Miracle-dz/UniFAR-FTUs huggingface dataset:
Prioritarianism and Climate Change

The paper compares utilitarianism and prioritarianism as alternative social welfare frameworks for evaluating climate policies. We review the main debates in the climate policy literature concerning the parameters of the utilitarian social welfare function, and discuss the analytical requirements and climate policy implications of prioritarianism both in deterministic and stochastic settings. We show that, given the specific characteristics of the climate issue and the assumptions routinely made in the climate literature, prioritarianism tends to support more lenient climate policies than undiscounted utilitarianism. This is based on the assumption of economic and social progress that makes the current generation worse-off than future generations. The presence of catastrophic climate outcomes that endanger the living conditions of future generations (or of the poorest individuals living in the future) relaxes this result.
SuperMix : Supplementary Material Anonymous CVPR submission

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Utility of lung ultrasound for extravascular lung water volume estimation during cytoreductive surgery and hyperthermic intraperitoneal chemotherapy

Background and Aims: Rising extravascular lung-water index (ELWI) following cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (CRS + HIPEC), if not timely intervened, can progress to pulmonary oedema. Transpulmonary thermodilution (TPTDL) is a standard technique to estimate ELWI (T-ELWI score), and track ongoing changes. Lung ultrasound (LUS) is another technique for ELWI (L-ELWI score) estimation. However, reproducibility and reliability of LUS for tracking serial L-ELWI changes during CRS + HIPEC remains to be validated. Methods: This prospective observational study included 360 L-ELWI and T-ELWI measurements at 12 peri-operative time-points. Cohen's Kappa test was used to assess reproducibility, Inter-rater agreement (between the anaesthetist and radiologist), and agreement between LUS and TPTDL for classifying the severity of pulmonary oedema. Reliability of LUS for 'tracking serial changes' in ELWI over time in individual patients was assessed by determining the repeated measures correlation (z-rrm) between weighted L-ELWI and T-ELWI scores. The ability of both techniques to discriminate pulmonary oedema was compared by analysing the area under ROC curves. Results: Excellent inter-rater agreement for assigned L-ELWI scores was observed (linear weighted κ = 0.95 for both). Both techniques had a good agreement in classifying the severity of pulmonary oedema (linear weighted κ = 0.63, 95% CI 0.51–0.79). T-ELWI and weighted L-ELWI scores correlated strongly (z-rrm = 0.88, 95% CI 0.80–0.92, P < 0.0001). Both techniques had comparable ability to discriminate pulmonary oedema (difference in area under ROC curve = 0.0014, 95%CI –0.0027 to 0.0055, P = 0.5043). Conclusion: We found the utility of LUS as a reliable and reproducible technique for ELWI estimation and tracking its changes over time in CRS + HIPEC.
Volatile-mediated defense regulation occurs in maize leaves but not in maize roots.

Plant leaves that are exposed to herbivore induced plant volatiles (HIPVs) respond by increasing their defenses, a phenomenon referred to as priming. Whether this phenomenon also occurs in the roots is unknown. Using maize plants, Zea mays, whose leaves respond strongly to leaf HIPVs, we measured the impact of belowground HIPVs, emanating from roots infested by the banded cucumber beetle, Diabrotica balteata, on constitutive and herbivore-induced levels of defense-related gene expression, phytohormones, volatile and non-volatile primary and secondary metabolites, growth and herbivore resistance in roots of neighboring plants. HIPV exposure did not increase constitutive or induced levels of any of the measured root traits. Furthermore, HIPV exposure did not reduce the performance or survival of D. balteata on maize or its ancestor teosinte. Cross-exposure experiments between HIPVs from roots and leaves revealed that maize roots, in contrast to maize leaves, neither emit nor respond strongly to defense-regulating HIPVs. Together, these results demonstrate that volatile-mediated defense regulation is restricted to the leaves of maize. This finding is in line with the lower diffusibility of volatiles in the soil and the availability of other, potentially more efficient information conduits below ground. This article is protected by copyright. All rights reserved.
How Low Can You Go? A Case Study in Extremely Low-Resource NMT

Neural Machine Translation (NMT) has improved dramatically in the past decade, with many NMT systems for high-resource languages approaching human-quality translations. However, many of the world’s languages are low-resource, with very little digitized parallel data available to train NMT models for them. Although there have been many advancements in developing techniques for low-resource NMT, many languages still have orders of magnitude less data than those used in the associated studies. One such extremely low-resource language is Cherokee, which has less than 15,000 parallel Cherokee-English sentences available. We present a case-study that evaluates the efficacy of common low-resource NMT techniques on Cherokee-English (ChrEn) translation. We analyze the performance of data augmentation, noisy self-training, back-translation, aggressive word dropout, pre-trained word embeddings, pre-trained decoders, mT5, and additional LSTM layers on improving a ChrEn NMT system. We find that pre-training the decoder with 100,000 monolingual English Sentences and back-translation using 5,000 English sentences offer a 0.9 and 0.8 BLEU score improvement over the baseline, respectively, while noisy self-training and aggressive word dropout provide inconsistent benefits in this extremely low-resource setting.
Plug-and-Play Quantum Adaptive Denoiser for Deconvolving Poisson Noisy Images

