Miracle-dz/UniFAR-FTUs huggingface dataset:
Superradiant parametric x-ray emission

We compute a spectrum of parametric X-ray radiation (PXR) inside a crystal from a bunch of electrons, which is periodically modulated in density. We consider that the bunch of electrons is exiting from a XFEL channel. We demonstrate that in the case of a resonance between the frequency of parametric X-ray radiation and a frequency of modulation of an electron bunch the sequence of strong quasi-monochromatic X-ray pulses is formed — superradiant parametric X-ray emission (SPXE) with frequencies multiples of the modulation frequency. The number of photons in the impulse of SPXE in the case of an extremely asymmetric diﬀraction is comparable with the photon number in the impulse of a XFEL. Moreover the SPXE is directed under the large angle to the electron velocity and every harmonic in the spectrum is emitted under its own angle.
A Survey on Model-based Reinforcement Learning

Reinforcement learning (RL) solves sequential decision-making problems via a trial-and-error process interacting with the environment. While RL achieves outstanding success in playing complex video games that allow huge trial-and-error, making errors is always undesired in the real world. To improve the sample efficiency and thus reduce the errors, model-based reinforcement learning (MBRL) is believed to be a promising direction, which builds environment models in which the trial-and-errors can take place without real costs. In this survey, we take a review of MBRL with a focus on the recent progress in deep RL. For non-tabular environments, there is always a generalization error between the learned environment model and the real environment. As such, it is of great importance to analyze the discrepancy between policy training in the environment model and that in the real environment, which in turn guides the algorithm design for better model learning, model usage, and policy training. Besides, we also discuss the recent advances of model-based techniques in other forms of RL, including offline RL, goal-conditioned RL, multi-agent RL, and meta-RL. Moreover, we discuss the applicability and advantages of MBRL in real-world tasks. Finally, we end this survey by discussing the promising prospects for the future development of MBRL. We think that MBRL has great potential and advantages in real-world applications that were overlooked, and we hope this survey could attract more research on MBRL.
Encoding High-level Quantum Programs as SZX-diagrams

The Scalable ZX-calculus is a compact graphical language used to reason about linear maps between quantum states. These diagrams have multiple applications, but they frequently have to be constructed in a case-by-case basis. In this work we present a method to encode quantum programs implemented in a fragment of the linear dependently typed Proto-Quipper-D language as families of SZX-diagrams. We define a subset of translatable Proto-Quipper-D programs and show that our procedure is able to encode non-trivial algorithms as diagrams that grow linearly on the size of the program.
Optimal data-driven hiring with equity for underrepresented groups

We present a data-driven prescriptive framework for fair decisions, motivated by hiring. An employer evaluates a set of applicants based on their observable attributes. The goal is to hire the best candidates while avoiding bias with regard to a certain protected attribute. Simply ignoring the protected attribute will not eliminate bias due to correlations in the data. We present a hiring policy that depends on the protected attribute functionally, but not statistically, and we prove that, among all possible fair policies, ours is optimal with respect to the firm's objective. We test our approach on both synthetic and real data, and find that it shows great practical potential to improve equity for underrepresented and historically marginalized groups.
A machine learning photon detection algorithm for coherent x-ray ultrafast fluctuation analysis

X-ray free electron laser experiments have brought unique capabilities and opened new directions in research, such as creating new states of matter or directly measuring atomic motion. One such area is the ability to use finely spaced sets of coherent x-ray pulses to be compared after scattering from a dynamic system at different times. This enables the study of fluctuations in many-body quantum systems at the level of the ultrafast pulse durations, but this method has been limited to a select number of examples and required complex and advanced analytical tools. By applying a new methodology to this problem, we have made qualitative advances in three separate areas that will likely also find application to new fields. As compared to the “droplet-type” models, which typically are used to estimate the photon distributions on pixelated detectors to obtain the coherent x-ray speckle patterns, our algorithm achieves an order of magnitude speedup on CPU hardware and two orders of magnitude improvement on GPU hardware. We also find that it retains accuracy in low-contrast conditions, which is the typical regime for many experiments in structural dynamics. Finally, it can predict photon distributions in high average-intensity applications, a regime which up until now has not been accessible. Our artificial intelligence-assisted algorithm will enable a wider adoption of x-ray coherence spectroscopies, by both automating previously challenging analyses and enabling new experiments that were not otherwise feasible without the developments described in this work.
Photometric Redshift Uncertainties in Weak Gravitational Lensing Shear Analysis: Models and Marginalization

