Miracle-dz/UniFAR-FTUs huggingface dataset:
Collaborative Machine Learning Markets with Data-Replication-Robust Payments

We study the problem of collaborative machine learning markets where multiple parties can achieve improved performance on their machine learning tasks by combining their training data. We discuss desired properties for these machine learning markets in terms of fair revenue distribution and potential threats, including data replication. We then instantiate a collaborative market for cases where parties share a common machine learning task and where parties' tasks are different. Our marketplace incentivizes parties to submit high quality training and true validation data. To this end, we introduce a novel payment division function that is robust-to-replication and customized output models that perform well only on requested machine learning tasks. In experiments, we validate the assumptions underlying our theoretical analysis and show that these are approximately satisfied for commonly used machine learning models.
Challenges with Extreme Class-Imbalance and Temporal Coherence: A Study on Solar Flare Data

In analyses of rare-events, regardless of the domain of application, class-imbalance issue is intrinsic. Although the challenges are known to data experts, their explicit impact on the analytic and the decisions made based on the findings are often overlooked. This is in particular prevalent in interdisciplinary research where the theoretical aspects are sometimes overshadowed by the challenges of the application. To show-case these undesirable impacts, we conduct a series of experiments on a recently created benchmark data, named Space Weather ANalytics for Solar Flares (SWAN-SF). This is a multivariate time series dataset of magnetic parameters of active regions. As a remedy for the imbalance issue, we study the impact of data manipulation (undersampling and oversampling) and model manipulation (using class weights). Furthermore, we bring to focus the auto-correlation of time series that is inherited from the use of sliding window for monitoring flares’ history. Temporal coherence, as we call this phenomenon, invalidates the randomness assumption, thus impacting all sampling practices including different cross-validation techniques. We illustrate how failing to notice this concept could give an artificial boost in the forecast performance and result in misleading findings. Throughout this study we utilized Support Vector Machine as a classifier, and True Skill Statistics as a verification metric for comparison of experiments. We conclude our work by specifying the correct practice in each case, and we hope that this study could benefit researchers in other domains where time series of rare events are of interest.
Differences in radio emission from similar M dwarfs in the binary system Ross 867-8

Serendipitously, we rediscovered radio emission from the binary system Ross 867 (M4.5V) and Ross 868 (M3.5V) while inspecting archival Giant Metrewave Radio Telescope (GMRT) observations. The binary system consists of two M-dwarf stars that share common characteristics such as spectral type, astrometric parameters, age, and emission at infrared, optical, and X-ray frequencies. The GMRT data at 610 MHz taken on July 2011 shows that the radio emission from Ross 867 is polarized and highly variable on hour timescales with a peak flux of 10.4 ± 0.7 mJy beam−1. Additionally, after reviewing archival data from several observatories (VLA, GMRT, JVLA, and LOFAR), we confirm that although the two stars are likely coeval, only Ross 867 was detected, while Ross 868 remains undetected at radio wavelengths. As the stars have a large orbital separation, this binary stellar system provides a coeval laboratory to examine and constrain the stellar properties linked to radio activity in M dwarfs. We speculate that the observed difference in radio activity between the dwarfs could be due to vastly different magnetic field topologies or that Ross 867 has an intrinsically different dynamo.
Individual-Based Stability in Hedonic Diversity Games

In hedonic diversity games (HDGs), recently introduced by Bredereck, Elkind, and Igarashi (2019), each agent belongs to one of two classes (men and women, vegetarians and meat-eaters, junior and senior researchers), and agents' preferences over coalitions are determined by the fraction of agents from their class in each coalition. Bredereck et al. show that while an HDG may fail to have a Nash stable (NS) or a core stable (CS) outcome, every HDG in which all agents have single-peaked preferences admits an individually stable (IS) outcome, which can be computed in polynomial time. In this work, we extend and strengthen these results in several ways. First, we establish that the problem of deciding if an HDG has an NS outcome is NP-complete, but admits an XP algorithm with respect to the size of the smaller class. Second, we show that, in fact, all HDGs admit IS outcomes that can be computed in polynomial time; our algorithm for finding such outcomes is considerably simpler than that of Bredereck et al. We also consider two ways of generalizing the model of Bredereck et al. to k ≥ 2 classes. We complement our theoretical results by empirical analysis, comparing the IS outcomes found by our algorithm, the algorithm of Bredereck et al. and a natural better-response dynamics.
An adaptive surrogate modeling based on deep neural networks for large-scale Bayesian inverse problems

