Miracle-dz/UniFAR-FTUs huggingface dataset:
Multitracer extension of the halo model: probing quenching and conformity in eBOSS

We develop a new Multitracer Halo Occupation Distribution (MTHOD) framework for the galaxy distribution and apply it to the extended Baryon Oscillation Spectroscopic Survey (eBOSS) final data between z = 0.7 − 1.1. We obtain a best fitting MTHOD for each tracer and describe the host halo properties of these galaxies. The mean halo masses for LRGs, ELGs, and QSOs are found to be $1.9 \times 10^{13} \, h^{-1}M_\odot$, $1.1 \times 10^{12} \, h^{-1}M_\odot$, and $5 \times 10^{12} \, h^{-1}M_\odot$ respectively in the eBOSS data. We use the MTHOD framework to create mock galaxy catalogues and predict auto- and cross-correlation functions for all the tracers. Comparing these results with data, we investigate galactic conformity, the phenomenon whereby the properties of neighbouring galaxies are mutually correlated in a manner that is not captured by the basic halo model. We detect 1-halo conformity at more than 3σ statistical significance, while obtaining upper limits on 2-halo conformity. We also look at the environmental dependence of the galaxy quenching efficiency and find that halo mass driven quenching successfully explains the behaviour in high density regions, but it fails to describe the quenching efficiency in low density regions. In particular, we show that the quenching efficiency in low density filaments is higher in the observed data, as compared to the prediction of the MTHOD with halo mass driven quenching. The mock galaxy catalogue constructed in this paper is publicly available on this website1.
Information capacity of continuous variable measurement channel

The present paper is devoted to investigation of the classical capacity of infinite-dimensional continuous variable quantum measurement channels. A number of usable conditions are introduced that enable us to apply previously obtained general results to specific models, in particular, to the multi-mode bosonic Gaussian measurement channels. An explicit formula for the classical capacity of the Gaussian measurement channel is obtained in this paper without assuming the global gauge symmetry, solely under certain ‘threshold condition’. The result is illustrated by the capacity computation for a one-mode squeezed-noise heterodyne measurement channel.
Real-variable characterizations of new anisotropic mixed-norm Hardy spaces

Let $\vec{p}\in(0,\infty)^n$ and $A$ be a general expansive matrix on $\mathbb{R}^n$. In this article, via the non-tangential grand maximal function, the authors first introduce the anisotropic mixed-norm Hardy spaces $H_A^{\vec{p}}(\mathbb{R}^n)$ associated with $A$ and then establish their radial or non-tangential maximal function characterizations. Moreover, the authors characterize $H_A^{\vec{p}}(\mathbb{R}^n)$, respectively, by means of atoms, finite atoms, Lusin area functions, Littlewood-Paley $g$-functions or $g_{\lambda}^\ast$-functions via first establishing an anisotropic Fefferman-Stein vector-valued inequality on the mixed-norm Lebesgue space $L^{\vec{p}}(\mathbb{R}^n)$. In addition, the authors also obtain the duality between $H_A^{\vec{p}}(\mathbb{R}^n)$ and the anisotropic mixed-norm Campanato spaces. As applications, the authors establish a criterion on the boundedness of sublinear operators from $H_A^{\vec{p}}(\mathbb{R}^n)$ into a quasi-Banach space. Applying this criterion, the authors then obtain the boundedness of anisotropic convolutional $\delta$-type and non-convolutional $\beta$-order Calderon-Zygmund operators from $H_A^{\vec{p}}(\mathbb{R}^n)$ to itself [or to $L^{\vec{p}}(\mathbb{R}^n)$]. As a corollary, the boundedness of anisotropic convolutional $\delta$-type Calderon-Zygmund operators on the mixed-norm Lebesgue space $L^{\vec{p}}(\mathbb{R}^n)$ with $\vec{p}\in(1,\infty)^n$ is also presented.
Target-Firm Information Asymmetry and Acquirer Returns

