Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of galaxies and AGNs in reionizing the IGM – II. Metal-tracing the faint sources of reionization at 5 ≲z≲ 6

We present a new method to study the contribution of faint sources to the UV background using the 1D correlation of metal absorbers with the intergalactic medium (IGM) transmission in a Quasi Stellar Object (QSO) sightline. We take advantage of a sample of $25$ high signal-to-noise ratio QSO spectra to retrieve $150$ triply-ionised carbon (\cfour) absorbers at $4.5\lesssim z\lesssim 6.2$, of which $37$ systems whose expected H{~\small I} absorption lie in the Lyman-$\alpha$ forest. We derive improved constraints on the cosmic density of \cfour \,at $4.3< z < 6.2$ and infer from abundance-matching that \cfour \,absorbers trace $M_{\text{UV}}\lesssim -16$ galaxies. Correlation with the Lyman-$\alpha$ forest of the QSOs indicates that these objects are surrounded by a highly opaque region at $r\lesssim 5 $ cMpc/h followed by an excess of transmission at $r\gtrsim 10$ cMpc/h detected at $2.7\sigma$. This is in contrast to equivalent measurements at lower redshifts where only the opaque trough is detected. We interpret this excess as a statistical enhancement of the local photoionisation rate due to clustered faint galaxies around the \cfour \,absorbers. Using the analytical framework described in Paper I of this series, we derive a constraint on the average product of the escape fraction and the Lyman continuum photon production efficiency of the galaxy population clustered around the \cfour \,absorbers, $\log \langle f_{\text{esc}}\xi_{\text{ion}}\rangle /[{\rm erg^{-1}~Hz}] = 25.01^{+0.30}_{-0.19}$. This implies that faint galaxies beyond the reach of current facilities may have harder radiation fields and/or larger escape fractions than currently detected objects at the end of the reionisation epoch.
Expectations and Stock Market Participation: Theory and Evidence

The stock market participation puzzle is the robust empirical finding that substantially fewer people participate in the stock market than predicted by standard models. Recent evidence suggests subjective returns play a key role in stock market participation. Furthermore, there is strong evidence that stock market experiences, i.e. realized returns, impact subjective returns. I bring a model into the laboratory and find that learning-driven subjective returns can explain limited participation. Stock market participation is increasing in both subjective returns and past realized returns. I find direct evidence that “learning from experience” generates heterogeneity in subjective returns, where subjects who experience low returns have lower subjective returns than subjects who experience high returns. In particular, subjects over-weigh price trends when they experience high returns and under-weigh it when they experience low returns. ∗Department of Economics, University of California, Irvine. I am deeply indebted to Bill Branch and John Duffy for their extensive input on this paper. I would also like to thank UC Irvine for funding this project and Philipp Chapkovski for experimental development assistance. I thank Gabriele Camera, Frank Heinemann, Cars Hommes, Ed Hopkins, Tatiana Kornienko, Mike McBride, Cody Nehiba, Guillaume Rocheteau, Joep Sonnemans, Eric Swanson, Pat Testa, Jan Tuinstra and members of the UCI Experimental workshop and UvA CeNDEF seminar for their valuable feedback. All errors are my own. Email: mikeshin@uci.edu.
Dark Energy Survey Year 1 results: weak lensing mass calibration of redMaPPer galaxy clusters

