Miracle-dz/UniFAR-FTUs huggingface dataset:
Electric field control of phonon angular momentum in perovskite BaTiO$_3$

We find that in BaTiO$_3$ the phonon angular momentum is dominantly pointing in directions perpendicular to the electrical polarization. Therefore, external electric field in ferroelectric BaTiO$_3$ does not control only the direction of electrical polarization, but also the direction of phonon angular momentum. This finding opens up the possibility for electric-field control of physical phenomena that rely on phonon angular momentum. We construct an intuitive model, based on our first-principles calculations, that captures the origin of the relationship between phonon angular momentum and electric polarization.
Incidence, scaling relations and physical conditions of ionized gas outflows in MaNGA

In this work, we investigate the strength and impact of ionised gas outflows within I ∼ 0.04 MaNGA galaxies. We find evidence for outflows in 322 galaxies (12% of the analysed lineemitting sample), 185 of which show evidence for AGN activity. Most outflows are centrally concentrated with a spatial extent that scales sublinearly with 'e. The incidence of outflows is enhanced at higher masses, central surface densities and deeper gravitational potentials, as well as at higher SFR and AGN luminosity. We quantify strong correlations between mass outflow rates and the mechanical drivers of the outflow of the form ¤ "out ∝ SFR0.97 and ¤ "out ∝ ! 0.55 AGN. We derive a master scaling relation describing the mass outflow rate of ionised gas as a function of "★, SFR, 'e and ! AGN. Most of the observed winds are anticipated to act as galactic fountains, with the fraction of galaxies with escaping winds increasing with decreasing potential well depth. We further investigate the physical properties of the outflowing gas finding evidence for enhanced attenuation in the outflow, possibly due to metal-enriched winds, and higher excitation compared to the gas in the galactic disk. Given that the majority of previous studies have focused on more extreme systems with higher SFRs and/or more luminous AGN, our study provides a unique view of the non-gravitational gaseous motions within ‘typical’ galaxies in the low-redshift Universe, where low-luminosity AGN and star formation contribute jointly to the observed outflow phenomenology.
S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning

Offline reinforcement learning proposes to learn policies from large collected datasets without interacting with the physical environment. These algorithms have made it possible to learn useful skills from data that can then be deployed in the environment in real-world settings where interactions may be costly or dangerous, such as autonomous driving or factories. However, current algorithms overfit to the dataset they are trained on and exhibit poor out-of-distribution generalization to the environment when deployed. In this paper, we study the effectiveness of performing data augmentations on the state space, and study 7 different augmentation schemes and how they behave with existing offline RL algorithms. We then combine the best data performing augmentation scheme with a state-of-the-art Q-learning technique, and improve the function approximation of the Q-networks by smoothening out the learned state-action space. We experimentally show that using this Surprisingly Simple Self-Supervision technique in RL (S4RL), we significantly improve over the current state-of-the-art algorithms on offline robot learning environments such as MetaWorld [1] and RoboSuite [2,3], and benchmark datasets such as D4RL [4].
Preventing extreme polarization of political attitudes

Significance Democracies require compromise. But compromise becomes almost impossible when voters are divided into diametrically opposed camps. The danger is that intolerance will grow, democratic norms will be undermined, and winners will be reluctant to let the losers ever regain power. To better understand how polarization can be prevented, or at least slowed, we developed a simple model in which people tend to be exposed to and attracted by views similar to their own, but are repulsed by views that are too dissimilar. The policy implications are described in terms of level of tolerance to other views, responsiveness to other views, exposure to dissimilar views, multiple ideological dimensions, economic self-interest, and external shocks. Extreme polarization can undermine democracy by making compromise impossible and transforming politics into a zero-sum game. “Ideological polarization”—the extent to which political views are widely dispersed—is already strong among elites, but less so among the general public [N. McCarty, Polarization: What Everyone Needs to Know, 2019, pp. 50–68]. Strong mutual distrust and hostility between Democrats and Republicans in the United States, combined with the elites’ already strong ideological polarization, could lead to increasing ideological polarization among the public. The paper addresses two questions: 1) Is there a level of ideological polarization above which polarization feeds upon itself to become a runaway process? 2) If so, what policy interventions could prevent such dangerous positive feedback loops? To explore these questions, we present an agent-based model of ideological polarization that differentiates between the tendency for two actors to interact (“exposure”) and how they respond when interactions occur, positing that interaction between similar actors reduces their difference, while interaction between dissimilar actors increases their difference. Our analysis explores the effects on polarization of different levels of tolerance to other views, responsiveness to other views, exposure to dissimilar actors, multiple ideological dimensions, economic self-interest, and external shocks. The results suggest strategies for preventing, or at least slowing, the development of extreme polarization.
Multipole Vortex Patch Equilibria for Active Scalar Equations