We show that acquirer returns are significantly higher in stock-swap acquisitions of difficult-to-value targets, as measured by R&D intensity and idiosyncratic return volatility. This finding contributes to an explanation of the determinants of, and value gains from, using stock as a method of payment. The effects of target-valuation uncertainty on both the method of payment and the market reaction to acquisitions are more likely to be apparent in samples of private acquisitions, as these effects can be masked in samples of acquisitions of publicly held targets. Nevertheless, our results hold for publicly traded targets in multivariate analysis. Copyright 2009, Oxford University Press.
o-Minimal method and generalized sum-product phenomena

Using tools from o-minimality, we prove that for two bivariate polynomials $P(x,y)$ and $Q(x,y)$ with coefficients in $\mathbb{R}$ or $\mathbb{C}$ to simultaneously exhibit small expansion, they must exploit the underlying additive or multiplicative structure of the field in nearly identical fashion. This in particular generalizes the main result of Shen~\cite{Shen} and yields an Elekes--Ronyai type structural result for symmetric non-expanders, resolving an issue mentioned by de Zeeuw in~\cite{survey}. Our result also places sum-product phenomena into a more general picture of model-theoretic interest.
Predictions for measuring the 21-cm multifrequency angular power spectrum using SKA-Low

The light-cone (LC) effect causes the mean as well as the statistical properties of the redshifted 21-cm signal $T_{\rm b}(\hat{\bf n},\nu)$ to change with frequency $\nu$ (or cosmic time). Consequently, the statistical homogeneity (ergodicity) of the signal along the line of sight (LoS) direction is broken. This is a severe problem particularly during the Epoch of Reionization (EoR) when the mean neutral hydrogen fraction ($\bar{x}_{\rm HI}$) changes rapidly as the universe evolves. This will also pose complications for large bandwidth observations. These effects imply that the 3D power spectrum $P(k)$ fails to quantify the entire second-order statistics of the signal as it assumes the signal to be ergodic and periodic along the LoS. As a proper alternative to $P(k)$, we use the multi-frequency angular power spectrum (MAPS) ${\mathcal C}_{\ell}(\nu_1,\nu_2)$ which does not assume the signal to be ergodic and periodic along the LoS. Here, we study the prospects for measuring the EoR 21-cm MAPS using future observations with the upcoming SKA-Low. Ignoring any contribution from the foregrounds, we find that the EoR 21-cm MAPS can be measured at a confidence level $\ge 5\sigma$ at angular scales $\ell \sim 1300$ for total observation time $t_{\rm obs} \ge 128\,{\rm hrs}$ across $\sim 44\,{\rm MHz}$ observational bandwidth. We also quantitatively address the effects of foregrounds on MAPS detectability forecast by avoiding signal contained within the foreground wedge in $(k_\perp, k_\parallel)$ plane. These results are very relevant for the upcoming large bandwidth EoR experiments as previous predictions were all restricted to individually analyzing the signal over small frequency (or equivalently redshift) intervals.
Regret Analysis of Causal Bandit Problems

We study how to learn optimal interventions sequentially given causal information represented as a causal graph along with associated conditional distributions. Causal modeling is useful in real world problems like online advertisement where complex causal mechanisms underlie the relationship between interventions and outcomes. We propose two algorithms, causal upper confidence bound (C-UCB) and causal Thompson Sampling (C-TS), that enjoy improved cumulative regret bounds compared with algorithms that do not use causal information. We thus resolve an open problem posed by \cite{lattimore2016causal}. Further, we extend C-UCB and C-TS to the linear bandit setting and propose causal linear UCB (CL-UCB) and causal linear TS (CL-TS) algorithms. These algorithms enjoy a cumulative regret bound that only scales with the feature dimension. Our experiments show the benefit of using causal information. For example, we observe that even with a few hundreds of iterations, the regret of causal algorithms is less than that of standard algorithms by a factor of three. We also show that under certain causal structures, our algorithms scale better than the standard bandit algorithms as the number of interventions increases.
Improving Generalization and Robustness with Noisy Collaboration in Knowledge Distillation