We constrain the mass--richness scaling relation of redMaPPer galaxy clusters identified in the Dark Energy Survey Year 1 data using weak gravitational lensing. We split clusters into 4×3 bins of richness λ and redshift z for λ≥20 and 0.2≤z≤0.65 and measure the mean masses of these bins using their stacked weak lensing signal. By modeling the scaling relation as ⟨M 200m |λ,z⟩=M 0 (λ/40) F ((1+z)/1.35) G , we constrain the normalization of the scaling relation at the 5.0 per cent level as M 0 =[3.081±0.075(stat)±0.133(sys)]⋅10 14 M ⊙ at λ=40 and z=0.35 . The richness scaling index is constrained to be F=1.356±0.051 (stat)±0.008 (sys) and the redshift scaling index G=−0.30±0.30 (stat)±0.06 (sys) . These are the tightest measurements of the normalization and richness scaling index made to date. We use a semi-analytic covariance matrix to characterize the statistical errors in the recovered weak lensing profiles. Our analysis accounts for the following sources of systematic error: shear and photometric redshift errors, cluster miscentering, cluster member dilution of the source sample, systematic uncertainties in the modeling of the halo--mass correlation function, halo triaxiality, and projection effects. We discuss prospects for reducing this systematic error budget, which dominates the uncertainty on M 0. Our result is in excellent agreement with, but has significantly smaller uncertainties than, previous measurements in the literature, and augurs well for the power of the DES cluster survey as a tool for precision cosmology and upcoming galaxy surveys such as LSST, Euclid and WFIRST.
Calibration and Laboratory Testing of Computer Measuring System 8AE-PD Dedicated for Analysis of Acoustic Emission Signals Generated by Partial Discharges Within Oil Power Transformers

In the paper, there are presented a general description of the constructed measuring system 8AE-PD, the results of calibration of this system by the Hsu-Nielsen method as well as the testing of the measuring system during recording signals generated by Hsu-Nielsen sources in a steel plate and a modeled partial discharge (PD) source. There is also presented the methodology of investigations by the calibrated acoustic emission method. The results of analyses of PD signals coming from the modeled sources are given. In particular, there are described the properties of acoustic emission (AE) signals generated by the PDs as well as the dependencies of the peak-to-peak voltage $U_{mm}$, the RMS voltage $U_{rms}$ and the descriptors of acronyms ADP and ADC on the apparent charge Q introduced by the modeled PD source. There are determined the limit values of the apparent charge Q introduced by the modeled PD source for which the recorded signal is identified as originating from the PDS.
DJ-1 / park 7 modulates vasorelaxation and blood pressure via epigenetic modification of endothelial nitric oxide synthase

DJ-1/park7 modulates vasorelaxation and blood pressure via epigenetic modification of endothelial nitric oxide synthase
Spatial and temporal aspects of the decline in cod (Gadus morhua L.) abundance in the Kattegat and eastern Skagerrak

Svedang, H. , and Bardon, G. 2003. Spatial and temporal aspects of the decline in cod (Gadus morhua L.) abundance in the Kattegat and eastern Skagerrak. - ICES Journal of Marine Science, 60: 32-37. We explored the temporal and spatial evolution of cod abundance along the Swedish west coast during the past 20-40 years. Analyses of cpue data from the Swedish Nephrops fishery revealed a persistent decline in offshore abundance since the beginning of the 1980s in both the Skagerrak and Kattegat, culminating in extremely low densities in the late 1990s. Owing to a lack of reliable logbook and survey data for the area inside the trawling limit, informal statistics such as records of sport fishing clubs and notes on catches made by a coastal fishermen were used to sketch the development of cod abundance in the coastal zone. These data indicate that the decline in catches of inshore cod preceded the one in offshore areas at the end of the 1970s. The information presented indicates that the depletion of the cod stock in these areas is much worse than suggested by routine annual assessments. 2003 International Council for the Exploration of the Sea. Published by Elsevier Science Ltd.
A Conjoint Analysis of Public Preferences for Agricultural Land Preservation

Public preferences for the nonmarket services of permanently preserved agricultural land are measured and compared using conjoint analysis. The results from a survey of 199 Delawareans suggest environmental and nonmarket-agricultural services are the most important preserved-land attributes. Results also suggest that open space associated with wetlands on farms is neither an amenity nor a disamenity. On the margin, preserved parcels with agricultural and environmental attributes provide net benefits, which may exceed $1,000,000 for a 1,000-acre parcel. Preserved forestland provides benefits per acre that are statistically equivalent to cropland, though forestland may be less expensive to preserve.
Estimation of Phenolic and Flavonoid Compounds and Antioxidant Activity of Spent Coffee and Black Tea (Processing) Waste for Potential Recovery and Reuse in Sudan