Recovering credible cosmological parameter constraints in a weak lensing shear analysis requires an accurate model that can be used to marginalize over nuisance parameters describing potential sources of systematic uncertainty, such as the uncertainties on the sample redshift distribution $n(z)$. Due to the challenge of running Markov Chain Monte-Carlo (MCMC) in the high dimensional parameter spaces in which the $n(z)$ uncertainties may be parameterized, it is common practice to simplify the $n(z)$ parameterization or combine MCMC chains that each have a fixed $n(z)$ resampled from the $n(z)$ uncertainties. In this work, we propose a statistically-principled Bayesian resampling approach for marginalizing over the $n(z)$ uncertainty using multiple MCMC chains. We self-consistently compare the new method to existing ones from the literature in the context of a forecasted cosmic shear analysis for the HSC three-year shape catalog, and find that these methods recover similar cosmological parameter constraints, implying that using the most computationally efficient of the approaches is appropriate. However, we find that for datasets with the constraining power of the full HSC survey dataset (and, by implication, those upcoming surveys with even tighter constraints), the choice of method for marginalizing over $n(z)$ uncertainty among the several methods from the literature may significantly impact the statistical uncertainties on cosmological parameters, and a careful model selection is needed to ensure credible parameter intervals.
Guided Safe Shooting: model based reinforcement learning with safety constraints

In the last decade, reinforcement learning successfully solved complex control tasks and decision-making problems, like the Go board game. Yet, there are few success stories when it comes to deploying those algorithms to real-world scenarios. One of the reasons is the lack of guarantees when dealing with and avoiding unsafe states, a fundamental requirement in critical control engineering systems. In this paper, we introduce Guided Safe Shooting (GuSS), a model-based RL approach that can learn to control systems with minimal violations of the safety constraints. The model is learned on the data collected during the operation of the system in an iterated batch fashion, and is then used to plan for the best action to perform at each time step. We propose three different safe planners, one based on a simple random shooting strategy and two based on MAP-Elites, a more advanced divergent-search algorithm. Experiments show that these planners help the learning agent avoid unsafe situations while maximally exploring the state space, a necessary aspect when learning an accurate model of the system. Furthermore, compared to model-free approaches, learning a model allows GuSS reducing the number of interactions with the real-system while still reaching high rewards, a fundamental requirement when handling engineering systems.
Design of Multi-model Linear Inferential Sensors with SVM-based Switching Logic

We study the problem of data-based design of multi-model linear inferential (soft) sensors. The multi-model linear inferential sensors promise increased prediction accuracy yet simplicity of the model structure and training. The standard approach to the multi-model inferential sensor design consists in three separate steps: 1) data labeling (establishing training subsets for individual models), 2) data classification (creating a switching logic for the models), and 3) training of individual models. There are two main issues with this concept: a) as steps 2)&3) are separate, discontinuities can occur when switching between the models; b) as steps 1)&3) are separate, data labelling disregards the quality of the resulting model. Our contribution aims at both the mentioned problems, where, for the problem a), we introduce a novel SVM-based model training coupled with switching logic identification and, for the problem b), we propose a direct optimization of data labelling. We illustrate the proposed methodology and its benefits on an example from the chemical engineering domain.
Perceptual Optimization of a Biologically-Inspired Tone Mapping Operator

—With the increasing popularity and accessibility of high dynamic range (HDR) photography, tone mapping operators (TMOs) for dynamic range compression and medium presentation are practically demanding. In this paper, we develop a two-stage neural network-based HDR image TMO that is biologically-inspired, computationally efﬁcient, and perceptually optimized. In Stage one, motivated by the physiology of the early stages of the human visual system (HVS), we ﬁrst decompose an HDR image into a normalized Laplacian pyramid. We then use two lightweight deep neural networks (DNNs) that take this normalized representation as input and estimate the Laplacian pyramid of the corresponding LDR image. We optimize the tone mapping network by minimizing the normalized Laplacian pyramid distance (NLPD), a perceptual metric calibrated against human judgments of tone-mapped image quality. In Stage two, we generate a pseudo-multi-exposure image stack with different color saturation and detail visibility by inputting an HDR image “calibrated” with different maximum luminances to the learned tone mapping network. We then train another lightweight DNN to fuse the LDR image stack into a desired LDR image by maximizing a variant of MEF-SSIM, another perceptually calibrated metric for image fusion. By doing so, the proposed TMO is fully automatic to tone map uncalibrated HDR images. Across an independent set of HDR images, we ﬁnd that our method produces images with consistently better visual quality, and is among the fastest local TMOs.
Convex space learning improves deep-generative oversampling for tabular imbalanced classification on smaller datasets