Inspired by trial-to-trial variability in the brain that can result from multiple noise sources, we introduce variability through noise at different levels in a knowledge distillation framework. We introduce "Fickle Teacher" which provides variable supervision signals to the student for the same input. We observe that the response variability from the teacher results in a significant generalization improvement in the student. We further propose "Soft-Randomization" as a novel technique for improving robustness to input variability in the student. This minimizes the dissimilarity between the student's distribution on noisy data with teacher's distribution on clean data. We show that soft-randomization, even with low noise intensity, improves the robustness significantly with minimal drop in generalization. Lastly, we propose a new technique, "Messy-collaboration", which introduces target variability, whereby student and/or teacher are trained with randomly corrupted labels. We find that supervision from a corrupted teacher improves the adversarial robustness of student significantly while preserving its generalization and natural robustness. Our extensive empirical results verify the effectiveness of adding constructive noise in the knowledge distillation framework for improving the generalization and robustness of the model.
Verification of Neural Networks: Specifying Global Robustness using Generative Models

The success of neural networks across most machine learning tasks and the persistence of adversarial examples have made the verification of such models an important quest. Several techniques have been successfully developed to verify robustness, and are now able to evaluate neural networks with thousands of nodes. The main weakness of this approach is in the specification: robustness is asserted on a validation set consisting of a finite set of examples, i.e. locally. We propose a notion of global robustness based on generative models, which asserts the robustness on a very large and representative set of examples. We show how this can be used for verifying neural networks. In this paper we experimentally explore the merits of this approach, and show how it can be used to construct realistic adversarial examples.
Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation

We consider stochastic second-order methods for minimizing smooth and strongly-convex functions under an interpolation condition satisfied by over-parameterized models. Under this condition, we show that the regularized subsampled Newton method (R-SSN) achieves global linear convergence with an adaptive step-size and a constant batch-size. By growing the batch size for both the subsampled gradient and Hessian, we show that R-SSN can converge at a quadratic rate in a local neighbourhood of the solution. We also show that R-SSN attains local linear convergence for the family of self-concordant functions. Furthermore, we analyze stochastic BFGS algorithms in the interpolation setting and prove their global linear convergence. We empirically evaluate stochastic L-BFGS and a "Hessian-free" implementation of R-SSN for binary classification on synthetic, linearly-separable datasets and real datasets under a kernel mapping. Our experimental results demonstrate the fast convergence of these methods, both in terms of the number of iterations and wall-clock time.
Not All are Made Equal: Consistency of Weighted Averaging Estimators Under Active Learning

Active learning seeks to build the best possible model with a budget of labelled data by sequentially selecting the next point to label. However the training set is no longer \textit{iid}, violating the conditions required by existing consistency results. Inspired by the success of Stone's Theorem we aim to regain consistency for weighted averaging estimators under active learning. Based on ideas in \citet{dasgupta2012consistency}, our approach is to enforce a small amount of random sampling by running an augmented version of the underlying active learning algorithm. We generalize Stone's Theorem in the noise free setting, proving consistency for well known classifiers such as $k$-NN, histogram and kernel estimators under conditions which mirror classical results. However in the presence of noise we can no longer deal with these estimators in a unified manner; for some satisfying this condition also guarantees sufficiency in the noisy case, while for others we can achieve near perfect inconsistency while this condition holds. Finally we provide conditions for consistency in the presence of noise, which give insight into why these estimators can behave so differently under the combination of noise and active learning.
Emotion Recognition in Conversations with Transfer Learning from Generative Conversation Modeling