This study aimed to evaluate the antioxidant power associated with spent coffee and black tea processing waste. Ethanolic extracts from the samples were prepared in order to determine the quantities/concentrations of the phenolic and flavonoid compounds, polyphenols, and associated levels of antioxidant activity. The results showed that both the spent coffee and black tea waste had high amounts of phenolic compounds and high antioxidant activity rates. The total phenolic and flavonoids content was found to be significantly higher (p < 0.05) in the spent black tea than in the spent coffee. The total phenolic content was found to be 152.8 and 97.87 mg of gallic acid equivalent/g, while the total amount of flavonoids was found to be 47.40 and 34.32 mg catechin/g in spent black tea and coffee, respectively. However, the spent coffee had a significantly higher (p < 0.05) antioxidant activity than that detected in the spent black tea (57.83%). Consequently, the results revealed that the waste residue of spent coffee and black tea may be considered as natural sources of bioactive compounds and that there may be potential for recycling these waste products, which could be applied in different industries to further develop functional foods.
What is the Effect of Importance Weighting in Deep Learning?

Importance-weighted risk minimization is a key ingredient in many machine learning algorithms for causal inference, domain adaptation, class imbalance, and off-policy reinforcement learning. While the effect of importance weighting is well-characterized for low-capacity misspecified models, little is known about how it impacts over-parameterized, deep neural networks. This work is inspired by recent theoretical results showing that on (linearly) separable data, deep linear networks optimized by SGD learn weight-agnostic solutions, prompting us to ask, for realistic deep networks, for which many practical datasets are separable, what is the effect of importance weighting? We present the surprising finding that while importance weighting impacts models early in training, its effect diminishes over successive epochs. Moreover, while L2 regularization and batch normalization (but not dropout), restore some of the impact of importance weighting, they express the effect via (seemingly) the wrong abstraction: why should practitioners tweak the L2 regularization, and by how much, to produce the correct weighting effect? Our experiments confirm these findings across a range of architectures and datasets.
A Truthful Cardinal Mechanism for One-Sided Matching

We revisit the well-studied problem of designing mechanisms for one-sided matching markets, where a set of $n$ agents needs to be matched to a set of $n$ heterogeneous items. Each agent $i$ has a value $v_{i,j}$ for each item $j$, and these values are private information that the agents may misreport if doing so leads to a preferred outcome. Ensuring that the agents have no incentive to misreport requires a careful design of the matching mechanism, and mechanisms proposed in the literature mitigate this issue by eliciting only the \emph{ordinal} preferences of the agents, i.e., their ranking of the items from most to least preferred. However, the efficiency guarantees of these mechanisms are based only on weak measures that are oblivious to the underlying values. In this paper we achieve stronger performance guarantees by introducing a mechanism that truthfully elicits the full \emph{cardinal} preferences of the agents, i.e., all of the $v_{i,j}$ values. We evaluate the performance of this mechanism using the much more demanding Nash bargaining solution as a benchmark, and we prove that our mechanism significantly outperforms all ordinal mechanisms (even non-truthful ones). To prove our approximation bounds, we also study the population monotonicity of the Nash bargaining solution in the context of matching markets, providing both upper and lower bounds which are of independent interest.
A constructive proof of dependent choice in classical arithmetic via memoization

