Miracle-dz/UniFAR-FTUs huggingface dataset:
Extreme and high synchrotron peak blazars beyond 4FGL: The 2BIGB γ-ray catalogue

This paper presents the results of a γ-ray likelihood analysis over all the extreme and high synchrotron peak blazars (EHSP and HSP) from the 3HSP catalogue. We investigate 2013 multifrequency positions under the eyes of Fermi Large Area Telescope, considering 11 yr of observations in the energy range between 500 MeV and 500 GeV, which results in 1160 γ-ray signatures detected down to the TS=9 threshold. The detections include 235 additional sources concerning the Fermi Large Area Telescope Fourth Source Catalog (4FGL), all confirmed via high-energy TS (Test Statistic) maps, and represent an improvement of ∼25 per cent for the number of EHSP and HSP currently described in γ-rays. We build the γ-ray spectral energy distribution (SED) for all the 1160 2BIGB sources, plot the corresponding γ-ray logN−logS, and measure their total contribution to the extragalactic gamma-ray background, which reaches up to ∼33 per cent at 100 GeV. Also, we show that the γ-ray detectability improves according to the synchrotron peak flux as represented by the figure of merit parameter, and note that the search for TeV peaked blazars may benefit from considering HSP and EHSP as a whole, instead of EHSPs only. The 2BIGB acronym stands for ‘Second Brazil-ICRANet Gamma-ray Blazars’ catalogue, and all the broad-band models and SED data points will be available on public data repositories (OpenUniverse, GitHub, and Brazilian Science Data Center-BSDC).
Determinant-Based Fast Greedy Sensor Selection Algorithm

In this paper, the sparse sensor placement problem for least-squares estimation is considered, and the previous novel approach of the sparse sensor selection algorithm is extended. The maximization of the determinant of the matrix which appears in pseudo-inverse matrix operations is employed as an objective function of the problem in the present extended approach. The procedure for the maximization of the determinant of the corresponding matrix is proved to be mathematically the same as that of the previously proposed QR method when the number of sensors is less than that of state variables (undersampling). On the other hand, the authors have developed a new algorithm for when the number of sensors is greater than that of state variables (oversampling). Then, a unified formulation of the two algorithms is derived, and the lower bound of the objective function given by this algorithm is shown using the monotone submodularity of the objective function. The effectiveness of the proposed algorithm on the problem using real datasets is demonstrated by comparing with the results of other algorithms. The numerical results show that the proposed algorithm improves the estimation error by approximately 10% compared with the conventional methods in the oversampling case, where the estimation error is defined as the ratio of the difference between the reconstructed data and the full observation data to the full observation. For the NOAA-SST sensor problem, which has more than ten thousand sensor candidate points, the proposed algorithm selects the sensor positions in few seconds, which required several hours with the other algorithms in the oversampling case on a 3.40 GHz computer.
Dynamic Contracts and the Sharpe Ratio: Theory and Evidence

We show theoretical and empirical asset pricing implications of long-term dynamic contracts between households and financial intermediaries. In our continuous-time joint contracting and asset pricing model, dynamic contracts induce households to have endogenous absolute- and downside-risk aversions over the intermediary’s wealth. In equilibrium, these two risk aversions are priced into the Sharpe ratio of the intermedi- ated economy. Financial sector volatilities and credit spreads, which we use as empirical proxies for the two respective risk aversions, affect the broad equity market Sharpe ratio in the time series as predicted by our model. In the cross-section of 135 equity and non-equity portfolios, short-run shocks to the two proxies is a priced factor (“intermediary insurance” factor) with a large negative risk price suggesting households’ willingness to pay a significant insurance premium to hedge away contracting break-downs with the intermediaries. Our results stress the role of intermediaries through dynamic contracts and the consequent endogenous risk aversions, even without capital constraints.
Adversarially Robust Few-Shot Learning: A Meta-Learning Approach

