucial aspects of OWC systems: Fundamental principles of OWC Devices and systems Modulation techniques and schemes (including polarization shift keying) Channel models and system performance analysis Emerging visible light communications Terrestrial free space optics communication Use of infrared in indoor OWC One entire chapter explores the emerging field of visible light communications, and others describe techniques for using theoretical analysis and simulation to mitigate channel impact on system performance. Additional topics include wavelet denoising, artificial neural networks, and spatial diversity. Content also covers different challenges encountered in OWC, as well as outlining possible solutions and current research trends. A major attraction of the book is the presentation of MATLAB simulations and codes, which enable readers to execute extensive simulations and better understand OWC in general.
The Role of Melting Snow in the Ocean Surface Heat Budget

We estimate the cooling flux from snow melting in the ocean through CloudSat satellite snowfall retrievals and reanalysis data. For snowfall events with less than 0.01 mm/hr, this flux is inconsequential. Melting snow begins to compete with other ocean surface heat fluxes as snowfall rates increase beyond 0.1 mm/hr, and it may often become the dominant heat flux as snowfall rates approach and exceed 1 mm/hr. The largest monthly average values of the melting snow cooling flux occur in winter months, approaching −10 W/m2 in both hemispheres. To determine the regional influence of melting snow on a seasonal basis, we calculate an impact metric that gauges the cooling flux of melting snow against the net flux in the ocean. This metric can be between 20% and 30% in the Northern Hemisphere during March, April, May; the Southern Ocean during March, April, May and September, October, November; and in high‐latitude polar oceans during sea ice freeze up seasons.
Financial Inclusion and Alternate Credit Scoring: Role of Big Data and Machine Learning in Fintech

We use unique and proprietary data from a large Fintech lender to analyze whether alternative data captured from an individual’s mobile phone (mobile/social footprint) can substitute for traditional credit bureau scores. Variables that measure a borrowers’ digital presence, such as the number and types of apps installed, crude measures of social connections, and measures of borrowers’ “deep social footprints” based on call logs, significantly improve default prediction and outperform the credit bureau score. Using machine learning-based prediction counterfactual analysis, we show that alternate credit scoring based on the mobile and social footprints can expand credit access for individuals who lack credit scores without adversely impacting the default outcomes. Our analysis suggests that the marginal benefit of using alternative data for credit decisions are likely to be higher for borrowers with low levels of income and education, as well as borrowers residing in regions with low levels of financial inclusion.
The Expected Inflation Channel of Government Spending in the Postwar U.S.

There exist sticky price models in which the output response to a government spending change can be large if the central bank is nonresponsive to inflation. According to this “expected inflation channel,” government spending drives up expected inflation, which in turn, reduces the real interest rate and leads to an increase in private consumption. This paper examines whether the channel was important in the post-WWII U.S., with particular attention to the 2009 Recovery Act period. First, we show that a model calibrated to have a large output multiplier requires a large response of expected inflation to a government spending shock. Next, we show that this large response is inconsistent with structural vector autoregression evidence from the Federal Reserve׳s passive policy period (1959–1979). Then, we study expected inflation measures during the Recovery Act period in conjunction with a panel of professional forecaster surveys, a cross-country comparison of bond yields and fiscal policy news announcements. We show that the expected inflation response was too small to engender a large output multiplier.
Dirichlet Mixtures : A Method for Improving Detection of Weakbut Signi cant Protein Sequence

This paper presents the mathematical foundations of Dirichlet mixtures, which have been used to improve database search results for homologous sequences, when a variable number of sequences from a protein family or domain are known. We present a method for condensing the information in a protein database into a mixture of Dirichlet densities. These mixtures are designed to be combined with observed amino acid frequencies, to form estimates of expected amino acid probabilities at each position in a proole, hidden Markov model, or other statistical model. These estimates give a statistical model greater generalization capacity, such that remotely related family members can be more reliably recognized by the model. Dirichlet mixtures have been shown to outperform substitution matrices and other methods for computing these expected amino acid distributions in database search, resulting in fewer false positives and false negatives for the families tested. This paper corrects a previously published formula for estimating these expected probabilities, and contains complete derivations of the Dirichlet mixture formulas, methods for optimizing the mixtures to match particular databases, and suggestions for eecient implementation.
Illuminating the Dark or how to recover what should not be seen