In Bayesian inverse problems, surrogate models are often constructed to speed up the computational procedure, as the parameter-to-data map can be very expensive to evaluate. However, due to the curse of dimensionality and the nonlinear concentration of the posterior, traditional surrogate approaches (such us the polynomial-based surrogates) are still not feasible for large scale problems. To this end, we present in this work an adaptive multi-fidelity surrogate modeling framework based on deep neural networks (DNNs), motivated by the facts that the DNNs can potentially handle functions with limited regularity and are powerful tools for high dimensional approximations. More precisely, we first construct offline a DNNs-based surrogate according to the prior distribution, and then, this prior-based DNN-surrogate will be adaptively \& locally refined online using only a few high-fidelity simulations. In particular, in the refine procedure, we construct a new shallow neural network that view the previous constructed surrogate as an input variable -- yielding a composite multi-fidelity neural network approach. This makes the online computational procedure rather efficient. Numerical examples are presented to confirm that the proposed approach can obtain accurate posterior information with a limited number of forward simulations.
Modeling the Temporal Population Distribution of $Ae.~aegypti$ Mosquito Using Big Earth Observation Data

Over 50% of the world population is at risk of mosquito-borne diseases. Female Ae. aegypti mosquito species transmit Zika, Dengue, and Chikungunya. The spread of these diseases correlate positively with the vector population, and this population depends on biotic and abiotic environmental factors including temperature, vegetation condition, humidity and precipitation. To combat virus outbreaks, information about vector population is required. To this aim, Earth observation (EO) data provide fast, efficient and economically viable means to estimate environmental features of interest. In this work, we present a temporal distribution model for adult female Ae. aegypti mosquitoes based on the joint use of the Normalized Difference Vegetation Index, the Normalized Difference Water Index, the Land Surface Temperature (both at day and night time), along with the precipitation information, extracted from EO data. The model was applied separately to data obtained during three different vector control and field data collection condition regimes, and used to explain the differences in environmental variable contributions across these regimes. To this aim, a random forest (RF) regression technique and its nonlinear features importance ranking based on mean decrease impurity (MDI) were implemented. To prove the robustness of the proposed model, other machine learning techniques, including support vector regression, decision trees and k-nearest neighbor regression, as well as artificial neural networks, and statistical models such as the linear regression model and generalized linear model were also considered. Our results show that machine learning techniques perform better than linear statistical models for the task at hand, and RF performs best. By ranking the importance of all features based on MDI in RF and selecting the subset comprising the most informative ones, a more parsimonious but equally effective and explainable model can be obtained. Moreover, the results can be empirically interpreted for use in vector control activities.
Does Stardom Affect the Informativeness of CEO Trades? Evidence from Insider Trades of Star and Non-star CEOs of S&P 1500 Firms

This study examines whether the celebrity or star status of a CEO affects the informativeness of his trades. Using a sample of CEOs of S&P 1500 firms over the period of 2004–2011 and three different measures to identify star CEOs, we find that trades of non-star CEOs are better predictors of future abnormal returns and earnings than trades of star CEOs. This superior predictive power of non-star CEO trades is mostly attributable to opportunistic trades in contrast to routine trades. These findings support the argument that trades of non-star insiders are more informative than those of star insiders. The findings remain strong after various robustness checks. JEL classification: G14; G23; G29
STANDING OUT FROM THE CROWD: THE OUTLIER’S EFFECT ON CORPORATE GOVERNANCE*

This paper examines the real effects of outliers in the context of extreme analyst optimism and corporate governance. Using earnings forecasts issued by sell-side analysts between 1991 and 2011, we construct a proxy to capture two essential traits of an outlier’s opinion—being markedly distinct from the others and coming from an individual instead of a cohort of agents. We show that when an analyst issues extremely optimistic forecasts that drastically deviate from peer consensus, there is a greater tendency on the part of firms to manage earnings. When exploring possible underlying mechanisms through which extreme analyst optimism imposes pressure on managers to meet short-term targets, we find that the arrival of an outlier forecast gives rise to the optimism of peer analysts and generates stronger reactions from investors. Further analyses reveal that private information and conflicts of interest cannot explain the effect of an outlier’s opinion on earnings management. Instead, an analyst’s self-motivated incentives are likely at play.
Using Noisy Self-Reports to Predict Twitter User Demographics

Computational social science studies often contextualize content analysis within standard demographics. Since demographics are unavailable on many social media platforms (e.g. Twitter), numerous studies have inferred demographics automatically. Despite many studies presenting proof-of-concept inference of race and ethnicity, training of practical systems remains elusive since there are few annotated datasets. Existing datasets are small, inaccurate, or fail to cover the four most common racial and ethnic groups in the United States. We present a method to identify self-reports of race and ethnicity from Twitter profile descriptions. Despite the noise of automated supervision, our self-report datasets enable improvements in classification performance on gold standard self-report survey data. The result is a reproducible method for creating large-scale training resources for race and ethnicity.
SUPPLEMENT TO “WHY DO PEOPLE KEEP THEIR PROMISES? AN EXPERIMENTAL TEST OF TWO EXPLANATIONS” (Econometrica, Vol. 76, No. 6, November 2008, 1467–1480) BY CHRISTOPH VANBERG