We study how a general steady configuration of finitely-many point vortices, with Newtonian interaction or generalized surface quasi-geostrophic interactions, can be desingularized into a steady configuration of vortex patches. The configurations can be uniformly rotating, uniformly translating, or completely stationary. Using a technique first introduced by Hmidi and Mateu \cite{Hmidi-Mateu} for vortex pairs, we reformulate the problem for the patch boundaries so that it no longer appears singular in the point-vortex limit. Provided the point vortex equilibrium is non-degenerate in a natural sense, solutions can then be constructed directly using the implicit function theorem, yielding asymptotics for the shape of the patch boundaries. As an application, we construct new families of asymmetric translating and rotating pairs, as well as stationary tripoles. We also show how the techniques can be adapted for highly symmetric configurations such as regular polygons, body-centered polygons and nested regular polygons by integrating the appropriate symmetries into the formulation of the problem.
Topical Language Generation using Transformers

Large-scale transformer-based language models (LMs) demonstrate impressive capabilities in open-text generation. However, controlling the generated text’s properties such as the topic, style, and sentiment is challenging and often requires significant changes to the model architecture or retraining and fine-tuning the model on new supervised data. This paper presents a novel approach for topical language generation (TLG) by combining a pre-trained LM with topic modeling information. We cast the problem using Bayesian probability formulation with topic probabilities as a prior, LM probabilities as the likelihood, and TLG probability as the posterior. In learning the model, we derive the topic probability distribution from the user-provided document’s natural structure. Furthermore, we extend our model by introducing new parameters and functions to influence the quantity of the topical features presented in the generated text. This feature would allow us to easily control the topical properties of the generated text. Our experimental results demonstrate that our model outperforms the state-of-the-art results on coherency, diversity, and fluency while being faster in decoding.
Upscaling of a Cahn–Hilliard Navier–Stokes model with precipitation and dissolution in a thin strip

Abstract We consider a phase-field model for the incompressible flow of two immiscible fluids. This model extends widespread models for two fluid phases by including a third, solid phase, which can evolve due to e.g. precipitation and dissolution. We consider a simple, two-dimensional geometry of a thin strip, which can still be seen as the representation of a single pore throat in a porous medium. Under moderate assumptions on the Péclet number and the capillary number, we investigate the limit case when the ratio between the width and the length of the strip goes to zero. In this way, and employing transversal averaging, we derive an upscaled model. The result is a multi-scale model consisting of the upscaled equations for the total flux and the ion transport, while the phase-field equation has to be solved in cell problems at the pore scale to determine the position of interfaces. We also investigate the sharp-interface limit of the multi-scale model, in which the phase-field parameter approaches 0. The resulting sharp-interface model consists only of Darcy-scale equations, as the cell problems can be solved explicitly. Notably, we find asymptotic consistency, that is, the upscaling process and the sharp-interface limit commute. We use numerical results to investigate the validity of the upscaling when discontinuities are formed in the upscaled model.
Self-supervised Text-to-SQL Learning with Header Alignment Training