Previous work on adversarially robust neural networks for image classification requires large training sets and computationally expensive training procedures. On the other hand, few-shot learning methods are highly vulnerable to adversarial examples. The goal of our work is to produce networks which both perform well at few-shot classification tasks and are simultaneously robust to adversarial examples. We develop an algorithm for producing adversarially robust meta-learners, and we thoroughly investigate factors which contribute to adversarial vulnerability. Moreover, our method achieves far superior robust performance on few-shot image classification tasks, such as Mini-ImageNet and CIFAR-FS, than robust transfer learning.
On the decomposability of mod 2 cohomological invariants of Weyl groups

We compute the invariants of Weyl groups of type $A_n$, $B_n$, $D_n$, $E_6$, $E_7$, $E_8$, $F_4$ and $G_2$ in mod 2 Milnor $K$-theory and more general cycle modules, which are annihilated by 2. Over a base field of characteristic coprime to the group order and where $-1$ is a square, the invariants decompose as direct sums of the coefficient module. With the exception of invariants coming from $G_2$-components, all basis elements are induced either by Stiefel-Whitney classes or specific invariants in the Witt ring. The proof is based on Serre's splitting principle that guarantees detection of invariants on elementary abelian 2-subgroups generated by reflection.
Predicting ASI Interviewer Severity Ratings for a Computer-Administered Addiction Severity Index

The Addiction Severity Index (ASI) is a reliable and valid measure of problem severity among addicted patients. Concerns have been raised about the reliability of the Interviewer Severity Rating (ISR), a summary score for each of seven domains. As part of an effort to build a computer-administered ASI, regression equations were developed to predict the ISR. Repeated re-sampling of 1124 ASIs conducted by trained interviewers, permitted derivation of stable regression equations predicting the ISR for each ASI domain from patients’ answers to pre-selected interview items. The resulting seven Predicted Severity Ratings (PSRs) were tested on eight, standardized vignettes, with “gold standard,” expert-generated ISRs. Reliabilities compared well to those of intensively trained interviewers. PSRs could be an alternative to interviewer ratings and may make possible a computer-administered version of the ASI.
Extracting Knowledge For Adversarial Detection and Defense in Deep Learning

Deep learning models are being integrated into a wide range of high-impact, security-critical systems, from self-driving cars to bio-medical diagnosis. However, recent research has demonstrated that many of these deep learning architectures are highly vulnerable to adversarial attacks—highlighting the vital need for defensive techniques to detect and mitigate these attacks before they occur. To combat these adversarial attacks, we developed UnMask, a knowledge-based adversarial detection and defense framework. The core idea behind UnMask is to protect these models by verifying that an image’s predicted class (“bird”) contains the expected building blocks (e.g., beak, wings, eyes). For example, if an image is classified as “bird”, but the extracted building blocks are wheel , seat and frame , the model may be under attack. UnMask detects such attacks and defends the model by rectifying the misclassification, re-classifying the image based on its extracted building blocks. Our extensive evaluation shows that UnMask (1) detects up to 92.9% of attacks, with a false positive rate of 9.67% and (2) defends the model by correctly classifying up to 92.24% of adversarial images produced by the current strongest attack, Projected Gradient Descent, in the gray-box setting.
Projected Gradient Method for Decentralized Optimization over Time-Varying Networks

Decentralized distributed optimization over time-varying graphs (networks) is nowadays a very popular branch of research in optimization theory and consensus theory. One of the motivations to consider such networks is an application to drone networks. However, the first theoretical results in this branch appeared only five years ago (Nedic, 2014). The first results about the possibility of geometric rates of convergence for strongly convex smooth optimization problems on such networks were obtained only two years ago (Nedic, 2017). In this paper, we improve the rate of geometric convergence in the latter paper for the considered class of problems, using an original penalty method trick and robustness of projected gradient descent.
Master Thesis of Finance IFRS ADOPTION AND STOCK PRICE INFORMATIVENESS IN EMERGING MARKETS

