Miracle-dz/UniFAR-FTUs huggingface dataset:
The dynamics of retirement behavior in couples: Reduced-form evidence from England and the US ⁄

In this paper we document and analyse the difierences in labour market outcomes for English and American couples using the 2002 and 2004 waves of ELSA and HRS. In particular, we look at couples’ retirement patterns, and how these are afiected by each country’s institutional settings, paying especial attention to the incentives implicit in social security systems. We use the institutional variation in age of entitlement to social security beneflts for women in the two countries -60 years in the UK and 62 in the US- to test in a difierence-in-difierences setting whether men’s transitions out of the labour force are sensitive to their wives’ incentives. We flnd that English men are more likely than their US counterparts to exit the labour force when their wives reach 60. We then regress men’s retirement indicator on their wife’s, instrumenting the latter with women’s exogenous retirement incentives. Again, we flnd signiflcant responses of men’s retirement to that of their wives. We interpret our results as evidence that men enjoy their retirement more if their wife is retired as well. Alternative explanations for the correlation in retirement outcomes may be present, but they cannot explain our results. This evidence of complementarity in spouses’ preference for leisure has important implications for policy analysis, as it implies that failure to account for cross-spouse in∞uences will yield inaccurate predictions of policy efiects.
Identification of Defensive Performance Factors in the 2010 FIFA World Cup South Africa

The aim of this study was to determine the efficacy of defensive play in elite football, to identify variables associated with the direct recovery of ball possession, and to propose a model for predicting the success of defensive transitions. We analyzed 804 transitions in the final stages of the Fedération Internationale Football Association (FIFA) World Cup 2010, and investigated the following variables using univariate, bivariate, and multivariate analyses: duration of defensive transition, possession loss zone, position of players at the start and end of the defensive transitions, defensive organization, general defensive approach, time of the match, position of defense, zone in which the offensive transition ends, match status, and outcome of the defensive transition. We found that the defensive transitions started most frequently in the middle offensive zone (48.9%), with an organized defense set-up (98.8%), and were unsuccessful on 57.2% of occasions. The bivariate analysis showed that the variable most strongly associated with direct recovery of the possession of the ball (p = 0.018) is the area in which the ball is lost, and the multivariate analysis showed that the duration of the defensive transition can be used as a performance indicator, with transitions lasting between 0 and 15 s associated with a higher likelihood of directly recovering the ball. This work has allowed us to identify a pattern of tactical-strategic behavior with major probabilities of success in the defensive transitions. These results will be able to be used by coaches to improve the performance of their teams in this type of situation in the game.
Phase separation in the vicinity of Fermi surface hot spots

Spatially inhomogeneous electronic states are expected to be key ingredients for the emergence of superconducting phases in quantum materials hosting charge-density-waves (CDWs). Prototypical materials are transition-metal dichalcogenides (TMDCs) and among them, 1$T$-TiSe$_2$ exhibiting intertwined CDW and superconducting states under Cu intercalation, pressure or electrical gating. Although it has been recently proposed that the emergence of superconductivity relates to CDW fluctuations and the development of spatial inhomogeneities in the CDW order, the fundamental mechanism underlying such a phase separation (PS) is still missing. Using angle-resolved photoemission spectroscopy and variable-temperature scanning tunneling microscopy, we report on the phase diagram of the CDW in 1$T$-TiSe$_2$ as a function of Ti self-doping, an overlooked degree of freedom inducing CDW texturing. We find an intrinsic tendency towards electronic PS in the vicinity of Fermi surface (FS) "hot spots", i.e. locations with band crossings close to, but not at the Fermi level. We therefore demonstrate an intimate relationship between the FS topology and the emergence of spatially textured electronic phases which is expected to be generalizable to many doped CDW compounds.
Universal Adversarial Perturbations for Speech Recognition Systems