Recognizing emotions in conversations is a challenging task due to the presence of contextual dependencies governed by self- and inter-personal influences. Recent approaches have focused on modeling these dependencies primarily via supervised learning. However, purely supervised strategies demand large amounts of annotated data, which is lacking in most of the available corpora in this task. To tackle this challenge, we look at transfer learning approaches as a viable alternative. Given the large amount of available conversational data, we investigate whether generative conversational models can be leveraged to transfer affective knowledge for the target task of detecting emotions in context. We propose an approach where we first train a neural dialogue model and then perform parameter transfer to initiate our target model. Apart from the traditional pre-trained sentence encoders, we also incorporate parameter transfer from the recurrent components that model inter-sentence context across the whole conversation. Based on this idea, we perform several experiments across multiple datasets and find improvement in performance and robustness against limited training data. Our models also achieve better validation performances in significantly fewer epochs. Overall, we infer that knowledge acquired from dialogue generators can indeed help recognize emotions in conversations.
Asset Pricing with Extreme Liquidity Risk

Defining extreme liquidity as the tail of the illiquidity for all stocks, I propose a direct measure of market-wide extreme liquidity risk and find that it is priced cross-sectionally in the U.S. Between 1973 and 2014, the stocks in the highest quintile of extreme liquidity risk loadings earned value-weighted average returns 5.6% per year higher than the stocks in the lowest quintile. The extreme liquidity risk premium is robust to common risk factors related to size, value, and momentum, and is different from that on aggregate liquidity risk documented in Pastor and Stambaugh (2003) as well as that based on the tail risk of Kelly and Jiang (2014). Extreme liquidity risk can provide an advanced warning about extreme liquidity events, and it reliably outperforms aggregate liquidity measures in predicting future market returns. I incorporate extreme liquidity risk into Acharya and Pedersen’s (2005) framework and find new supporting evidence for their liquidity-adjusted capital asset pricing model. I explore potential economic mechanisms through which the rare and large fluctuations in stock-level liquidity risks are priced.
Economics and Statistics Administration

Research and development (R&D) depreciation rates are critical to calculating the rates of return to R&D investments and capital service costs, both of which are important for capitalizing R&D investments in the national income and product accounts. Although important, measuring R&D depreciation rates is extremely difficult because both the price and output of R&D capital are generally unobservable. To resolve these difficulties, economists have adopted various approaches to estimate industry-specific R&D depreciation rates, but the differences in their results cannot easily be reconciled. In addition, many of their calculations rely on unverifiable assumptions. Unlike tangible capital which depreciates due to physical decay or wear and tear, business R&D capital depreciates because its contribution to a firm’s profit declines over time. Based on this understanding, I developed a forward-looking profit model with a gestation lag to derive both constant and time-varying industry-specific R&D depreciation rates for ten R&D intensive industries that are identified in BEA’s R&D Satellite Account. I used two data sources, a Compustat SIC-based database and a BEA-NSF NAICS-based database, to perform model calculations. The data cover the period from 1989 to 2008. The results align with the major conclusions from recent studies that R&D depreciation rates are higher than the traditionally assumed 15 percent and vary across industries. Moreover, the industry-specific time-varying R&D depreciation rates provide information about the dynamics of technological evolution and competition across industries. Acknowledgments. I would like to thank Ernie Berndt, Wesley Cohen, Erwin Diewert, Bronwyn Hall, Brian Sliker, and many seminar participants in 2010 NBER Summer Institute CRIW Workshop and 2011 ASSA Conference for helpful comments. I am grateful to Brian Moyer and Carol Moylan for their support to this project, and to Jennifer Lee and Jeff Young for excellent assistance with data compilation. The views expressed herein are those of the author and do not necessarily reflect the views of Bureau of Economic Analysis.
Predictors of Students’ Desire to be an Entrepreneur: Kyrgyzstan, Georgian, and the United States

Entrepreneurship is an important element of national economic growth, and college business students represent an important feeder pool for a nation’s supply of entrepreneurs. The purpose of this study is to identify and contrast predictors of students’ desire to be entrepreneurs in Kyrgyzstan, Georgia, and the United States. Three hundred and five undergraduate business students in Kyrgyzstan, Georgia, and the United States completed the Entrepreneurial Motives Questionnaire (Aziz, Friedman & Sayfullin, 2012). While the recognition motive was important for all students, predictors of their desire to be entrepreneurs differed across the three countries. In contrast to Kyrgyzstan and the United States, students’ in Georgia overall desire to be entrepreneurs was more complex as finance, recognition, freedom, marketing opportunities and economic conditions reached significance. National initiatives that recognize entrepreneurial accomplishments may therefore encourage more individuals to start and manage businesses. A more complex strategy may be required in Georgia, as the decision to be an entrepreneur appeared to be more multidimensional.
Effective medium approach in the renormalization group theory of phase transitions