This paper observes the influence of International Financial Reporting Standards (IFRS) adoption to stock price informativeness measured by stock return synchronicity specifically in emerging markets. It has been argued that IFRS are able to improve the amount of firm-specific information capitalized into stock prices by enhancing financial reporting disclosure and comparativeness. However, the effect of IFRS adoption in emerging markets is still questionable as it has also been argued that emerging markets are not good sources of data as stock prices tend to move closely together in emerging markets. Based on findings by Beuselinck et al. (2009), this paper expects stock return synchronicity to decrease during the period of mandatory IFRS adoption, for then to increase again in the period following the IFRS adoption. This is because IFRS disclose more firm-specific information on the period of its adoption which leads to higher firm-specific variance (i.e. lower synchronicity), but soon afterwards markets improve their expectation on future returns based on IFRS which leads to higher synchronicity. Using firm-level data from 8 emerging market countries, this paper finds that although IFRS adoption significantly affects stock returns synchronicity of most of the firms in observation in a negative way, there is no significant impact of IFRS adoption to returns synchronicity of overall firms in emerging markets in the sample. However, majority of the countries in the sample are significantly influenced by IFRS adoption in the way this paper expects, although the remaining does not exhibit any impact of IFRS.
Neural Approximate Dynamic Programming for On-Demand Ride-Pooling

On-demand ride-pooling (e.g., UberPool, LyftLine, GrabShare) has recently become popular because of its ability to lower costs for passengers while simultaneously increasing revenue for drivers and aggregation companies (e.g., Uber). Unlike in Taxi on Demand (ToD) services – where a vehicle is assigned one passenger at a time – in on-demand ride-pooling, each vehicle must simultaneously serve multiple passengers with heterogeneous origin and destination pairs without violating any quality constraints. To ensure near real-time response, existing solutions to the real-time ride-pooling problem are myopic in that they optimise the objective (e.g., maximise the number of passengers served) for the current time step without considering the effect such an assignment could have on assignments in future time steps. However, considering the future effects of an assignment that also has to consider what combinations of passenger requests can be assigned to vehicles adds a layer of combinatorial complexity to the already challenging problem of considering future effects in the ToD case.A popular approach that addresses the limitations of myopic assignments in ToD problems is Approximate Dynamic Programming (ADP). Existing ADP methods for ToD can only handle Linear Program (LP) based assignments, however, as the value update relies on dual values from the LP. The assignment problem in ride pooling requires an Integer Linear Program (ILP) that has bad LP relaxations. Therefore, our key technical contribution is in providing a general ADP method that can learn from the ILP based assignment found in ride-pooling. Additionally, we handle the extra combinatorial complexity from combinations of passenger requests by using a Neural Network based approximate value function and show a connection to Deep Reinforcement Learning that allows us to learn this value-function with increased stability and sample-efficiency. We show that our approach easily outperforms leading approaches for on-demand ride-pooling on a real-world dataset by up to 16%, a significant improvement in city-scale transportation problems.
Constraints on neutrinos and WDM Hints, neutrino bounds and WDM constraints from SDSS DR14 Lyman-$\alpha$ and Planck full-survey data

The Ly-$\alpha$ forest 1D flux power spectrum is a powerful probe of several cosmological parameters. Assuming a $\Lambda$CDM cosmology including massive neutrinos, we find that the latest SDSS DR14 BOSS and eBOSS Ly-$\alpha$ forest data is in very good agreement with current weak lensing constraints on $(\Omega_m, \sigma_8)$ and has the same small level of tension with Planck. We did not identify a systematic effect in the data analysis that could explain this small tension, but we show that it can be reduced in extended cosmological models where the spectral index is not the same on the very different times and scales probed by CMB and Ly-$\alpha$ data. A particular case is that of a $\Lambda$CDM model including a running of the spectral index on top of massive neutrinos. With combined Ly-$\alpha$ and Planck data, we find a slight (3$\sigma$) preference for negative running, $\alpha_s= -0.010 \pm 0.004$ (68% CL). Neutrino mass bounds are found to be robust against different assumptions. In the $\Lambda$CDM model with running, we find $\sum m_\nu 10\;\mathrm{keV}$ (95% CL) from Ly-$\alpha$ data alone.
Streaming Frequent Items with Timestamps and Detecting Large Neighborhoods in Graph Streams

