Miracle-dz/UniFAR-FTUs huggingface dataset:
J ul 2 02 1 Exceptional zero formulas for anticyclotomic ?-adic !-functions

In this note we define anticyclotomic ? -adic measures attached to a finite set of places ( above ? , a modular elliptic curve over a general number field and a quadratic extension / . We study the exceptional zero phenomenon that arises when has multiplicative reduction at some place in (. In this direction, we obtain ? -adic Gross-Zagier formulas relating derivatives of the corresponding ? -adic L-functions to the extended Mordell-Weil group ( ). We describe first the case ( = {p} depicting the leading coefficient of the anticyclotomic ? -adic ! -function in terms of Darmon points. Our final result generalizes a recent result in [7] which uses the construction of plectic points due to Fornea and Gehrmann. We obtain a general formula for arbitrary ( that computes the A-th derivative of the ? -adic L-function, where A is the number of places in ( where has multiplicative reduction, in terms of plectic points and Tate periods of .
Does Foreign Direct Investment Benefit Domestic Companies via Increased Productivity: Horizontal, Backward, and Forward Linkages

While the literature has explored the relationship between FDI and productivity, a consensus has yet to be reached regarding FDI’s impacts on the productivity of domestic companies in host countries. This paper expands upon the findings of previous works by introducing endogenous input decision-making and vertical linkages across industries. The analysis shows that, on average, FDI improves domestic companies’ productivity through the horizontal and backward channels, but does not affect the increase in productivity of domestic companies through forward linkage. Additionally, the mechanisms of backward spillovers vary depending on industry’s structure. JEL classification: F2; O1; O3
A general model for the demographic signatures of the transition from pandemic emergence to endemicity

A realistic age-structured model predicts plausible scenarios for future Covid-19 burden. Anticipating the medium- and long-term trajectory of pathogen emergence has acquired new urgency given the ongoing COVID-19 pandemic. For many human pathogens, the burden of disease depends on age and previous exposure. Understanding the intersection between human population demography and transmission dynamics is therefore critical. Here, we develop a realistic age-structured mathematical model that integrates demography, social mixing, and immunity to establish a plausible range for future age incidence and mortality. With respect to COVID-19, we identify a plausible transition in the age structure of risks once the disease reaches seasonal endemism across a range of immunity durations and relative severity of primary versus subsequent reinfections. We train the model using diverse real-world demographies and age-structured mixing to bound expectations for changing age incidence and disease burden. The mathematical framework is flexible and can help tailor mitigation strategies in countries worldwide with varying demographies and social mixing patterns.
THE EDIFYING DISCOURSES OF ADAM SMITH: FOCALISM, COMMERCE, AND SERVING THE COMMON GOOD

Adam Smith’s discourses aim to encourage mores, practices, and public policies in service to the common good, or that which a universally benevolent spectator would approve of. The Wealth of Nations illustrates how in pursuing our own happiness within the bounds of prudence and commutative justice, we may be said, literally or metaphorically, to cooperate with God in furthering the happiness of humankind. The Theory of Moral Sentiments elaborates an ethic, here called “focalism,” that instructs us to proportion our beneficent efforts to our knowledge and ability. The relationship between political economy and focalism is bidirectionally reinforcing. In one direction, the ethic of focalism contributes to the moral authorization of self-love, thereby invigorating and dignifying honest commercial activities. In the other direction, the insights of political economy reinforce the ethic of focalism by elaborating how through prudent commerce and focal beneficence, we cooperate, even if only metaphorically, in a grand social enterprise.
INFLUENCE OF MUNICIPAL SOLID WASTE COMPOST (MSWC) ON THE GROWTH AND YIELD OF GREEN GRAM (VIGNA RADIATE (L) WILCZEK), FENUGREEK (TRIGONELLA FOENUM-GRAECUM L.) AND ON SOIL QUALITY