A new Plug-and-Play (PnP) alternating direction of multipliers (ADMM) scheme is proposed in this paper, by embedding a recently introduced adaptive denoiser using the Schroedinger equation’s solutions of quantum physics. The potential of the proposed model is studied for Poisson image deconvolution, which is a common problem occurring in number of imaging applications, such as limited photon acquisition or X-ray computed tomography. Numerical results show the efficiency and good adaptability of the proposed scheme compared to recent state-of-the-art techniques, for both high and low signal-to-noise ratio scenarios. This performance gain regardless of the amount of noise affecting the observations is explained by the flexibility of the embedded quantum denoiser constructed without anticipating any prior statistics about the noise, which is one of the main advantages of this method. The main novelty of this work resided in the integration of a modified quantum denoiser into the PnP-ADMM framework and the numerical proof of convergence of the resulting algorithm.
Robotic Template Library

Robotic Template Library (RTL) is a set of tools for dealing with geometry and point cloud processing, especially in robotic applications. The software package covers basic objects such as vectors, line segments, quaternions, rigid transformations, etc., however, its main contribution lies in the more advanced modules: The segmentation module for batch or stream clustering of point clouds, the fast vectorization module for approximation of continuous point clouds by geometric objects of higher grade and the LaTeX export module enabling automated generation of high-quality visual outputs. It is a header-only library written in C++17, uses the Eigen library as a linear algebra back-end, and is designed with high computational performance in mind. RTL can be used in all robotic tasks such as motion planning, map building, object recognition and many others, but the point cloud processing utilities are general enough to be employed in any field touching object reconstruction and computer vision applications as well.
Sparse GCA and Thresholded Gradient Descent

Generalized correlation analysis (GCA) is concerned with uncovering linear relationships across multiple datasets. It generalizes canonical correlation analysis that is designed for two datasets. We study sparse GCA when there are potentially multiple generalized correlation tuples in data and the loading matrix has a small number of nonzero rows. It includes sparse CCA and sparse PCA of correlation matrices as special cases. We first formulate sparse GCA as generalized eigenvalue problems at both population and sample levels via a careful choice of normalization constraints. Based on a Lagrangian form of the sample optimization problem, we propose a thresholded gradient descent algorithm for estimating GCA loading vectors and matrices in high dimensions. We derive tight estimation error bounds for estimators generated by the algorithm with proper initialization. We also demonstrate the prowess of the algorithm on a number of synthetic datasets.
Symbolic regression for the work function of metal/organic interfaces: when are descriptors physically meaningful?

We apply symbolic regression in the form of the Sure Independence Screening and Sparsifying Operator (SISSO) to hybrid inorganic/organic interfaces, with the intent to extract physically meaningful correlations between the adsorption-induced work-function modifications and the properties of the constituents. We separately investigate two cases: Hypothetical, free standing self-assembled monolayers with a large intrinsic dipole moment, and metal-organic interfaces with a large chargetransfer induced dipole. For the former, we find – without notable prior assumptions – the Topping model, as expected from literature. For the latter, where presently no sensible correlations are known, we find expressions with high predictive accuracy. These are, however, partly unphysical. We show that this is because even first-principles data are noisy, and due to intrinsic limitations of the SISSO approach.
Efficient teraherz radiation from electrostatic wakefields in non-uniform plasmas

It is shown that electrostatic plasma wakefields can efficiently radiate at harmonics of the plasma frequency when the plasma has a positive density gradient along the propagation direction of a driver. The driver propagating at a sub-luminal group velocity excites the plasma wakefield with the same phase velocity. However, due to the positive density gradient, the wake phase velocity steadily increases behind the driver. As soon as the phase velocity becomes super-luminal, the electrostatic wakefield couples efficiently to radiative electromagnetic modes. The period of time when the phase velocity stays above the speed of light depends on the density gradient scale length. The wake radiates at harmonics of the plasma frequency in the teraherz (THz) band. The angle of emission depends on the gradient scale and the time passed behind the driver. For appropriate plasma and driver parameters, the wake can radiate away nearly all its energy, which potentially results in an efficient, narrow band and tunable source of THz radiation.
A semiparametric Bayesian approach to epidemics, with application to the spread of the coronavirus MERS in South Korea in 2015