Detecting frequent items is a fundamental problem in data streaming research. However, in many applications, besides the frequent items themselves, meta data such as the timestamps of when the frequent items appeared or other application-specific data that "arrives" with the frequent items needs to be reported too. To this end, we introduce the Neighborhood Detection problem in graph streams, which both accurately models situations such as those stated above, and addresses the fundamental problem of detecting large neighborhoods or stars in graph streams. In Neighborhood Detection, an algorithm receives the edges of a bipartite graph $G=(A, B, E)$ with $|A| = n$ and $|B| = \text{poly}~n$ in arbitrary order and is given a threshold parameter $d$. Provided that there is at least one $A$-node of degree at least $d$, the objective is to output a node $a \in A$ together with at least $\frac{d}{c}$ of its neighbors, where $c$ is the approximation factor. We show that in insertion-only streams, there is a one-pass $\tilde{O}(n + n^{\frac{1}{c}}d)$ space $c$-approximation streaming algorithm, for integral values of $c \ge 2$. We complement this result with a lower bound, showing that computing a $(c/1.01)$-approximation requires space $\Omega(n / c^2 + n^{\frac{1}{c-1}}d / c^2)$, for any integral $c \ge 2$, which renders our algorithm optimal for a large range of settings (up to logarithmic factors). In insertion-deletion (turnstile) streams, we give a one-pass $c$-approximation algorithm with space $\tilde{O}(\frac{dn}{c^2})$ (if $c \le \sqrt{n}$). We also prove that this is best possible up to logarithmic factors. Our lower bounds are obtained by defining new multi-party and two-party communication problems, respectively, and proving lower bounds on their communication complexities using information theoretic arguments.
CoopNet: Cooperative Convolutional Neural Network for Low-Power MCUs

Fixed-point quantization and binarization are two reduction methods adopted to deploy Convolutional Neural Networks (CNN) on end-nodes powered by low-power micro-controller units (MCUs). While most of the existing works use them as stand-alone optimizations, this work aims at demonstrating there is margin for a joint cooperation that leads to inferential engines with lower latency and higher accuracy. Called CoopNet, the proposed heterogeneous model is conceived, implemented and tested on off-the-shelf MCUs with small on-chip memory and few computational resources. Experimental results conducted on three different CNNs using as test-bench the low-power RISC core of the Cortex-M family by ARM validate the CoopNet proposal by showing substantial improvements w.r. t. designs where quantization and binarization are applied separately.
Predictive properties of forecast combination, ensemble methods, and Bayesian predictive synthesis

This paper studies the theoretical predictive properties of classes of forecast combination methods. A novel strategy based on continuous time stochastic processes is proposed and developed, where the combined predictive error processes are expressed as stochastic differential equations, evaluated using Ito's lemma. We identify a class of forecast combination methods, which we categorize as non-linear synthesis, and find that it entails an extra term in the predictive error process that "corrects" the bias from misspecification and dependence amongst forecasts, effectively improving forecasts. We show that a subclass of the recently developed framework of Bayesian predictive synthesis fits within this class. Theoretical properties are examined and we show that non-linear synthesis improves the expected squared forecast error over any and all linear combination, averaging, and ensemble of forecasts, under mild conditions that are met in most real applications. We discuss the conditions for which non-linear synthesis outperforms linear combinations, and its implications for developing further strategies. A finite sample simulation study is presented to illustrate our results.
Rotation of molecular clouds in M 51