Thank you for participating in this experiment. The purpose of this experiment is to study how people make decisions in a particular situation. In case you should have questions at any time, please raise your hand. Please do not speak to other participants during the experiment. You will receive 2 50 EUR for arriving on time. Depending on the decisions made, you may receive an additional amount (as described below). At the end of the experiment, the entire amount will be paid to you individually and privately in cash. The experiment consists of 8 rounds. In each round, you will interact with a different, randomly chosen participant. Under no circumstances will you interact with the same participant twice. No participant will learn the identity of the persons with whom he or she has interacted during the experiment. At the end of the experiment, one of the 8 rounds will be randomly chosen for payment (every round is equally likely). The amount that you will receive at the end of the experiment will depend on the decisions made in that round. Each round consists of four steps, which are described below.
RussNet: Building a Lexical Database for the Russian Language

The paper describes the on-going work on creating the WordNet-type lexicon for Russian, so called RussNet. The project started 3 years ago; preliminary results will be available at www.phil.pu.ru. The existing database contains verbs, nouns, and adjectives, the number of senses amounting to 2500. The Top Ontology of RussNet is under construction, it will be co-ordinated with that of EuroWN. RussNet has inherited EuroWN language-internal relations. Several types of derivational links are added to describe Cross-Part-Of-Speech relations as well as Inner-PartOf-Speech ones. Adjective-to-noun and verb-to-noun relations of words in collocations are described in details. An overview of methods used for construction of the Russian WordNet is presented; the procedure of sense definition generation is also discussed.
Solving Online Threat Screening Games using Constrained Action Space Reinforcement Learning

Large-scale screening for potential threats with limited resources and capacity for screening is a problem of interest at airports, seaports, and other ports of entry. Adversaries can observe screening procedures and arrive at a time when there will be gaps in screening due to limited resource capacities. To capture this game between ports and adversaries, this problem has been previously represented as a Stackelberg game, referred to as a Threat Screening Game (TSG). Given the significant complexity associated with solving TSGs and uncertainty in arrivals of customers, existing work has assumed that screenees arrive and are allocated security resources at the beginning of the time-window. In practice, screenees such as airport passengers arrive in bursts correlated with flight time and are not bound by fixed time-windows. To address this, we propose an online threat screening model in which the screening strategy is determined adaptively as a passenger arrives while satisfying a hard bound on acceptable risk of not screening a threat. To solve the online problem, we first reformulate it as a Markov Decision Process (MDP) in which the hard bound on risk translates to a constraint on the action space and then solve the resultant MDP using Deep Reinforcement Learning (DRL). To this end, we provide a novel way to efficiently enforce linear inequality constraints on the action output in DRL. We show that our solution allows us to significantly reduce screenee wait time without compromising on the risk.
Online Power Allocation at Energy Harvesting Transmitter for Multiple Receivers With and Without Individual Rate Constraints for OMA and NOMA Transmissions

In this paper, we propose an online power allocation scheme to maximize the time average of the expected value of sum rate for multiple downlink receivers with energy harvesting transmitter. The transmitter employs non-orthogonal multiple access (NOMA) and/or orthogonal multiple access (OMA) to transmit data to multiple users. Additionally, we consider the scenario where each individual user has a quality of service constraint on its required instantaneous rate. The decisions of total transmit power and power allocation for different users in a given time slot are obtained with the help of Lyapunov optimization technique. The proposed schemes do not require any statistical information of the channel states and the harvested energy. The proposed power allocation schemes entail in small complexity based power allocation decisions. Therefore, the proposed schemes can provide solutions in real time and are more suited for online power allocation problems where the system state parameters (e.g., channel state, harvested energy etc) change quickly. The performances of the proposed schemes are demonstrated with the help of simulation results.
Instance-Invariant Adaptive Object Detection via Progressive Disentanglement

Most state-of-the-art methods of object detection suffer from poor generalization ability when the training and test data are from different domains, e.g., with different styles. To address this problem, previous methods mainly use holistic representations to align feature-level and pixel-level distributions of different domains, which may neglect the instance-level characteristics of objects in images. Besides, when transferring detection ability across different domains, it is important to obtain the instance-level features that are domain-invariant, instead of the styles that are domain-specific. Therefore, in order to extract instance-invariant features, we should disentangle the domain-invariant features from the domain-specific features. To this end, a progressive disentangled framework is first proposed to solve domain adaptive object detection. Particularly, base on disentangled learning used for feature decomposition, we devise two disentangled layers to decompose domain-invariant and domain-specific features. And the instance-invariant features are extracted based on the domain-invariant features. Finally, to enhance the disentanglement, a three-stage training mechanism including multiple loss functions is devised to optimize our model. In the experiment, we verify the effectiveness of our method on three domain-shift scenes. Our method is separately 2.3\%, 3.6\%, and 4.0\% higher than the baseline method \cite{saito2019strong}.
Color-wise Attention Network for Low-light Image Enhancement