In this work, we demonstrate the existence of universal adversarial audio perturbations that cause mis-transcription of audio signals by automatic speech recognition (ASR) systems. We propose an algorithm to find a single quasi-imperceptible perturbation, which when added to any arbitrary speech signal, will most likely fool the victim speech recognition model. Our experiments demonstrate the application of our proposed technique by crafting audio-agnostic universal perturbations for the state-of-the-art ASR system -- Mozilla DeepSpeech. Additionally, we show that such perturbations generalize to a significant extent across models that are not available during training, by performing a transferability test on a WaveNet based ASR system.
Technical Change Theory and Learning Curves: Patterns of Progress in Energy Technologies

This paper presents a comparative analysis of energy technology learning and progress within the framework of Schumpeter’s invention-innovation-diffusion paradigm. We estimate learning by doing and research rates for a range of energy technologies in four stages of technical progress. Emerging and mature technologies respond slowly to research and development (RD evolving technologies exhibit high learning-by-doing and research rates; reviving technologies exhibit considerable response to learning-by-research although they do not face significant market constraints. We generally find higher learning-by-doing than learning-by-research rates but do not find any development stage where learning-by-doing alone is the dominant driver of technical change. Also, high capital intensity and market constraints appear to slow down the pace of progress of emerging and evolving technologies. We find little scope for potential substitution between learning-by-doing and learning-by-research across the technologies and different stages of their development path.
Neuroscore: A Brain-inspired Evaluation Metric for Generative Adversarial Networks

Generative adversarial networks (GANs) are increasingly attracting attention in the computer vision, natural language processing, speech synthesis and similar domains. Arguably the most striking results have been in the area of image synthesis. However, evaluating the performance of GANs is still an open and challenging problem. Existing evaluation metrics primarily measure the dissimilarity between real and generated images using automated statistical methods. They often require large sample sizes for evaluation and do not directly reflect the human perception of the image quality. In this work, we introduce an evaluation metric we call Neuroscore, for evaluating the performance of GANs, that more directly reflects psychoperceptual image quality through the utilization of brain signals. Our results show that Neuroscore has superior performances to the current evaluation metrics in that: (1) It is more consistent with human judgment; (2) The evaluation process needs much smaller numbers of samples; and (3) It is able to rank the quality of images on a per GAN basis. A convolutional neural network based brain-inspired framework is also proposed to predict Neuroscore from GAN-generated images. Importantly, we show that including neural responses during the training phase of the network can significantly improve the prediction capability of the proposed model.
Dual-Task Paradigms and Time Control

Although many accounts of task switching emphasize the importance of working memory as a substantial source of the switch cost, there is a lack of evidence demonstrating that task switching actually places additional demands on working memory. The present study addressed this issue by implementing task switching in continuous complex span tasks with strictly controlled time parameters. A series of 4 experiments demonstrate that recall performance decreased as a function of the number of task switches and that the concurrent load of item maintenance had no influence on task switching. These results indicate that task switching induces a cost on working memory functioning. Implications for theories of task switching, working memory, and resource sharing are addressed.
Downregulation of HSP60 disrupts mitochondrial proteostasis to promote tumorigenesis and progression in clear cell renal cell carcinoma

In the present study, we demonstrate that HSP60 is unequivocally downregulated in clear cell renal cell carcinoma (ccRCC) tissues compared to pericarcinous tissues. Overexpression of HSP60 in ccRCC cancer cells suppresses cell growth. HSP60 knockdown increases cell growth and proliferation in both cell culture and nude mice xenografts, and drives cells to undergo epithelial to mesenchymal transition (EMT). Our results propose that HSP60 silencing disrupts the integrity of the respiratory complex I and triggers the excessive ROS production, which promotes tumor progression in the following aspects: (1) ROS activates the AMPK pathway that promotes acquisition of the Warburg phenotype in HSP60-KN cells; (2) ROS generated by HSP60 knockdown or by rotenone inhibition drives cells to undergo EMT; and (3) the high level of ROS may also fragment the Fe-S clusters that up regulates ADHFe1 expression and the 2-hydroxygluterate (2-HG) production leading to changes in DNA methylation. These results suggest that the high level of ROS is needed for tumorigenesis and progression in tumors with the low HSP60 expression and HSP60 is a potential diagnostic biomarker as well as a therapeutic target in ccRCC.
Let's Work Together! We-Intention to Use Instant Messaging for E-Collaboration