In a recent paper, Herbelin developed dPA${^\omega}$, a calculus in which constructive proofs for the axioms of countable and dependent choices could be derived via the memoization of choice functions. However, the property of normalization (and therefore the one of soundness) was only conjectured. The difficulty for the proof of normalization is due to the simultaneous presence of dependent types (for the constructive part of the choice), of control operators (for classical logic), of coinductive objects (to encode functions of type ${\mathbb{N} \to A}$ into streams (${a_0},{a_1},.$)) and of lazy evaluation with sharing (for memoizing these coinductive objects). Elaborating on previous works, we introduce in this paper a variant of dPA${^\omega}$ presented as a sequent calculus. On the one hand, we take advantage of a variant of Krivine classical realizability that we developed to prove the normalization of classical call-by-need. On the other hand, we benefit from dL${_{\hat{tp}}}$, a classical sequent calculus with dependent types in which type safety is ensured by using delimited continuations together with a syntactic restriction. By combining the techniques developed in these papers, we manage to define a realizability interpretation a la Krivine of our calculus that allows us to prove normalization and soundness. This paper goes over the whole process, starting from Herbelin's calculus dPA${^\omega}$ until our introduction of its sequent calculus counterpart dLPA${^\omega}$.
Beyond the Bare Stage: Exploring Props as Potential Improviser-Controlled Technology

While improvised theatre (improv) is often performed on a bare stage, improvisers sometimes incorporate physical props to inspire new directions for a scene and to enrich their performance. A tech booth can improvise light and sound technical elements, but coordinating with improvisers' actions on-stage is challenging. Our goal is to inform the design of an augmented prop that lets improvisers tangibly control light and sound technical elements while performing. We interviewed five professional improvisers about their use of physical props in improv, and their expectations of a possible augmented prop that controls technical theatre elements. We propose a set of guidelines for the design of an augmented prop that fits with the existing world of unpredictable improvised performance.
inFORCE: Bi-directional `Force' Shape Display for Haptic Interaction

While previously proposed hardware on pin-based shape display has improved various technical aspects, there has been a clear limitation on the haptic quality of variable 'force' feedback. In this paper, we explore a novel haptic interaction design space with 'force' controlled shape display. Utilizing high performance linear actuators with current reading functionality, we built a 10 x 5 'force' shape display, named inFORCE, that can both detect and exert variable force on individual pins. By integrating closed-loop force control, our system can provide real-time variable haptic feedback in response to the way users press the pins. Our haptic interaction design space includes volumetric haptic feedback, material emulation, layer snapping, and friction. Our proposed interaction methods, for example, enables people to "press through'' computationally rendered dynamic shapes to understand the internal structure of 3D volumetric information. We also demonstrate a material property capturing functionality. Our technical evaluation and user study assesses the hardware capability and haptic perception through interaction with inFORCE. We also discuss application spaces that 'force' shape display can be used for.
Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks

Being able to predict the crowd flows in each and every part of a city, especially in irregular regions, is strategically important for traffic control, risk assessment, and public safety. However, it is very challenging because of interactions and spatial correlations between different regions. In addition, it is affected by many factors: i) multiple temporal correlations among different time intervals: closeness, period, trend; ii) complex external influential factors: weather, events; iii) meta features: time of the day, day of the week, and so on. In this paper, we formulate crowd flow forecasting in irregular regions as a spatio-temporal graph (STG) prediction problem in which each node represents a region with time-varying flows. By extending graph convolution to handle the spatial information, we propose using spatial graph convolution to build a multi-view graph convolutional network (MVGCN) for the crowd flow forecasting problem, where different views can capture different factors as mentioned above. We evaluate MVGCN using four real-world datasets (taxicabs and bikes) and extensive experimental results show that our approach outperforms the adaptations of state-of-the-art methods. And we have developed a crowd flow forecasting system for irregular regions that can now be used internally.
CairnFORM: a Shape-Changing Ring Chart Notifying Renewable Energy Availability in Peripheral Locations

We present CairnFORM, a shape-changing cylindrical display that physicalizes forecasts of renewable energy availability. CairnFORM aims at creating and encouraging new socially-shared practices by displaying energy data in collective and public spaces, such as public places and workplaces. It is 360°-readable, and as a dynamic physical ring chart, it can change its cylindrical symmetry with quiet motion. We conducted two user studies. The first study clearly revealed the attractiveness of CairnFORM in a public place and its usability for a range task and for a compare task. Consequently, this makes CairnFORM useful to analyze renewable energy availability. The second study revealed that a non-constant motion speed is the better visualization stimulus at a workplace.
Algorithmic complexity of multiplex networks