Absence of nearby light sources while capturing an image will degrade the visibility and quality of the captured image, making computer vision tasks difficult. In this paper, a color-wise attention network (CWAN) is proposed for low-light image enhancement based on convolutional neural networks. Motivated by the human visual system when looking at dark images, CWAN learns an end-to-end mapping between low-light and enhanced images while searching for any useful color cues in the low-light image to aid in the color enhancement process. Once these regions are identified, CWAN attention will be mainly focused to synthesize these local regions, as well as the global image. Both quantitative and qualitative experiments on challenging datasets demonstrate the advantages of our method in comparison with state-of-the-art methods.
The Effects of Mindfulness Practice with Music Listening on Working Memory

by Emily I. Messick University of the Pacific 2019 The purpose of this experimental study was to investigate mindfulness strategies and their influence on working memory. The potential role of music in facilitating mindfulness practice is explored. Various listening exercises were investigated along with their influence on working memory (i.e. attention control). Thirty-four individuals were randomly assigned to participate in one of four listening groups: 1) mindfulness with music, 2) mindfulness without music, 3) music only, and 4) silence. Thirty-four participants engaged in a computerized digit-span task before and after the listening exercise to assess preand post-test working memory performance. Thirty participants were included in data-analysis due to technical errors in data collection. Differences between listening exercises were explored and comparisons were made between mindfulness, non-mindfulness, music, and non-music based exercises. Two-tailed independent samples t-tests found no significant differences in working memory when comparing mindfulness versus non-mindfulness and music versus non-music based exercises. An Analysis of Variance (ANOVA) indicated no significant differences in working memory for any of the listening conditions. Results call for further examination of control variables and methodology to explore the role of music listening in mindfulness practice. Implications for further research and contributions to music therapy and music education are considered.
Giant increase of temporal coherence in optically trapped polariton condensate

Coherent bosonic ensembles offer the promise of harnessing quantum effects in photonic and quantum circuits. In the dynamic equilibrium regime, the application of polariton condensates is hindered by exciton-polariton scattering induced de-coherence in the presence of a dark exciton reservoir. By spatially separating the condensate from the reservoir, we drive the system into the weak interaction regime, where the ensemble coherence time exceeds the individual particle lifetime by nearly three orders of magnitude. The observed nanosecond coherence provides an upper limit for polariton self-interactions. In contrast to conventional photon lasers, we observe an increased contribution from the super-Poissonian component of the condensate to the overall particle number fluctuations. Coupled with the recent emergence of a quantum regime in polaritonics, coherence times extended to several nanoseconds favour the realization of quantum information protocols.
Tunable large spin Hall and spin Nernst effects in the Dirac semimetals 
ZrXY (X=Si,Ge;Y=S,Se,Te)

The ZrSiS-type compounds are Dirac semimetals and have been attracting considerable interest in recent years due to their topological electronic properties and possible applications. In particular, gapped Dirac nodes can possess large spin Berry curvatures and thus give rise to large spin Hall effect (SHE) and spin Nernst effect (SNE), which may be used to generate pure spin current for spintronics and spin caloritronics without applied magnetic field or magnetic material. In this paper we study both SHE and SNE in ZrXY (X = Si, Ge; Y = S, Se, Te) based on \textit{ab initio} relativistic band structure calculations. Our theoretical calculations reveal that some of these compounds exhibit large intrinsic spin Hall conductivity (SHC) and spin Nernst conductivity (SNC). The calculated SHC of ZrSiTe is as large as -755 ($\hbar$/e)(S/cm). Since the electric conductivity of these Dirac semimetals are much smaller than that of platinum which has the largest intrinsic SHC of $\sim$2200 ($\hbar$/e)(S/cm), this indicates that they will have a larger spin Hall angle than that of platinum. Remarkably, we find that both the magnitude and sign of the SHE and SNE in these compounds can be significantly tuned by changing either the electric field direction or spin current direction and may also be optimized by slightly varying the Fermi level via chemical doping. Analysis of the calculated band- and $k$-resolved spin Berry curvatures show that the large SHE and SNE as well as their remarkable tunabilities originate from the presence of many slightly spin-orbit coupling-gapped Dirac nodal lines near the Fermi level in these Dirac semimetals. Our findings suggest that the ZrSiS-type compounds are promising candidates for spintronic and spin caloritronic devices, and will certainly stimulate further experiments on these Dirac semimetals.