Miracle-dz/UniFAR-FTUs huggingface dataset:
LEARNING HYPERINFLATIONS

Emprical studies of hyperinflations reveal that the rational expectations hypothesis fails to hold. To address this issue, we study a model of hyperinflation and learning in an attempt to better understand the volatility in movements of expectations, money, and prices. The findings surprisingly imply that the dynamics under neural network learning appear to support the outcome achieved under least squares learning reported in the earlier literature. Relaxing the assumption that inflationary expectations are rational, however, is essential since it improves the fit of the model to actual data from episodes of severe hyperinflation. Simulations provide ample evidence that if equilibrium in the model exists, then the inflation rate converges to the low inflation rational expectations equilibrium. This suggests a classical result: a permanent increase in the government deficit raises the stationary inflation rate (Marcet and Sargent, 1989).
Bayesian Simultaneous Estimation of Unsaturated Flow and Solute Transport Parameters from a Laboratory Infiltration Experiment

Numerical modeling has become an irreplaceable tool for the investigation of water flow and solute transport in the unsaturated zone. The use of this tool for real situations is often faced with lack of knowledge of hydraulic and soil transport parameters. In this study, advanced experimental and numerical techniques are developed for an accurate estimation of the soil parameters. A laboratory unsaturated flow and solute transport experiment is conducted on a large undisturbed soil column of around 40 cm length. Bromide, used as a nonreactive contaminant, is injected at the surface of the undisturbed soil, followed by a leaching phase. The pressure measurements at different locations along the soil column as well as the outflow bromide concentration are collected during the experiment and used for the statistical calibration of flow and solute transport. The Richards equation, combined with constitutive relations for water content and permeability, is used to describe unsaturated flow. Both linear and non-equilibrium mobile–immobile transport models are investigated for the solute transport. All hydraulic and mass transport parameters are inferred using a one-step Bayesian estimation with the Markov chain Monte Carlo sampler. The results prove that the pressure and concentration measurements are able to identify almost all hydraulic and mass transport parameters. The mobile–immobile transport model better reproduces the infiltration experiment. It produces narrower uncertainty intervals for soil parameters and predictive output concentrations.
Projection Bias in the Decision to Go Solar : Evidence on Costly Cancellations

This paper studies the effect of short-run weather fluctuations on solar panel adoption in California. I find that customers whose sign-up for solar panels is followed by bad weather are more likely to cancel their contract. In contrast, non-residential customers appear unaffected by short-term weather conditions. These results are consistent with residential customers suffering from projection bias – that is, they rely too heavily on transient conditions when predicting long-run utility. I show further evidence that this effect is mainly driven by mistaken predictions about future solar panel performance rather than electricity demand. There is also suggestive evidence that having more neighbors with solar panels does not mitigate projection bias, and that marginal customers are more susceptible to it. The results add to the emerging field evidence on projection bias, particularly by carefully considering the dynamics of the entire purchase-return cycle. The results also have efficiency implications for current subsidy programs on solar panel adoption.
Enhancing Failure Propagation Analysis in Cloud Computing Systems

In order to plan for failure recovery, the designers of cloud systems need to understand how their system can potentially fail. Unfortunately, analyzing the failure behavior of such systems can be very difficult and time-consuming, due to the large volume of events, non-determinism, and reuse of third-party components. To address these issues, we propose a novel approach that joins fault injection with anomaly detection to identify the symptoms of failures. We evaluated the proposed approach in the context of the OpenStack cloud computing platform. We show that our model can significantly improve the accuracy of failure analysis in terms of false positives and negatives, with a low computational cost.
Onset of planet formation in the warm inner disk

Aims. Collisional growth of dust occurs in all regions of protoplanetary disks with certain materials dominating between various condensation lines. The sticking properties of the prevalent dust species depend on the specific temperatures. The inner disk is the realm of silicates spanning a wide range of temperatures from room temperature up to sublimation beyond 1500 K. Methods. For the first time, we carried out laboratory collision experiments with hot levitated basalt dust aggregates of 1 mm in size. The aggregates are compact with a filling factor of 0.37 ± 0.06. The constituent grains have a wide size distribution that peaks at about 0.6 μm. Temperatures in the experiments are varied between approximately 600 and 1100 K. Results. Collisions are slow with velocities between 0.002 and 0.15 m s−1, i.e., relevant for protoplanetary disks. Aside from variations of the coefficients of restitution due to varying collision velocities, the experiments show low sticking probability below 900 K and an increasing sticking probability starting at 900 K. Conclusions. This implies that dust can grow to larger size in hot regions, which might change planet formation. One scenario is an enhanced probability for local planetesimal formation. Another scenario is a reduction of planetesimal formation as larger grains are more readily removed as a consequence of radial drift. However, the increased growth at high temperatures likely changes planetesimal formation one way or the other.
Electromagnetic induction imaging with atomic magnetometers: Unlocking the low-conductivity regime