Pot culture experiments were carried out to study the effect of characterized municipal solid waste compost (MSWC) amendments (0, 50, 100, 250, 500, 750 and 1000 g) with garden soil (6 kg) on the growth and the yield of green gram (Vigna radiata (L) wilczek) and fenugreek (Trigonella foenum-graecum L.) as well as changes on soil quality. The growth and the yield of green gram and fenugreek were enhanced by MSWC application upto 500 g. Soil amended with > 500 g MSWC inhibited the plants biometric parameters neither with-significantly nor without-significantly suggest a possible toxic effects to both plants. No dosage effect was detected for total chlorophyll synthesized, except green gram grown in 1000 g of MSWC amendments. Compared with control, percentage of protein was increased significantly upto 250 g for green gram and 500 g for fenugreek. A significant reduction in percentage of carbohydrates was observed above 750 g of MSWC exposure for green gram and above 500 g for fenugreek. There was no specific trend and significant changes were observed in percentage of reducing sugars in both plants except green gram exposed to 50 g MSWC exposure. There was no significant difference in all plant yield parameters of both plants except fenugreek seed weight in 250 g MSWC amendment. Generally, chemical parameters of soil are improved by the addition of MSWC, after 90 d of experimental period.
Overconfidence and Coordinating Distributed Investment Decisions: When Being Systematically Wrong is Right

This paper analyzes the impact of both non-systematically inaccurate and systematically inaccurate predictions on the coordination of distributed investment decisions. The predictions of concern pertain to the expected cash outlay necessary to launch and operate an investment project, to the expected cash inflows generated by the project's operation, and to the self-assessment of whether or not the abilities expected to be needed for operation coincide with one's own. Systematic errors manifested by systematically over- or underestimating these predicted project indicators are interpreted as being produced by overconfidence. We show that within a hierarchical business organization with distributed authorities for decision-making, in some cases being overconfident can mitigate the negative effect of erroneous forecasting compared to being non-systematically wrong. Moreover, we offer policy advice on efficiently sequencing error correction in order to increase the performance of the multi-stage coordination system.
Revisiting Fiscal Responsibility Norms: A Cross Country Analysis of the Impact of COVID-19

In this paper, we have reviewed the COVID induced shock to the debt and deficit profiles of 10 of the largest economies by size of GDP in 2019 referred to in this paper as the Big-10 economies. There is a sharp upsurge in their government debt-GDP ratios because their policy responses to the COVID induced recession have been large fiscal stimuli based on borrowing. With low and often negative growth rates and high fiscal deficit, the debt-GDP ratios are projected to rise sharply in these economies. As normalcy is restored, these countries may attempt to sharply reduce their borrowing levels relative to GDP. However, we argue that before this is done, individual countries may do well to reassess their sustainability norms whether cast in terms of agreements such as the Maastricht Treaty or country level Fiscal Responsibility Legislations (FRLs) or other similar norms. This revision is called for because of the longer-term trends in these economies of rising money supply, falling nominal interest rates and nominal growth rate. The contribution of this article lies in highlighting that the existing FRL norms have become dated in the European and other similar economies because of significant changes in macro parameters such as the interest rate, the long-term growth rate and the government debt-GDP profiles of these countries as compared to the time when these norms were originally determined. There is thus a need now to re-determine these norms which may be higher than their current levels. Even though, some recent literature suggests that the sustainability benchmarks may have shifted upwards, we argue that the post COVID debt-GDP ratios have exceeded these revised benchmarks by significant margins in the case of a number of the Big-10 economies.
In-utero exposure to local COVID-19 incidence and infant birthweight in Brazil

ABSTRACT In this article, we investigate whether in-utero exposure to municipal-level COVID-19 incidence rates in Brazil was associated with lower infant birth weight. To lower endogeneity related to conception during COVID-19, we include infants who were conceived before and born after, the first official case of COVID-19 in Brazil, 26 February 2020. Using the administrative natality data of infants, in-utero exposure to local COVID-19 incidence rate did not affect infant birth weight, and the estimates are robust to heterogeneities by mother’s demographic and socioeconomic characteristics. Findings have implications for the effect of in-utero exposure to local COVID-19 incidence rates and birth weight.
A TORC 1-histone axis regulates chromatin organisation and non-canonical 1 induction of autophagy to ameliorate ageing 2 3