Instant messaging is one of the fastest growing internet-based collaborative technologies and is being widely used for work collaboration. In this paper, we modified the model of goal-directed behaviour by adding the social influence processes to investigate the we-intention to use instant messaging for ecollaboration. An online survey was conducted and data collected from 163 respondents were analyzed using the partial least squares. The research model explained 67.4% of the variance in weintention. Desires, group norms, past behaviour, perceived behaviour control were found statistically significant in determining the we-intention to use instant messaging, whilst positive and negative anticipated emotions and social identity had significant effects on we-intention through desire. This is one of the very first models in Information Systems that employs the concept of we-intention to explain e-collaboration. We believe that the implications of this study provide new and important insights to both researchers and practitioners.
Green's functions with oblique Neumann boundary conditions in a wedge

We study semi-martingale obliquely reflected Brownian motion (SRBM) with drift in a wedge of the plane in the transient case. Our main result determines a general explicit integral expression for the Laplace transform of Green's functions of this process. To that purpose we establish a new kernel functional equation connecting Laplace transforms of Green's functions inside the wedge and on its edges. This is reminiscent of the recurrent case where a functional equation derives from the "Basic adjoint relationship" which characterizes the stationary distribution. This equation leads us to a non-homogeneous Carleman boundary value problem. Its resolution provides a formula for the Laplace transform function in terms of contour integrals and a conformal mapping.
A Survey on Deep Learning based Brain Computer Interface: Recent Advances and New Frontiers

Brain-Computer Interface (BCI) bridges the human's neural world and the outer physical world by decoding individuals' brain signals into commands recognizable by computer devices. Deep learning has lifted the performance of brain-computer interface systems significantly in recent years. In this article, we systematically investigate brain signal types for BCI and related deep learning concepts for brain signal analysis. We then present a comprehensive survey of deep learning techniques used for BCI, by summarizing over 230 contributions most published in the past five years. Finally, we discuss the applied areas, opening challenges, and future directions for deep learning-based BCI.
Electromechanical Brillouin scattering in integrated optomechanical waveguides

In the well-known stimulated Brillouin scattering (SBS) process, spontaneous acoustic phonons in materials are stimulated by laser light and scatter the latter into a Stokes sideband. SBS becomes more pronounced in optical fibers and has been harnessed to amplify optical signals and even achieve lasing. Exploitation of SBS has recently surged on integrated photonics platforms, as simultaneous confinement of photons and phonons in waveguides leads to drastically enhanced interaction. Instead of being optically stimulated, coherent phonons can also be electromechanically excited with very high efficiency, as has been exploited in radio frequency acoustic filters. Here, we demonstrate electromechanically excited Brillouin scattering in integrated optomechanical waveguides made of piezoelectric material aluminum nitride (AlN). Acoustic phonons of 16 GHz in frequency are excited with nanofabricated electromechanical transducers to scatter counterpropagating photons in the waveguide into a single anti-Stokes sideband. We show that phase-matching conditions of Brillouin scattering can be tuned by varying both the optical wavelength and the acoustic frequency to realize tunable single-sideband modulation. Combining Brillouin scattering photonics with nanoelectromechanical systems, our approach provides an efficient interface between microwave and optical photons that will be important for microwave photonics and potentially quantum transduction.
EdgeSegNet: A Compact Network for Semantic Segmentation