We demonstrate non-destructive imaging and discrimination of specimens with electrical conductivities down to $5\times10^2$ Sm$^{-1}$. The images are obtained with an $^{85}$Rb radio-frequency atomic magnetometer operating at room temperature, in an unshielded environment. This extends the domain of electromagnetic imaging with atomic magnetometers to low-conductivity, non-metallic materials - representing an improvement of more than two orders of magnitude from previous results. We validate the technology by imaging and identifying different concentrations of n-dopants in Si samples. Simultaneous images of specimens with a difference in conductivity up to twenty times is demonstrated. Multi-frequency image analysis allows unambiguous discrimination of different dopant levels, with a resolution of 2.5 times. These results demonstrate a viable approach for non-invasive imaging of non-metallic materials, as well as for accessing and imaging the bulk properties of large wafers of semiconductors, with the potential for full material characterisation and quality monitoring. High-impact specific applications in science and technology are identified.
Optimality of Maximum Likelihood for Log-Concave Density Estimation and Bounded Convex Regression

In this paper, we study two problems: (1) estimation of a $d$-dimensional log-concave distribution and (2) bounded multivariate convex regression with random design with an underlying log-concave density or a compactly supported distribution with a continuous density. First, we show that for all $d \ge 4$ the maximum likelihood estimators of both problems achieve an optimal risk of $\Theta_d(n^{-2/(d+1)})$ (up to a logarithmic factor) in terms of squared Hellinger distance and $L_2$ squared distance, respectively. Previously, the optimality of both these estimators was known only for $d\le 3$. We also prove that the $\epsilon$-entropy numbers of the two aforementioned families are equal up to logarithmic factors. We complement these results by proving a sharp bound $\Theta_d(n^{-2/(d+4)})$ on the minimax rate (up to logarithmic factors) with respect to the total variation distance. Finally, we prove that estimating a log-concave density - even a uniform distribution on a convex set - up to a fixed accuracy requires the number of samples \emph{at least} exponential in the dimension. We do that by improving the dimensional constant in the best known lower bound for the minimax rate from $2^{-d}\cdot n^{-2/(d+1)}$ to $c\cdot n^{-2/(d+1)}$ (when $d\geq 2$).
Learning Rich Representations For Structured Visual Prediction Tasks

We describe an approach to learning rich representations for images, that enables simple and effective predictors in a range of vision tasks involving spatially structured maps. Our key idea is to map small image elements to feature representations extracted from a sequence of nested regions of increasing spatial extent. These regions are obtained by "zooming out" from the pixel/superpixel all the way to scene-level resolution, and hence we call these zoom-out features. Applied to semantic segmentation and other structured prediction tasks, our approach exploits statistical structure in the image and in the label space without setting up explicit structured prediction mechanisms, and thus avoids complex and expensive inference. Instead image elements are classified by a feedforward multilayer network with skip-layer connections spanning the zoom-out levels. When used in conjunction with modern neural architectures such as ResNet, DenseNet and NASNet (to which it is complementary) our approach achieves competitive accuracy on segmentation benchmarks. In addition, we propose an approach for learning category-level semantic segmentation purely from image-level classification tag. It exploits localization cues that emerge from training a modified zoom-out architecture tailored for classification tasks, to drive a weakly supervised process that automatically labels a sparse, diverse training set of points likely to belong to classes of interest. Finally, we introduce data-driven regularization functions for the supervised training of CNNs. Our innovation takes the form of a regularizer derived by learning an autoencoder over the set of annotations. This approach leverages an improved representation of label space to inform extraction of features from images
Alchemy: Techniques for Rectification Based Irregular Scene Text Recognition

Reading text from natural images is challenging due to the great variety in text font, color, size, complex background and etc. The perspective distortion and non-linear spatial arrangement of characters make it further difﬁcult. While rectiﬁcation based method is intuitively grounded and has pushed the envelope by far, its potential is far from being well exploited. In this paper, we present a bag of techniques that prove to signiﬁcantly improve the performance of rectiﬁcation based method. On curved text dataset, our method achieves an accuracy of 89 . 6% on CUTE-80 and 76 . 3% on Total-Text, an improvement over previous state-of-the-art by 6 . 3% and 14 . 7% respectively. Furthermore, our combination of techniques helps us win the ICDAR 2019 Arbitrary-Shaped Text Challenge (Latin script), achieving an accuracy of 74 . 3% on the held-out test set. We release our code as well as data samples for further exploration at https://github.com/Jyouhou/ ICDAR2019-ArT-Recognition-Alchemy.
Large eddy simulation study of the humidity variation in the shadow of a large wind farm