induction of autophagy to ameliorate ageing 2 3 Yu-Xuan Lu 1 , Jennifer C. Regan 2,4 , Jacqueline Eßer 1 , Lisa F. Drews 1 , Thomas Weinseis 1 , Julia 4 Stinn 1 , Oliver Hahn 1,5 , Richard A. Miller 3 , Sebastian Grönke 1 and Linda Partridge* 1,2 5 6 1 Max Planck Institute for Biology of Ageing, Joseph-Stelzmann-Strasse 9b, 50931, Cologne, 7 Germany. 8 2 Institute of Healthy Ageing, Department of Genetics, Evolution and Environment, University 9 College London, Gower St, London, WC1E 6BT, UK. 10 3 Department of Pathology, University of Michigan, Ann Arbor, Michigan, 48103 USA. 11 4 Institute of Immunology and Infection Research, University of Edinburgh, Charlotte Auerbach 12 Road, Edinburgh, EH9 3FL, UK (current address). 13 5 Department of Neurology and Neurological Sciences, Stanford University School of Medicine, 14 Stanford, California, USA (current address). 15 * Corresponding author: Linda.Partridge@age.mpg. de 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
Evaluating the progress of Deep Reinforcement Learning in the real world: aligning domain-agnostic and domain-specific research

Deep Reinforcement Learning (DRL) is considered a potential framework to improve many real-world autonomous systems; it has attracted the attention of multiple and diverse fields. Nevertheless, the successful deployment in the real world is a test most of DRL models still need to pass. In this work we focus on this issue by reviewing and evaluating the research efforts from both domain-agnostic and domain-specific communities. On one hand, we offer a comprehensive summary of DRL challenges and summarize the different proposals to mitigate them; this helps identifying five gaps of domain-agnostic research. On the other hand, from the domain-specific perspective, we discuss different success stories and argue why other models might fail to be deployed. Finally, we take up on ways to move forward accounting for both perspectives.
Peaked bulk crystal nucleation in charged sphere melts from salt concentration dependent crystallization experiments at very low metastability

We determined bulk crystal nucleation rates in aqueous suspensions of charged spheres at low metastability. Experiments were performed in dependence on electrolyte concen-tration and for two different particle number densities. The time-dependent nucleation rate shows a pronounced initial peak, while post-solidification crystal size distributions are skewed towards larger crystallite sizes. At each concentration, the nucleation rate density initially drops exponentially with increasing salt concentration. The complete data set, however, shows an unexpected scaling of the nucleation rate densities with met-astability times the number density of particles. Parameterization of our results in terms of Classical Nucleation Theory reveals unusually low interfacial free energies of the nu-cleus surfaces and nucleation barriers well below the thermal energy. We tentatively attribute our observations to the presence of doublets introduced by the employed con-ditioning technique and acting as nucleation seeds.
Data Lake Ingestion Management

Data Lake (DL) is a Big Data analysis solution which ingests raw data in their native format and allows users to process these data upon usage. Data ingestion is not a simple copy and paste of data, it is a complicated and important phase to ensure that ingested data are findable, accessible, interoperable and reusable at all times. Our solution is threefold. Firstly, we propose a metadata model that includes information about external data sources, data ingestion processes, ingested data, dataset veracity and dataset security. Secondly, we present the algorithms that ensure the ingestion phase (data storage and metadata instanciation). Thirdly, we introduce a developed metadata management system whereby users can easily consult different elements stored in DL.
Learning-Based Predictive Control via Real-Time Aggregate Flexibility