Since we can leverage a large amount of unlabeled data without any human supervision to train a model and transfer the knowledge to target tasks, self-supervised learning is a de-facto component for the recent success of deep learning in various fields. However, in many cases, there is a discrepancy between a self-supervised learning objective and a task-specific objective. In order to tackle such discrepancy in Text-to-SQL task, we propose a novel self-supervised learning framework. We utilize the task-specific properties of Text-to-SQL task and the underlying structures of table contents to train the models to learn useful knowledge of the \textit{header-column} alignment task from unlabeled table data. We are able to transfer the knowledge to the supervised Text-to-SQL training with annotated samples, so that the model can leverage the knowledge to better perform the \textit{header-span} alignment task to predict SQL statements. Experimental results show that our self-supervised learning framework significantly improves the performance of the existing strong BERT based models without using large external corpora. In particular, our method is effective for training the model with scarce labeled data. The source code of this work is available in GitHub.
Imitation learning for variable speed motion generation over multiple actions

Robotic motion generation methods using machine learning have been studied. Bilateral control-based imitation learning can imitate human motions using force information. Using this method, variable speed motion generation that con-siders physical phenomena such as the inertia and friction can be achieved. However, the previous study focused on a simple reciprocating motion. To learn the complex relationship between the force and speed more accurately, it is necessary to learn multiple actions using many joints. In this paper, we propose a variable speed motion generation method for multiple motions. We considered four types of neural network models for the motion generation and determined the best model for multiple motions at variable speeds. Subsequently, we used the best model to evaluate the reproducibility of the task completion time for the input completion time command. The results revealed that the proposed method could change the task completion time according to the specified completion time command in multiple motions.
Deep Transfer Learning for Infectious Disease Case Detection Using Electronic Medical Records

During an infectious disease pandemic, it is critical to share electronic medical records or models (learned from these records) across regions. Applying one region's data/model to another region often have distribution shift issues that violate the assumptions of traditional machine learning techniques. Transfer learning can be a solution. To explore the potential of deep transfer learning algorithms, we applied two data-based algorithms (domain adversarial neural networks and maximum classifier discrepancy) and model-based transfer learning algorithms to infectious disease detection tasks. We further studied well-defined synthetic scenarios where the data distribution differences between two regions are known. Our experiments show that, in the context of infectious disease classification, transfer learning may be useful when (1) the source and target are similar and the target training data is insufficient and (2) the target training data does not have labels. Model-based transfer learning works well in the first situation, in which case the performance closely matched that of the data-based transfer learning models. Still, further investigation of the domain shift in real world research data to account for the drop in performance is needed.
Improved Coherence Index-Based Bound in Compressive Sensing

Within the compressive sensing (CS) paradigm, sparse signals can be reconstructed based on a reduced set of measurements, whereby reliability of the solution is determined by its uniqueness. With its mathematically tractable and feasible calculation, the coherence index is one of very few CS uniqueness metrics with considerable practical importance. We propose an improvement of the coherence-based uniqueness relation for the matching pursuit algorithms. Starting from a simple and intuitive derivation of the standard uniqueness condition, based on the coherence index, we derive a less conservative coherence index-based lower bound for signal sparsity. The results are generalized to the uniqueness condition of the $l_0$-norm minimization for a signal represented in two orthonormal bases.
MPU: Towards Bandwidth-abundant SIMT Processor via Near-bank Computing

With the growing number of data-intensive workloads, GPU, which is the state-of-the-art single-instruction-multiple-thread (SIMT) processor, is hindered by the memory bandwidth wall. To alleviate this bottleneck, previously proposed 3D-stacking near-bank computing accelerators benefit from abundant bank-internal bandwidth by bringing computations closer to the DRAM banks. However, these accelerators are specialized for certain application domains with simple architecture data paths and customized software mapping schemes. For general purpose scenarios, lightweight hardware designs for diverse data paths, architectural supports for the SIMT programming model, and end-to-end software optimizations remain challenging. To address these issues, we propose MPU (Memory-centric Processing Unit), the first SIMT processor based on 3D-stacking near-bank computing architecture. First, to realize diverse data paths with small overheads while leveraging bank-level bandwidth, MPU adopts a hybrid pipeline with the capability of offloading instructions to near-bank compute-logic. Second, we explore two architectural supports for the SIMT programming model, including a near-bank shared memory design and a multiple activated row-buffers enhancement. Third, we present an end-to-end compilation flow for MPU to support CUDA programs. To fully utilize MPU's hybrid pipeline, we develop a backend optimization for the instruction offloading decision. The evaluation results of MPU demonstrate 3.46x speedup and 2.57x energy reduction compared with an NVIDIA Tesla V100 GPU on a set of representative data-intensive workloads.
Population-Based Evolution Optimizes a Meta-Learning Objective