Numerous studies have shown that wind turbine wakes within a large wind farm bring about changes to both the dynamics and thermodynamics of the atmospheric boundary layers (ABL). Previously, we investigated the relative humidity budget within a wind farm via field measurements in the near-wake region and large eddy simulations (LES). The effect of the compounding wakes within a large wind farm on the relative humidity was also investigated by LES. In this study, we investigate how the areas of relative humidity variation, that was observed in the near-wake, develop downstream in the shadow region of a large wind farm. To this end, LES of a wind farm consisting of 8x6 wind turbines with periodic boundary condition in the lateral direction (inferring an infinitely wide farm) interacting with a stable ABL is carried out. Two wind farm layouts, aligned and staggered, are considered in the analysis and the results from both configurations are compared to each other. It is observed that a decrease of relative humidity underneath the hub height and an increase above the hub height build up within the wind farm, and are maintained in the downstream of the farm for long distances. The staggered farm layout is more effective in keeping a more elongated region of low relative humidity underneath the hub, when compared to the aligned layout.
Modeling Multi-Action Policy for Task-Oriented Dialogues

Dialogue management (DM) plays a key role in the quality of the interaction with the user in a task-oriented dialogue system. In most existing approaches, the agent predicts only one DM policy action per turn. This significantly limits the expressive power of the conversational agent and introduces unwanted turns of interactions that may challenge users’ patience. Longer conversations also lead to more errors and the system needs to be more robust to handle them. In this paper, we compare the performance of several models on the task of predicting multiple acts for each turn. A novel policy model is proposed based on a recurrent cell called gated Continue-Act-Slots (gCAS) that overcomes the limitations of the existing models. Experimental results show that gCAS outperforms other approaches. The datasets and code are available at https://leishu02.github. io/.
Adaptive test of independence based on HSIC measures

Dependence measures based on reproducing kernel Hilbert spaces, also known as Hilbert-Schmidt Independence Criterion and denoted HSIC, are widely used to statistically decide whether or not two random vectors are dependent. Recently, non-parametric HSIC-based statistical tests of independence have been performed. However, these tests lead to the question of the choice of the kernels associated to the HSIC. In particular, there is as yet no method to objectively select specific kernels with theoretical guarantees in terms of first and second kind errors. One of the main contributions of this work is to develop a new HSIC-based aggregated procedure which avoids such a kernel choice, and to provide theoretical guarantees for this procedure. To achieve this, we first introduce non-asymptotic single tests based on Gaussian kernels with a given bandwidth, which are of prescribed level $\alpha \in (0,1)$. From a theoretical point of view, we upper-bound their uniform separation rate of testing over Sobolev and Nikol'skii balls. Then, we aggregate several single tests, and obtain similar upper-bounds for the uniform separation rate of the aggregated procedure over the same regularity spaces. Another main contribution is that we provide a lower-bound for the non-asymptotic minimax separation rate of testing over Sobolev balls, and deduce that the aggregated procedure is adaptive in the minimax sense over such regularity spaces. Finally, from a practical point of view, we perform numerical studies in order to assess the efficiency of our aggregated procedure and compare it to existing independence tests in the literature.
A Queuing Approach to Parking: Modeling, Verification, and Prediction

We present a queuing model of parking dynamics and a model-based prediction method to provide real-time probabilistic forecasts of future parking occupancy. The queuing model has a non-homogeneous arrival rate and time-varying service time distribution. All statistical assumptions of the model are verified using data from 29 truck parking locations, each with between 55 and 299 parking spots. For each location and each spot the data specifies the arrival and departure times of a truck, for 16 months of operation. The modeling framework presented in this paper provides empirical support for queuing models adopted in many theoretical studies and policy designs. We discuss how our framework can be used to study parking problems in different environments. Based on the queuing model, we propose two prediction methods, a microscopic method and a macroscopic method, that provide a real-time probabilistic forecast of parking occupancy for an arbitrary forecast horizon. These model-based methods convert a probabilistic forecast problem into a parameter estimation problem that can be tackled using classical estimation methods such as regressions or pure machine learning algorithms. We characterize a lower bound for an arbitrary real-time prediction algorithm. We evaluate the performance of these methods using the truck data comparing the outcomes of their implementations with other model-based and model-free methods proposed in the literature.
Dialog Intent Induction with Deep Multi-View Clustering