In this study, we introduce EdgeSegNet, a compact deep convolutional neural network for the task of semantic segmentation. A human-machine collaborative design strategy is leveraged to create EdgeSegNet, where principled network design prototyping is coupled with machine-driven design exploration to create networks with customized module-level macroarchitecture and microarchitecture designs tailored for the task. Experimental results showed that EdgeSegNet can achieve semantic segmentation accuracy comparable with much larger and computationally complex networks (>20x} smaller model size than RefineNet) as well as achieving an inference speed of ~38.5 FPS on an NVidia Jetson AGX Xavier. As such, the proposed EdgeSegNet is well-suited for low-power edge scenarios.
Modeling the Spatial Distribution and Origin of CO Gas in Debris Disks

The detection of gas in debris disks raises the question of whether this gas is a remnant from the primordial protoplanetary phase, or released by the collision of secondary bodies. In this paper we analyze ALMA observations at 1″–1. ″5 resolution of three debris disks where the 12CO(2–1) rotational line was detected: HD 131835, HD 138813, and HD 156623. We apply the iterative Lucy–Richardson deconvolution technique to the problem of circumstellar disks to derive disk geometries and surface brightness distributions of the gas. The derived disk parameters are used as input for thermochemical models to test both primordial and cometary scenarios for the origin of the gas. We favor a secondary origin for the gas in these disks and find that the CO gas masses ( M ) require production rates (∼5 × 10−7 M⊕ yr−1) similar to those estimated for the bona fide gas-rich debris disk β Pic.
Comparison of Anti-Hepatitis B Virus Activities of Lamivudine and Clevudine by a Quantitative Assay

ABSTRACT In this study, we used a quantitative assay to measure the concentration-dependent effects of antivirals on extracellular hepatitis B virus (HBV) DNA as well as on different cytoplasmic and nuclear forms of HBV DNA that participate in HBV replication. HBV recombinant baculovirus, which efficiently delivers the HBV genome to HepG2 cells, was used for this study because (i) antivirals can be administered prior to initiation of HBV infection or after HBV infection and (ii) sufficiently high HBV replication levels are achieved that HBV covalently closed circular (CCC) DNA can be easily detected and individual HBV DNA species can be quantitatively analyzed separately from total HBV DNA. The results showed that the levels of HBV replicative intermediate and extracellular DNA decreased in a concentration-dependent fashion following antiviral treatment. The 50% effective concentration (EC50) and EC90 values and the Hill slopes differed for the different HBV DNA species analyzed. The data clearly indicated that (i) nuclear HBV DNAs are more resistant to antiviral therapy than cytoplasmic or extracellular HBV DNAs and (ii) nuclear HBV CCC DNA is more resistant than the nuclear relaxed circular form. This report presents the first in vitro comparison of the effects of two antivirals administered prior to initiation of HBV infection and the first thorough in vitro quantitative study of concentration-dependent antiviral effects on HBV CCC DNA.
Economic Growth and Internet Usage Impact on Publication Productivity among ASEAN’s and World’s Best Universities

Measuring the number of papers which are published each year, publication productivity is the factor which shows the reputation of universities and countries. However, the effect of growing economy and using internet on the publication productivity in Asian countries has not been discovered yet. The present research is going to figure out the publication productivity among the elite universities in Asian countries and also ten top universities around the world in the last twenty years (from 1993 to 2012). Furthermore, the current research is aimed to study the relationship among publication, gross domestic product (GDP) and internet usage. It is worth to mention that the publication of the top Ten Malaysian Universities was regarded for the similar period of time. To get the exact numbers of documents like papers, conference articles, review papers and letters which are published by the universities in the last twenty years, the writer of the same paper used the Science Direct database. Moreover, the data for GDP and the number of internet usage was collected through the World Bank database (World Data Bank). To compare all kinds of publications,one-way ANOVAwas used and to investigate the impact of economic growth and internet usageon publication productivity, multiple regression analysis was applied.The results showed that therate of publication growth was 1.9, 20.9, and 65.5 % in top universities in the world, ASEAN countries and Malaysia, respectively.The results also showed thatthere was a positive and significant correlationbetween GDP and the number of internet users with the number of publications in ASEAN and Malaysian universities. Internet usagehad much more influence in comparison withthe GDP in predicting the number of publicationsamong these groups except for top ten Malaysian universities from 2003 to 2012. In summary, publication trends in top ten Malaysian and ASEAN universities are promising. However, policy makers and science managersshouldspend much more percentage of their GDP on Internet facilities and research studies that their outputs lead to more rapid economic growthand internet usage.
Identifying Present Bias from the Timing of Choices