Data is commonly stored in tabular format. Several ﬁelds of research are prone to small imbalanced tabular data. Supervised Machine Learning on such data is often difﬁcult due to class imbalance. Synthetic data generation, i.e., oversampling, is a common remedy used to improve classiﬁer performance. State-of-the-art linear interpolation approaches, such as LoRAS and ProWRAS can be used to generate synthetic samples from the convex space of the minority class to improve classiﬁer performance in such cases. Deep generative networks are common deep learning approaches for synthetic sample generation, widely used for synthetic image generation. However, their scope on synthetic tabular data generation in the context of imbalanced classiﬁcation is not adequately explored. In this article, we show that existing deep generative models perform poorly compared to linear interpolation based approaches for imbalanced classiﬁcation problems on smaller tabular datasets. To overcome this, we propose a deep generative model, ConvGeN that combines the idea of convex space learning with deep generative models. ConvGeN learns the coefﬁcients for the convex combinations of the minority class samples, such that the synthetic data is distinct enough from the majority class. Our benchmarking experiments demonstrate that our proposed model ConvGeN improves imbalanced classiﬁcation on such small datasets, as compared to existing deep generative models, while being at-par with the existing linear interpolation approaches. Moreover, we discuss how our model can be used for synthetic tabular data generation in general, even outside the scope of data imbalance and thus, improves the overall applicability of convex space learning. in our study, according to parameter settings. These parameter settings for ConvGeN can be retrieved as standard parameter settings and can be used as a starting point to optimize the model for future works.
The Current State of Project Management Research: Trends, Interpretations, and Predictions

This paper describes the methodology and results of a research effort that identified the project management research published in English since 1960. An annotated bibliography was created of 3,554 articles, papers, dissertations, and government research reports. Trends were identified in each of the nine A Guide to the Project Management Body of Knowledge (PMBOK® Guide) knowledge areas. A workshop was conducted with experienced practitioners to help interpret the identified trends and to predict future directions for project management research.
ToolTango: Common sense Generalization in Predicting Sequential Tool Interactions for Robot Plan Synthesis

Robots assisting us in environments such as factories or homes must learn to make use of objects as tools to perform tasks, for instance, using a tray to carry objects. We consider the problem of learning common sense knowledge of when a tool may be useful and how its use may be composed with other tools to accomplish a high-level task instructed by a human. Specifically, we introduce a novel neural model, termed TOOLTANGO, that first predicts the next tool to be used, and then uses this information to predict the next action. We show that this joint model can inform learning of a fine-grained policy enabling the robot to use a particular tool in sequence and adds a significant value in making the model more accurate. TOOLTANGO encodes the world state, comprising objects and symbolic relationships between them, using a graph neural network and is trained using demonstrations from human teachers instructing a virtual robot in a physics simulator. The model learns to attend over the scene using knowledge of the goal and the action history, finally decoding the symbolic action to execute. Crucially, we address generalization to unseen environments where some known tools are missing, but unseen alternative tools are present. We show that by augmenting the representation of the environment with pre-trained embeddings derived from a knowledge-base, the model can generalize effectively to novel environments. Experimental results show at least 48.8-58.1% absolute improvement over the baselines in predicting successful symbolic plans for a simulated mobile manipulator in novel environments with unseen objects. This work takes a step in the direction of enabling robots to rapidly synthesize robust plans for complex tasks, particularly in novel settings.
Modelling Populations of Interaction Networks via Distance Metrics

Network data arises through observation of relational information between a collection of entities. Recent work in the literature has independently considered when (i) one observes a sample of networks, connectome data in neuroscience being a ubiquitous example, and (ii) the units of observation within a network are edges or paths, such as emails between people or a series of page visits to a website by a user, often referred to as interaction network data. The intersection of these two cases, however, is yet to be considered. In this paper, we propose a new Bayesian modelling framework to analyse such data. Given a practitioner-specified distance metric between observations, we define families of models through location and scale parameters, akin to a Gaussian distribution, with subsequent inference of model parameters providing reasoned statistical summaries for this non-standard data structure. To facilitate inference, we propose specialised Markov chain Monte Carlo (MCMC) schemes capable of sampling from doubly-intractable posterior distributions over discrete and multi-dimensional parameter spaces. Through simulation studies we confirm the efficacy of our methodology and inference scheme, whilst its application we illustrate via an example analysis of a location-based social network (LSBN) data set.
Enhancement of spin mixing conductance by $s$-$d$ orbital hybridization in heavy metals

In a magnetic multilayer, the spin transfer between localized magnetization dynamics and itinerant conduction spin arises from the interaction between a normal metal and an adjacent ferromagnetic layer. The spin-mixing conductance then governs the spin-transfer torques and spin pumping at the magnetic interface. Theoretical description of spin-mixing conductance at the magnetic interface often employs a single conduction-band model. However, there is orbital hybridization between conduction $s$ electron and localized $d$ electron of the heavy transition metal, in which the single conduction-band model is insufficient to describe the $s$-$d$ orbital hybridization. In this work, using the generalized Anderson model, we estimate the spin-mixing conductance that arises from the $s$-$d$ orbital hybridization. We find that the orbital hybridization increases the magnitude of the spin-mixing conductance.
A compact four‐element MIMO antenna for WLAN applications