An effective medium approach similar to the coherent potential approximation (CPA) in the theory of disordered alloys and to the DMFT has been extended to the renormalization group equations in the local potential approximation (LPA). Non-universal characteristics of the second order phase transitions such as the critical temperatures and critical amplitudes have been calculated in good agreement with the best known estimates. A possibility of cluster extension of the LPA to improve its accuracy and to make the approach systematic and self-contained has been discussed. A qualitative explanation of the discrepancy between theoretical value of the critical exponent $\beta$ and recent experimental data on ordering in beta brass by the influence of non-universal contributions has been suggested.
The Emergence of Compositional Languages for Numeric Concepts Through Iterated Learning in Neural Agents

Since first introduced, computer simulation has been an increasingly important tool in evolutionary linguistics. Recently, with the development of deep learning techniques, research in grounded language learning has also started to focus on facilitating the emergence of compositional languages without pre-defined elementary linguistic knowledge. In this work, we explore the emergence of compositional languages for numeric concepts in multi-agent communication systems. We demonstrate that compositional language for encoding numeric concepts can emerge through iterated learning in populations of deep neural network agents. However, language properties greatly depend on the input representations given to agents. We found that compositional languages only emerge if they require less iterations to be fully learnt than other non-degenerate languages for agents on a given input representation.
Exploring Motives for Using Facebook: A Latent Profile Analysis

People use Facebook from a variety of motives and literature is dominated by descriptive statistics and variableoriented approaches (e.g., regression). The aim of this paper is to extend previous research by adopting a person-oriented approach (i.e., LPA latent profile analysis) and examine whether profiles of motivation can be identified. Two empirical studies were performed engaging Romanian university students (557 and 550, respectively). The LPA and mclust package version 5.3 were used to identify groups of students who share similar response patterns to multiple facets of motivation when using Facebook. The results of LPA revealed the presence of three profiles, reflecting different levels of motivations for using Facebook: low, moderate, and high. These profiles differed significantly from each other with respect to maintaining social relationships, extending social relationships, and information & collaboration. The results illustrate that maintaining social relations is the most important factor to create the profiles. Also, students in the high motivational profile reported more time spent on Facebook than those in the other two profiles.
TEMPORAL MONITORING OF GLACIER CHANGE IN DHAULIGANGA BASIN, KUMAUN HIMALAYA USING GEO-SPATIAL TECHNIQUES

Abstract. Present study is based on the change in surface area of the glaciers in downstream area of Dhauliganga basin, Pithoragarh district, Uttarakhand. For this study, Survey of India toposheets (surveyed in 1963) have been used for preparation of initial boundary of glaciers. Total 11 glaciers have been selected for the study in downstream valley. RGI (Randolph Glacier Inventory) data version 60. (15_RGI 60.SouthAsiaEast) from GLIMS, released July 28 2017, used for measuring temporal change in surface area. Landsat 8 OLI/TIRS image of 06 November 2016 has used to measure present area of glaciers. ASTER GDEM was used for automatic extraction of basin boundary by hydro-processing methods. Glacier boundaries were manually digitised from the Landsat image for better accuracy. The total glacier area was 69.35 km2 (1963), 48.10 km2 (2001) and 34.35 (2016) as determined from SOI toposheet, GLIMS and the Landsat image of 2016 respectively. Total vacated area by the retreat and melting of glaciers from 1963 to 2016 is 35 km2, which accounts to a total loss of 50 % from the total glaciated area. Google Earth imagery was also considered for marking the actual position of glacier outlines. Ice walls in topographic maps are also considered as the part of glacier but in present satellite image they are free from ice.