The grand-design spiral galaxy M 51 was observed at 40 pc resolution in CO(1–0) by the PAWS project. A large number of molecular clouds were identified and we search for velocity gradients in two high signal-to-noise subsamples, containing 682 and 376 clouds. The velocity gradients are found to be systematically prograde oriented, as was previously found for the rather flocculent spiral M 33. This strongly supports the idea that the velocity gradients reflect cloud rotation, rather than more random dynamical forces, such as turbulence. Not only are the gradients prograde, but their ∂v/∂x and ∂v/∂y coefficients follow galactic shear in sign, although with a lower amplitude. No link is found between the orientation of the gradient and the orientation of the cloud. The values of the cloud angular momenta appear to be an extension of the values noted for galactic clouds despite the orders of magnitude difference in cloud mass. Roughly 30% of the clouds show retrograde velocity gradients. For a strictly rising rotation curve, as in M 51, gravitational contraction would be expected to yield strictly prograde rotators within an axisymmetric potential. In M 51, the fraction of retrograde rotators is found to be higher in the spiral arms than in the disk as a whole. Along the leading edge of the spiral arms, a majority of the clouds are retrograde rotators. While this work should be continued on other nearby galaxies, the M 33 and M 51 studies have shown that clouds rotate and that they rotate mostly prograde, although the amplitudes are not such that rotational energy is a significant support mechanism against gravitation. In this work, we show that retrograde rotation is linked to the presence of a spiral gravitational potential.
The influence of duration of long-distance road cycling on the amounts of immunoglobulin A & G in blood of professional cyclists

Scope and Purpose: Athletes are susceptible to upper respiratory tract infections during intensive trainings, as well as during the first or the second week after participating in the long-distance road cycling competitions. Therefore, the influence of duration of such cycling on amounts of immunoglobulin in blood of cyclists was studied in this research. Materials and Methods: 16 professional cyclists between the ages 17_20 and with the average height and weight of 168 cm and 59 kg, respectively, were divided into two groups of eight. The first group was riding for 1 and the second one for 3 hours. To measure the amounts of IgA and IgG, the blood samples of these two groups were gathered both before and immediately after the activity. The data were then analyzed through a correlated T-test and an independent one. Results: The findings showed that there was not a significant reduction in the secretory immunoglobulin A and G in the first group considering the samples before and after the activity (α = 0.05). But there existed a significant reduction of secretory immunoglobulin A and G for the second group after the 3-hour activity (α = 0.05). Discussion: According to these results, there is a significant fall in the levels of immunoglobulin A and G right after the long-distance cycling of higher duration and the athletes may be susceptible to upper respiratory tract infections during intensive trainings as well as the first or the second week after participating in the long-distance competitions.
TURNOVER STARS AND THE VALUE OF HUMAN CAPITAL Evidence from Broadway Shows

The theater provides a quasi-natural experiment for testing the direct impact of key personnel changes in complex organizations. Theater shows routinely turn-over actors in lead roles for a variety of reasons. However, all other elements of the show remain in place, including the director, the script, other actors and the physical theater environment. Even the lines stay the same. Our analysis focuses on transitions between top featured cast members in long-running Broadway shows. We compare sales, capacity and ticket prices just before and just after the change in cast. We also characterize the performers in various ways, and control for the attributes of the show, as well as for team characteristics. We find that only the most talented theater stars affect the financial success of theater shows, supporting the MacDonald (1988) version of the superstar hypothesis. A transition from a “non-star” to a “star” results in higher ticket prices and higher revenues. However, celebrities and well known stars from other fields of the performing arts do not seem to have a significant effect on prices or revenues. Teams are important, and a departure of a team or someone who had worked with the current cast before is detrimental to the performance of the show. Seasonal effects are important as well.
Debt Structure Dispersion and Loan Covenants

Firms often borrow simultaneously from different sources. Coordination problems among the owners of different types of debt, however, may lead to a run on the borrower in case of financial distress and inefficient liquidation in case of default. In this paper, we study the effect of dispersion between different debt types in firms’ existing debt structures on the use of covenants in new corporate loans. We find that the loans include more covenants when the borrowers’ existing debt is more dispersed, e.g., among loans, bonds, commercial paper, and capital leases. This effect is strongest for borrowers with high default risk and opaque accounting. Our findings suggest that covenants are used to address not only creditor-shareholder conflicts but also conflicts between different types of debt. Further, our results indicate a dynamic component missing from static debt structure models: Dispersion today entails constraints in the form of covenants when raising future debt. JEL classification: G32