Functional encryption (FE) is a cryptographic primitive which allows to partially decrypt ciphertexts, e.g. evaluate a function over encrypted inputs and obtain the output in clear. The downside of employing FE schemes is that some details about input data “leak”. We call information leakage of a FE scheme the maximal information one can gain about input data from the clear-text output of FE evaluated function. FE which are usable in practice support only limited functionalities, in particular linear or quadratic polynomial evaluation. In a ﬁrst contribution of this work we describe how to combine a quadratic FE scheme with a classiﬁcation algorithm in order to perform a classiﬁcation over encrypted data use-case. Compared to direct usage of FE for a linear or a polynomial classiﬁer our method allows to increase classiﬁcation accuracy and/or decrease the number of used FE secret keys. In a second contribution we show how to estimate the information leakage of the classiﬁcation use-case and how to compare it to an ideal information leakage. The ideal information leakage is the minimal information leakage intrinsic to achieve the use-case requirement (e.g. perform a clas-siﬁcation task). We introduce a method for estimating the information leakage (real and ideal ones) based on machine learning techniques, in particular on neural networks.
Uncontrolled Randomness in Blockchains: Covert Bulletin Board for Illicit Activities

The blockchain technology represents a new paradigm to realize persistent distributed ledgers globally. While the blockchain technology is promising in a great number of ﬁelds, it can be abused to covertly store and disseminate potentially harmful digital content. Consequently, using blockchains as uncensored decentralized networks for arbitrary data distribution poses a serious regulatory issue. In this work, we show the severity of the problem by demonstrating a new technique that can be exploited to use the blockchain as a covert bulletin board to secretly store and distribute objectionable content. More speciﬁcally, all major blockchain systems use randomized cryptographic primitives, such as digital signatures and non-interactive zero-knowledge proofs, and we illustrate how the uncontrolled randomness in such primitives can be maliciously manipulated to enable covert communication and hidden persistent storage. We also demonstrate how the same technique can be extended to launch subversion attacks on the wallets of most top-ranked cryptocurrencies, such as Bitcoin, Ethereum, Monero, etc. To clarify the potential risk of uncontrolled randomness, we design, implement and evaluate our technique against the widely-used ECDSA signature scheme, the CryptoNote’s ring signature scheme, and Monero’s ring conﬁdential transactions. Note that the signiﬁcance of the demonstrated attacks stems from their undetectability, their adverse eﬀect on the future of decentralized blockchains, and their serious repercussions on users’ privacy and crypto funds. Finally, besides presenting the attacks, we provide a discussion of current countermeasures and suggest some countermeasures to mitigate the threat of such attacks.
ANALISIS PENGARUH PENGGUNAAN SISTEM INFORMASI PELAYANAN CABANG TERHADAP KINERJA OPERASIONAL KARYAWANPADA PT. TASPEN (PERSERO)CABANG PALEMBANG

Sistem informasi pelayanan cabang mempunyai peranan dan fungsi yang sangat penting bagi PT. TASPEN (persero) Cabang Palembang.Adapun rumusan masalah penelitian yang dilakukan yaitu untuk mengetahui seberapa besar pengaruh sistem informasi pelayanan cabang terhadap kinerja karyawan pada PT. TASPEN (persero) Cabang Palembang. Model Penelitian yang digunakan adalah model kesuksesan sistem informasi DeLone & McLean. Metode yang digunakan yaitu metode analisis SEM (Struqtural Equation Model) serta melakukan pengujian statistik data. Data diperoleh dengan mengirimkan kuesioner kepada responden yang berjumlah 34 orang. Dalam mengelola data primer penulis menggunakan program komputer yaitu SPSS (Statistical Product and Service Solution) dan PLS (partial least square). Hasil dari penelitian dan analisis yang dilakukan dapat disimpulkan bahwa sistem inforamasi pelayanan cabang memiliki pengaruh bersar terhadap kinerja operasional karyawan PT. Taspen (persero) cabang Palembang.
Formal Sequential Equivalence Checking of Digital Systems by Symbolic Simulation

A new approach to sequential verification of designs at different levels of abstraction by symbolic simulation is proposed. The automatic formal verification tool has been used for equivalence checking of structural descriptions at rt-level and their corresponding behavioral specifications. Gate-level results of a commercial synthesis tool have been compared to specifications at behavioral or structural rt-level. The specification need not be synthesizable nor cycle equivalent to the implementation. In addition, a future application of the method to property verification is proposed. Symbolic simulation is guided along logically consistent paths in the two descriptions to be compared. An open library of different equivalence detection techniques is used in order to find a good compromise between accuracy and speed. Decision diagram (OBDD) based techniques detect corner-cases of equivalence. Graph explosion is avoided by using the results of the other equivalence detection techniques and by representing only small parts of the verification problem by decision diagrams. The cooperation of all techniques as well as good debugging support are made feasible by notifying detected relationships at equivalence classes instead of manipulating symbolic terms.
Log Analysis of Estonian Internet Voting 2013-2015

In this report we describe our efforts in analysing log files produced by the Estonian i-voting system in the KOV2013, EP2014 and RK2015 elections in combination with other information available, so as to detect attacks against the i-voting system, detect system malfunctions and study voter behaviour.