This article presents a compact four‐element multiple‐input multiple‐output (MIMO) antenna for wireless local area network applications. The overall volume of the proposed MIMO antenna is 26 mm × 26 mm × 0.8 mm and the edge‐to‐edge distance is 4 mm. The MIMO antenna operates at 5.6 to 5.8 GHz frequency band with isolation greater than 15.4 dB. The envelope correlation coefficient and the channel capacity loss for the proposed MIMO antenna are within the acceptable limits in the operating frequency band. The simulated results are verified with fabrication and measurements.
Bear the Query in Mind: Visual Grounding with Query-conditioned Convolution

Visual grounding is a task that aims to locate a target object according to a natural language expression. As a multi-modal task, feature interaction between textual and visual inputs is vital. However, previous solutions mainly handle each modality independently before fusing them together, which does not take full advantage of relevant textual information while extracting visual features. To better leverage the textual-visual relationship in visual grounding, we propose a Query-conditioned Convolution Module (QCM) that extracts query-aware visual features by incorporating query information into the generation of convolutional kernels. With our proposed QCM, the downstream fusion module receives visual features that are more discriminative and focused on the desired object described in the expression, leading to more accurate predictions. Extensive experiments on three popular visual grounding datasets demonstrate that our method achieves state-of-the-art performance. In addition, the query-aware visual features are informative enough to achieve comparable performance to the latest methods when directly used for prediction without further multi-modal fusion.
Environmental dependence of the molecular cloud lifecycle in 54 main sequence galaxies

The processes of star formation and feedback, regulating the cycle of matter between gas and stars on the scales of giant molecular clouds (GMCs; ∼100 pc), play a major role in governing galaxy evolution. Measuring the time-scales of GMC evolution is important to identify and characterise the specific physical mechanisms that drive this transition. By applying a robust statistical method to high-resolution CO and narrow-band Hα imaging from the PHANGS survey, we systematically measure the evolutionary timeline from molecular clouds to exposed young stellar regions on GMC scales, across the discs of an unprecedented sample of 54 star-forming main-sequence galaxies (excluding their unresolved centres). We find that clouds live for about 1 − 3 GMC turbulence crossing times (5 − 30 Myr) and are efficiently dispersed by stellar feedback within 1 − 5 Myr once the star-forming region becomes partially exposed, resulting in integrated star formation efficiencies of 1 − 8%. These ranges reflect physical galaxy-to-galaxy variation. In order to evaluate whether galactic environment influences GMC evolution, we correlate our measurements with average properties of the GMCs and their local galactic environment. We find several strong correlations that can be physically understood, revealing a quantitative link between galactic-scale environmental properties and the small-scale GMC evolution. Notably, the measured CO-visible cloud lifetimes become shorter with decreasing galaxy mass, mostly due to the increasing presence of CO-dark molecular gas in such environment. Our results represent a first step towards a comprehensive picture of cloud assembly and dispersal, which requires further extension and refinement with tracers of the atomic gas, dust, and deeply-embedded stars.
Fewer Errors, but More Stereotypes? The Effect of Model Size on Gender Bias

The size of pretrained models is increasing, and so is their performance on a variety of NLP tasks. However, as their memorization capacity grows, they might pick up more social biases. In this work, we examine the connection between model size and its gender bias (specifically, occupational gender bias). We measure bias in three masked language model families (RoBERTa, DeBERTa, and T5) in two setups: directly using prompt based method, and using a downstream task (Winogender). We find on the one hand that larger models receive higher bias scores on the former task, but when evaluated on the latter, they make fewer gender errors. To examine these potentially conflicting results, we carefully investigate the behavior of the different models on Winogender. We find that while larger models outperform smaller ones, the probability that their mistakes are caused by gender bias is higher. Moreover, we find that the proportion of stereotypical errors compared to anti-stereotypical ones grows with the model size. Our findings highlight the potential risks that can arise from increasing model size.
Riemannian CUR Decompositions for Robust Principal Component Analysis

Robust Principal Component Analysis (PCA) has received massive attention in recent years. It aims to recover a low-rank matrix and a sparse matrix from their sum. This paper proposes a novel nonconvex Robust PCA algorithm, coined Riemannian CUR (RieCUR), which utilizes the ideas of Riemannian optimization and robust CUR decompositions. This algorithm has the same computational complexity as Iterated Robust CUR, which is currently state-of-the-art, but is more robust to outliers. RieCUR is also able to tolerate a significant amount of outliers, and is comparable to Accelerated Alternating Projections, which has high outlier tolerance but worse computational complexity than the proposed method. Thus, the proposed algorithm achieves state-of-the-art performance on Robust PCA both in terms of computational complexity and outlier tolerance.