A (partially naïve) quasi-hyperbolic discounter repeatedly chooses whether to complete a task. Her net benefits of task completion are drawn independently between periods from a time-invariant distribution. We show that the probability of completing the task conditional on not having done so earlier increases towards the deadline. Conversely, we establish nonidentifiability by proving that for any time-preference parameters and any dataset with such (weakly increasing) task-completion probabilities, there exists a stationary payoff distribution that rationalizes the agent’s behavior if she is either sophisticated or fully naïve. Additionally, we provide sharp partial identification for the case of observable continuation values. (JEL C14, D11, D15, D90, D91)
Rapid cooling of a strain-coupled oscillator by an optical phase-shift measurement

We consider an optical probe that interacts with an ensemble of rare earth ions doping a materialin the shape of a cantilever. By optical spectral hole burning, the inhomogeneously broadenedtransition in the ions is prepared to transmit the probe field within a narrow window, but bendingof the cantilever causes strain in the material which shifts the ion resonances. The motion of thecantilever may thus be registered by the phase shift of the probe. By continuously measuringthe optical field we induce a rapid reduction of the position and momentum uncertainty of thecantilever. Doing so, the probing extracts entropy and thus effectively cools the thermal state ofmotion towards a known, conditional oscillatory motion with strongly reduced thermal fluctuations.Moreover, as the optical probe provides a force on the resonator proportional to its intensity, it ispossible to exploit the phase shift measurements in order to create an active feedback loop, whicheliminates the thermal fluctuations of the resonator. We describe this system theoretically, andprovide numerical simulations which demonstrate the rapid reduction in resonator position andmomentum uncertainty, as well as the implementation of the active cooling protocol.
The genetic landscape of anaplastic astrocytoma

Anaplastic astrocytoma WHO grade III (A3) is a lethal brain tumor that often occurs in middle aged patients. Clinically, it is challenging to distinguish A3 from glioblastoma multiforme (GBM) WHO grade IV. To reveal the genetic landscape of this tumor type, we sequenced the exome of a cohort of A3s (n=16). For comparison and to illuminate the genomic landscape of other glioma subtypes, we also included in our study diffuse astrocytoma WHO grade II (A2, n=7), oligoastrocytoma WHO grade II (OA2, n=2), anaplastic oligoastrocytoma WHO grade III (OA3, n=4), and GBM (n=28). Exome sequencing of A3s identified frequent mutations in IDH1 (75%, 12/16), ATRX (63%, 10/16), and TP53 (82%, 13/16). In contrast, the majority of GBMs (75%, 21/28) did not contain IDH1 or ATRX mutations, and displayed a distinct spectrum of mutations. Finally, our study also identified novel genes that were not previously linked to this tumor type. In particular, we found mutations in Notch pathway genes (NOTCH1, NOTCH2, NOTCH4, NOTCH2NL), including a recurrent NOTCH1-A465Tmutation, in 31% (5/16) of A3s. This study suggests genetic signatures will be useful for the classification of gliomas.
Data-driven reference model selection and application to L-DDC design

The choice of a reference model in data-driven control techniques is a critical step. Indeed, it should represent the desired closed-loop performances and be achievable by the plant at the same time. In this paper, we propose a method to build such a reference model, both reproducible by the system and having a desired behaviour. It is applicable to Linear Time-Invariant (LTI) monovariable systems and relies on the estimation of the plant's instabilities through a data-driven stability analysis technique. The L-DDC (Loewner Data Driven Control) algorithm is used to illustrate the impact of the choice of the reference model on the control design process. Finally, the proposed choice of specifications allows to use a controller validation technique based on the small-gain theorem.