Aggregators have emerged as crucial tools for the coordination of distributed, controllable loads. To be used effectively, an aggregator must be able to communicate the available flexibility of the loads they control, as known as the aggregate flexibility to a system operator. However, most of existing aggregate flexibility measures often are slow-timescale estimations and much less attention has been paid to real-time coordination between an aggregator and an operator. In this paper, we consider solving an online optimization in a closed-loop system and present a design of real-time aggregate flexibility feedback, termed the maximum entropy feedback (MEF). In addition to deriving analytic properties of the MEF, combining learning and control, we show that it can be approximated using reinforcement learning and used as a penalty term in a novel control algorithm – the penalized predictive control (PPC), which modifies vanilla model predictive control (MPC). The benefits of our scheme are (1). Efficient Communication. An operator running PPC does not need to know the exact states and constraints of the loads, but only the MEF. (2). Fast Computation. The PPC often has much less number of variables than an MPC formulation. (3). Lower Costs We show that under certain regularity assumptions, the PPC is optimal. We illustrate the efficacy of the PPC using a dataset from an adaptive electric vehicle charging network and show that PPC outperforms classical MPC.
Stacked phase-space density of galaxies around massive clusters: comparison of dynamical and lensing masses

We present a measurement of average histograms of line-of-sight velocities over pairs of galaxies and galaxy clusters. Since the histogram can be measured at different galaxy-cluster separations, this observable is commonly referred to as the stacked phase-space density. We formulate the stacked phase-space density based on a halo-model approach so that the model can be applied to real samples of galaxies and clusters. We examine our model by using an actual sample of massive clusters with known weak-lensing masses and spectroscopic observations of galaxies around the clusters. A likelihood analysis with our model enables us to infer the spherical-symmetric velocity dispersion of observed galaxies in massive clusters. We find the velocity dispersion of galaxies surrounding clusters with their lensing masses of $1.1\times 10^{15}\, h^{-1}\,{\rm M}_{\odot }$ to be $1180^{+83}_{-70}\, \mathrm{km\,s^{-1}}$ at the 68 per cent confidence level. Our constraint confirms that the relation between the galaxy velocity dispersion and the host cluster mass in our sample is consistent with the prediction in dark-matter-only N-body simulations under General Relativity. Assuming that the Poisson equation in clusters can be altered by an effective gravitational constant of Geff, our measurement of the velocity dispersion can place a tight constraint of $0.88 \lt G_\mathrm{eff}/G_\mathrm{N} \lt 1.29\, (68{{\ \rm per\ cent}})$ at length-scales of a few Mpc about 2.5 Giga years ago, where GN is the Newton’s constant.
Controlled Caption Generation for Images Through Adversarial Attacks

Deep learning is found to be vulnerable to adversarial examples. However, its adversarial susceptibility in image caption generation is under-explored. We study adversarial examples for vision and language models, which typically adopt an encoder-decoder framework consisting of two major components: a Convolutional Neural Network (i.e., CNN) for image feature extraction and a Recurrent Neural Network (RNN) for caption generation. In particular, we investigate attacks on the visual encoder's hidden layer that is fed to the subsequent recurrent network. The existing methods either attack the classification layer of the visual encoder or they back-propagate the gradients from the language model. In contrast, we propose a GAN-based algorithm for crafting adversarial examples for neural image captioning that mimics the internal representation of the CNN such that the resulting deep features of the input image enable a controlled incorrect caption generation through the recurrent network. Our contribution provides new insights for understanding adversarial attacks on vision systems with language component. The proposed method employs two strategies for a comprehensive evaluation. The first examines if a neural image captioning system can be misled to output targeted image captions. The second analyzes the possibility of keywords into the predicted captions. Experiments show that our algorithm can craft effective adversarial images based on the CNN hidden layers to fool captioning framework. Moreover, we discover the proposed attack to be highly transferable. Our work leads to new robustness implications for neural image captioning.
On the Probabilistic Degree of an n-variate Boolean Function