We introduce the dialog intent induction task and present a novel deep multi-view clustering approach to tackle the problem. Dialog intent induction aims at discovering user intents from user query utterances in human-human conversations such as dialogs between customer support agents and customers. Motivated by the intuition that a dialog intent is not only expressed in the user query utterance but also captured in the rest of the dialog, we split a conversation into two independent views and exploit multi-view clustering techniques for inducing the dialog intent. In par- ticular, we propose alternating-view k-means (AV-KMEANS) for joint multi-view represen- tation learning and clustering analysis. The key innovation is that the instance-view representations are updated iteratively by predicting the cluster assignment obtained from the alternative view, so that the multi-view representations of the instances lead to similar cluster assignments. Experiments on two public datasets show that AV-KMEANS can induce better dialog intent clusters than state-of-the-art unsupervised representation learning methods and standard multi-view clustering approaches.
UGC 1378 – a Milky Way sized galaxy embedded in a giant low surface brightness disc

The dominant physical processes responsible for the formation and longevity of giant gaseous and stellar discs in galaxies remain controversial. Although they are rare (less than 10 confirmed as of now), giant low-surface brightness (gLSB) discy galaxies provide interesting insights given their extreme nature. We describe observations of UGC 1378 including deep spectroscopy with the Russian 6-m telescope and multiband imaging with Binospec at the MMT. Galaxy UGC 1378 has both high surface brightness and an extended low surface brightness discs. Our stellar velocity dispersion data for the high surface brightness, Milky Way sized, disc appears inconsistent with a recent major merger, a widely discussed formation scenario for the very extended low surface brightness disc. We estimate the star formation rates (SFRs) from archival Wide-Field Infrared Survey Explorer data. The SFR surface density in the LSB disc is low relative to its gas density, consistent with recent gas accretion. We argue that the unusually large size of UGC 1378’s disc may be the product of a rich gas reservoir (e.g. a cosmic filament) and an isolated environment that has preserved the giant disc.
The impact of AGN feedback on galaxy intrinsic alignments in the Horizon simulations

The intrinsic correlations of galaxy shapes and orientations across the large-scale structure of the Universe are a known contaminant to weak gravitational lensing. They are known to be dependent on galaxy properties, such as their mass and morphologies. The complex interplay between alignments and the physical processes that drive galaxy evolution remains vastly unexplored. We assess the sensitivity of intrinsic alignments (shapes and angular momenta) to active galactic nuclei (AGN) feedback by comparing galaxy alignment in twin runs of the cosmological hydrodynamical Horizon simulation, which do and do not include AGN feedback, respectively. We measure intrinsic alignments in three dimensions and in projection at $z$ = 0 and $z$ = 1. We find that the projected alignment signal of all galaxies with resolved shapes with respect to the density field in the simulation is robust to AGN feedback, thus giving similar predictions for contamination to weak lensing. The relative alignment of galaxy shapes around galaxy positions is however significantly impacted, especially when considering high-mass ellipsoids. Using a sample of galaxy ‘twins’ across simulations, we determine that AGN changes both the galaxy selection and their actual alignments. Finally, we measure the alignments of angular momenta of galaxies with their nearest filament. Overall, these are more significant in the presence of AGN as a result of the higher abundance of massive pressure-supported galaxies.
Entanglement between Identical Particles Is a Useful and Consistent Resource

The existence of fundamentally identical particles represents a foundational distinction between classical and quantum mechanics. Due to their exchange symmetry, identical particles can appear to be entangled -- another uniquely quantum phenomenon with far-reaching practical implications. However, a long-standing debate has questioned whether identical particle entanglement is physical or merely a mathematical artefact. In this work, we provide such particle entanglement with a consistent theoretical description as a quantum resource in processes frequently encountered in optical and cold atomic systems. This leads to a plethora of applications of immediate practical impact. On one hand, we show that the metrological advantage for estimating phase shifts in systems of identical bosons amounts to a measure of their particle entanglement, with a clearcut operational meaning. On the other hand, we demonstrate in general terms that particle entanglement is the property resulting in directly usable mode entanglement when distributed to separated parties, with particle conservation laws in play. Application of our tools to an experimental implementation with Bose-Einstein condensates leads to the first quantitative estimation of identical particle entanglement. Further connections are revealed between particle entanglement and other resources such as optical nonclassicality and quantum coherence. Overall, this work marks a resolutive step in the ongoing debate by delivering a unifying conceptual and practical understanding of entanglement between identical particles.