We consider incomplete observations of stochastic processes governing the spread of infectious diseases through finite populations by way of contact. We propose a flexible semiparametric modelling framework with at least three advantages. First, it enables researchers to study the structure of a population contact network and its impact on the spread of infectious diseases. Second, it can accommodate short- and long-tailed degree distributions and detect potential superspreaders, who represent an important public health concern. Third, it addresses the important issue of incomplete data. Starting from first principles, we show when the incomplete-data generating process is ignorable for the purpose of Bayesian inference for the parameters of the population model. We demonstrate the semiparametric modelling framework by simulations and an application to the partially observed MERS epidemic in South Korea in 2015. We conclude with an extended discussion of open questions and directions for future research.
Do we need to estimate the variance in robust mean estimation?

In this paper, we propose self-tuned robust estimators for estimating the mean of heavy-tailed distributions, which refer to distributions with only finite variances. Our approach introduces a new loss function that considers both the mean parameter and a robustification parameter. By jointly optimizing the empirical loss function with respect to both parameters, the robustification parameter estimator can automatically adapt to the unknown data variance, and thus the self-tuned mean estimator can achieve optimal finite-sample performance. Our method outperforms previous approaches in terms of both computational and asymptotic efficiency. Specifically, it does not require cross-validation or Lepski's method to tune the robustification parameter, and the variance of our estimator achieves the Cram\'er-Rao lower bound. Project source code is available at \url{https://github.com/statsle/automean}.
MHER: Model-based Hindsight Experience Replay

Solving multi-goal reinforcement learning (RL) problems with sparse rewards is generally challenging. Existing approaches have utilized goal relabeling on collected experiences to alleviate issues raised from sparse rewards. However, these methods are still limited in efficiency and cannot make full use of experiences. In this paper, we propose Model-based Hindsight Experience Replay (MHER), which exploits experiences more efficiently by leveraging environmental dynamics to generate virtual achieved goals. Replacing original goals with virtual goals generated from interaction with a trained dynamics model leads to a novel relabeling method, model-based relabeling (MBR). Based on MBR, MHER performs both reinforcement learning and supervised learning for efficient policy improvement. Theoretically, we also prove the supervised part in MHER, i.e., goal-conditioned supervised learning with MBR data, optimizes a lower bound on the multi-goal RL objective. Experimental results in several point-based tasks and simulated robotics environments show that MHER achieves significantly higher sample efficiency than previous model-free and model-based multi-goal methods.
Fast, Efficient and Flexible Particle Accelerator Optimisation Using Densely Connected and Invertible Neural Networks

Particle accelerators are enabling tools for scientific exploration and discovery in various disciplines. However, finding optimised operation points for these complex machines is a challenging task due to the large number of parameters involved and the underlying non-linear dynamics. Here, we introduce two families of data-driven surrogate models, based on deep and invertible neural networks, that can replace the expensive physics computer models. These models are employed in multi-objective optimisations to find Pareto optimal operation points for two fundamentally different types of particle accelerators. Our approach reduces the time-to-solution for a multi-objective accelerator optimisation up to a factor of 640 and the computational cost up to 98%. The framework established here should pave the way for future online and real-time multi-objective optimisation of particle accelerators.
Using Anomaly Feature Vectors for Detecting, Classifying and Warning of Outlier Adversarial Examples

We present DeClaW, a system for detecting, classifying, and warning of adversarial inputs presented to a classification neural network. In contrast to current state-of-the-art methods that, given an input, detect whether an input is clean or adversarial, we aim to also identify the types of adversarial attack (e.g., PGD, Carlini-Wagner or clean). To achieve this, we extract statistical profiles, which we term as anomaly feature vectors, from a set of latent features. Preliminary findings suggest that AFVs can help distinguish among several types of adversarial attacks (e.g., PGD versus Carlini-Wagner) with close to 93% accuracy on the CIFAR-10 dataset. The results open the door to using AFV-based methods for exploring not only adversarial attack detection but also classification of the attack type and then design of attack-specific mitigation strategies.
Time-dependent homogeneous states of binary granular suspensions