Nisan and Szegedy (CC 1994) showed that any Boolean function f : { 0 , 1 } n → { 0 , 1 } that depends on all its input variables, when represented as a real-valued multivariate polynomial P ( x 1 ,., x n ) , has degree at least log n − O ( loglog n ) . This was improved to a tight ( log n − O ( 1 )) bound by Chiarelli, Hatami and Saks (Combinatorica 2020). Similar statements are also known for other Boolean function complexity measures such as Sensitivity (Simon (FCT 1983)), Quantum query complexity, and Approximate degree (Ambainis and de Wolf (CC 2014)). In this paper, we address this question for Probabilistic degree . The function f has probabilistic degree at most d if there is a random real-valued polynomial of degree at most d that agrees with f at each input with high probability. Our understanding of this complexity measure is signiﬁcantly weaker than those above: for instance, we do not even know the probabilistic degree of the OR function, the best-known bounds put it between ( log n ) 1 / 2 − o ( 1 ) and O ( log n ) (Beigel, Reingold, Spielman (STOC 1991); Tarui (TCS 1993); Harsha, Srinivasan (RSA 2019)). Here we can give a near-optimal understanding of the probabilistic degree of n -variate functions f , modulo our lack of understanding of the probabilistic degree of OR. We show that if the probabilistic degree of OR is ( log n ) c , then the minimum possible probabilistic degree of such an f is at least ( log n ) c / ( c + 1 ) − o ( 1 ) , and we show this is tight up to ( log n ) o ( 1 ) factors.
How a Conformally Flat (GR)4 Impacts Gauss‐Bonnet Gravity?

First and foremost, we show that a 4‐dimensional conformally flat generalized Ricci recurrent spacetime (GR)4 is an Einstein manifold. We examine such a spacetime as a solution of f(R,G) ‐gravity theory and it is shown that the additional terms from the modification of the gravitational sector can be expressed as a perfect fluid. Several energy conditions are investigated with f(R,G)=R+G and f(R,G)=R2+GlnG . For both the models, weak, null and dominant energy conditions are satisfied while strong energy condition is violated, which is a good agreement with the recent observational studies which reveals that the current universe is in accelerating phase.
Continuous-wave frequency upconversion with a molecular optomechanical nanocavity

Description Optomechanical upconversion Molecules have rich signatures in their spectra at infrared wavelengths and are typically accessed with dedicated spectroscopic instrumentation. Chen et al. and Xomalis et al. report optomechanical frequency upconversion from the mid-infrared to the visible domain using molecular vibrations coupled to a plasmonic nanocavity at ambient conditions (see the Perspective by Gordon). Using different nanoantenna designs, one with a nanoparticle-on-resonator and the other with nanoparticle-in-groove, both approaches show the ability to upconvert the mid-infrared vibrations of the molecules in the nanocavity to visible light wavelengths. The effect could be used to simplify infrared spectroscopy, possibly with single-molecule sensitivity. —ISO Mid-infrared light is upconverted to visible wavelengths using vibrating molecules inside a plasmonic nanocavity. Coherent upconversion of terahertz and mid-infrared signals into visible light opens new horizons for spectroscopy, imaging, and sensing but represents a challenge for conventional nonlinear optics. Here, we used a plasmonic nanocavity hosting a few hundred molecules to demonstrate optomechanical transduction of submicrowatt continuous-wave signals from the mid-infrared (32 terahertz) onto the visible domain at ambient conditions. The incoming field resonantly drives a collective molecular vibration, which imprints a coherent modulation on a visible pump laser and results in upconverted Raman sidebands with subnatural linewidth. Our dual-band nanocavity offers an estimated 13 orders of magnitude enhancement in upconversion efficiency per molecule. Our results demonstrate that molecular cavity optomechanics is a flexible paradigm for frequency conversion leveraging tailorable molecular and plasmonic properties.
RoboCup@Home Education 2020 Best Performance: RoboBreizh, a modular approach

Every year, the Robocup@Home competition challenges teams and robots' abilities. In 2020, the RoboCup@Home Education challenge was organized online, altering the usual competition rules. In this paper, we present the latest developments that lead the RoboBreizh team to win the contest. These developments include several modules linked to each other allowing the Pepper robot to understand, act and adapt itself to a local environment. Up-to-date available technologies have been used for navigation and dialogue. First contribution includes combining object detection and pose estimation techniques to detect user's intention. Second contribution involves using Learning by Demonstrations to easily learn new movements that improve the Pepper robot's skills. This proposal won the best performance award of the 2020 RoboCup@Home Education challenge.