Multilayer networks preserve full information about the different interactions among the constituents of a complex system, and have recently proven quite useful in modelling transportation networks, social circles, and the human brain. A fundamental and still open problem is to assess if and when the multilayer representation of a system provides a qualitatively better model than the classical single-layer aggregated network. Here we tackle this problem from an algorithmic information theory perspective. We propose an intuitive way to encode a multilayer network into a bit string, and we define the complexity of a multilayer network as the ratio of the Kolmogorov complexity of the bit strings associated to the multilayer and to the corresponding aggregated graph. We find that there exists a maximum amount of additional information that a multilayer model can encode with respect to the equivalent single-layer graph. We show how our complexity measure can be used to obtain low-dimensional representations of multidimensional systems, to cluster multilayer networks into a small set of meaningful super-families, and to detect tipping points in the evolution of different time-varying multilayer graphs. Interestingly, the low-dimensional multiplex networks obtained with the proposed method also retain most of the dynamical properties of the original systems, as demonstrated for instance by the preservation of the epidemic threshold in the multiplex SIS model. These results suggest that information-theoretic approaches can be effectively employed for a more systematic analysis of static and time-varying multidimensional complex systems.
Cloze-driven Pretraining of Self-attention Networks

We present a new approach for pretraining a bi-directional transformer model that provides significant performance gains across a variety of language understanding problems. Our model solves a cloze-style word reconstruction task, where each word is ablated and must be predicted given the rest of the text. Experiments demonstrate large performance gains on GLUE and new state of the art results on NER as well as constituency parsing benchmarks, consistent with BERT. We also present a detailed analysis of a number of factors that contribute to effective pretraining, including data domain and size, model capacity, and variations on the cloze objective.
Large Intelligent Surface Assisted MIMO Communications

This work focuses on the downlink of a single-cell multi-user (MU) system in which a base station (BS) equipped with M antennas communicates with K single-antenna users through a large intelligent surface (LIS) installed in the line-of-sight (LoS) of the BS. LIS is envisioned to offer unprecedented massive multiple-input multiple-output (MIMO) like gains by utilizing N passive reflecting elements that induce phase shifts on the impinging electromagnetic waves to smartly reconfigure the signal propagation. We study the minimum signal-to-interference-plus-noise ratio (SINR) achieved by the optimal linear precoder (OLP), that maximizes the minimum SINR subject to a given power constraint for any given LIS phase matrix, for the cases where the LoS channel matrix between the BS and the LIS is of rank-one and of full-rank. In the former scenario, the minimum SINR is bounded by a quantity that goes to zero with K > 1. For the high-rank scenario, we develop accurate deterministic approximations for the parameters of the asymptotically OLP, which are then utilized to optimize the LIS phase matrix. Simulation results show that the LIS-assisted system can offer Massive MIMO like gains with a much fewer number of active antennas, thereby significantly reducing the energy consumption at the BS.
Learning to Augment Synthetic Images for Sim2Real Policy Transfer

Vision and learning have made significant progress that could improve robotics policies for complex tasks and environments. Learning deep neural networks for image understanding, however, requires large amounts of domain-specific visual data. While collecting such data from real robots is possible, such an approach limits the scalability as learning policies typically requires thousands of trials.In this work we attempt to learn manipulation policies in simulated environments. Simulators enable scalability and provide access to the underlying world state during training. Policies learned in simulators, however, do not transfer well to real scenes given the domain gap between real and synthetic data. We follow recent work on domain randomization and augment synthetic images with sequences of random transformations. Our main contribution is to optimize the augmentation strategy for sim2real transfer and to enable domain-independent policy learning. We design an efficient search for depth image augmentations using object localization as a proxy task. Given the resulting sequence of random transformations, we use it to augment synthetic depth images during policy learning. Our augmentation strategy is policy-independent and enables policy learning with no real images. We demonstrate our approach to significantly improve accuracy on three manipulation tasks evaluated on a real robot.