The time evolution of a homogeneous bidisperse granular suspension is studied in the context of the Enskog kinetic equation. The influence of the surrounding viscous gas on the solid particles is modeled via a deterministic viscous drag force plus a stochastic Langevin-like term. It is found first that, regardless of the initial conditions, the system reaches (after a transient period lasting a few collisions per particle) a universal unsteady hydrodynamic regime where the distribution function of each species not only depends on the dimensionless velocity (as in the homogeneous cooling state) but also on the instantaneous temperature scaled with respect to the background temperature. To confirm this result, theoretical predictions for the time-dependent partial temperatures are compared against direct simulation Monte Carlo (DSMC) results; the comparison shows an excellent agreement confirming the applicability of hydrodynamics in granular suspensions. Also, in the transient regime, the so-called Mpemba-like effect (namely, when an initially hotter sample cools sooner than the colder one) is analyzed for inelastic collisions. The theoretical analysis of the Mpemba effect is performed for initial states close to and far away from the asymptotic steady state. In both cases, a good agreement is found again between theory and DSMC results. As a complement of the previous studies, we determine in this paper the dependence of the steady values of the dynamic properties of the suspension on the parameter space of the system. More specifically, we focus on our attention in the temperature ratio $T_1/T_2$ and the fourth degree cumulants $c_1$ and $c_2$ (measuring the departure of the velocity distributions $f_1$ and $f_2$ from their Maxwellian forms). Finally, a linear stability analysis of the steady state solution is also carried out showing that the steady state is always linearly stable.
GlyphCRM: Bidirectional Encoder Representation for Chinese Character with its Glyph

Previous works indicate that the glyph of Chinese characters contains rich semantic information and has the potential to enhance the representation of Chinese characters. The typical method to utilize the glyph features is by incorporating them into the character embedding space. Inspired by previous methods, we innovatively propose a Chinese pre-trained representation model named as GlyphCRM, which abandons the ID-based character embedding method yet solely based on sequential character images. We render each character into a binary grayscale image and design two-channel position feature maps for it. Formally, we first design a two-layer residual convolutional neural network, namely HanGlyph to generate the initial glyph representation of Chinese characters, and subsequently adopt multiple bidirectional encoder Transformer blocks as the superstructure to capture the context-sensitive information. Meanwhile, we feed the glyph features extracted from each layer of the HanGlyph module into the underlying Transformer blocks by skip-connection method to fully exploit the glyph features of Chinese characters. As the HanGlyph module can obtain a sufficient glyph representation of any Chinese character, the long-standing out-of-vocabulary problem could be effectively solved. Extensive experimental results indicate that GlyphCRM substantially outperforms the previous BERT-based state-of-the-art model on 9 fine-tuning tasks, and it has strong transferability and generalization on specialized fields and low-resource tasks. We hope this work could spark further research beyond the realms of well-established representation of Chinese texts.
Impact Remediation: Optimal Interventions to Reduce Inequality

A significant body of research in the data sciences considers un-fair discrimination against social categories such as race or gender that could occur or be amplified as a result of algorithmic decisions. Simultaneously, real-world disparities continue to exist, even before algorithmic decisions are made. In this work, we draw on insights from the social sciences and humanistic studies brought into the realm of causal modeling and constrained optimization, and develop a novel algorithmic framework for tackling pre-existing real-world disparities. The purpose of our framework, which we call the “impact remediation framework,” is to measure real-world disparities and discover the optimal intervention policies that could help improve equity or access to opportunity for those who are underserved with respect to an outcome of interest. We develop a disaggregated approach to tackling pre-existing disparities that relaxes the typical set of assumptions required for the use of social categories in structural causal models. Our approach flexibly incorporates counterfactuals and is compatible with various ontological assumptions about the nature of social categories. We demonstrate impact remediation with a real-world case study and compare our disaggregated approach to an existing state-of-the-art approach, comparing its structure and resulting policy recommendations. In contrast to most work on optimal policy learning, we explore disparity reduction itself as an objective, explicitly focusing the power of algorithms on reducing inequality.
Automatic Metadata Extraction Incorporating Visual Features from Scanned Electronic Theses and Dissertations

Electronic Theses and Dissertations (ETDs) contain domain knowledge that can be used for many digital library tasks, such as analyzing citation networks and predicting research trends. Automatic metadata extraction is important to build scalable digital library search engines. Most existing methods are designed for born-digital documents such as GROBID, CERMINE, and ParsCit, so they often fail to extract metadata from scanned documents such as for ETDs. Traditional sequence tagging methods mainly rely on text-based features. In this paper, we propose a conditional random field (CRF) model that combines text-based and visual features. To verify the robustness of our model, we extended an existing corpus and created a new ground truth corpus consisting of 500 ETD cover pages with human validated metadata. Our experiments show that CRF with visual features outperformed both a heuristic baseline and a CRF model with only text-based features. The proposed model achieved 81.3%-96% F1 measure on seven metadata fields. The data and source code are publicly available on Google Drive11httns://tinvurl.com/y8kxzwrp and a GitHub repository22https://github.com/lamps-lab/ETDMiner/tree/master/etd_crf, respectively.