Meta-learning models, or models that learn to learn, have been a long-desired target for their ability to quickly solve new tasks. Traditional meta-learning methods can require expensive inner and outer loops, thus there is demand for algorithms that discover strong learners without explicitly searching for them. We draw parallels to the study of evolvable genomes in evolutionary systems -- genomes with a strong capacity to adapt -- and propose that meta-learning and adaptive evolvability optimize for the same objective: high performance after a set of learning iterations. We argue that population-based evolutionary systems with non-static fitness landscapes naturally bias towards high-evolvability genomes, and therefore optimize for populations with strong learning ability. We demonstrate this claim with a simple evolutionary algorithm, Population-Based Meta Learning (PBML), that consistently discovers genomes which display higher rates of improvement over generations, and can rapidly adapt to solve sparse fitness and robotic control tasks.
Deep Multiway Canonical Correlation Analysis For Multi-Subject Eeg Normalization

The normalization of brain recordings from multiple subjects responding to the natural stimuli is one of the key challenges in auditory neuroscience. The objective of this normalization is to transform the brain data in such a way as to remove the inter-subject redundancies and to boost the component related to the stimuli. In this paper, we propose a deep learning framework to improve the correlation of electroencephalography (EEG) data recorded from multiple subjects engaged in an audio listening task. The proposed model extends the linear multi-way canonical correlation analysis (CCA) for audio-EEG analysis using an auto-encoder network with a shared encoder layer. The model is trained to optimize a combined loss involving correlation and reconstruction. The experiments are performed on EEG data collected from subjects listening to natural speech and music. In these experiments, we show that the proposed deep multi-way CCA (DMCCA) based model significantly improves the correlations over the linear multi-way CCA approach with absolute improvements of 0.08 and 0.29 in terms of the Pearson correlation values for speech and music tasks respectively.
Machine Learning-Based Direct Solver for One-To-Many Problems on Temporal Shaping of Electron Beams

To control the temporal profile of an electron beam to meet requirements of various advanced scientific applications, a widely-used technique is to manipulate the dispersion terms which turns out to be one-to-many problems. Due to their intrinsic one-to-many property, current popular stochastic optimization approaches on temporal shaping are not very effective, for being trapped into local optima or suggesting only one solution. Here we propose a real-time solver for one-to-many problems of temporal shaping, with the aid of a semi-supervised machine learning method, the conditional generative adversarial network (CGAN). We demonstrate that the CGAN solver can learn the one-to-many dynamics and is able to accurately and quickly predict the required dispersion terms for different custom temporal profiles. This machine learning-based solver overcomes the limitation of the stochastic optimization methods and is expected to have the potential for wide applications to one-to-many problems in other scientific fields.
Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction

—Physical motion models offer interpretable pre- dictions for the motion of vehicles. However, some model parameters, such as those related to aero- and hydrodynamics, are expensive to measure and are often only roughly approximated reducing prediction accuracy. Recurrent neural networks achieve high prediction accuracy at low cost, as they can use cheap measurements collected during routine operation of the vehicle, but their results are hard to interpret. To precisely predict vehicle states without expensive measurements of phys- ical parameters, we propose a hybrid approach combining deep learning and physical motion models including a novel two-phase training procedure. We achieve interpretability by restricting the output range of the deep neural network as part of the hybrid model, which limits the uncertainty introduced by the neural network to a known quantity. We have evaluated our approach for the use case of ship and quadcopter motion. The results show that our hybrid model can improve model interpretability with no decrease in accuracy compared to existing deep learning approaches.
What is Multimodality?

The last years have shown rapid developments in the field of multimodal machine learning, combining e.g., vision, text or speech. In this position paper we explain how the field uses outdated definitions of multimodality that prove unfit for the machine learning era. We propose a new task-relative definition of (multi)modality in the context of multimodal machine learning that focuses on representations and information that are relevant for a given machine learning task. With our new definition of multimodality we aim to provide a missing foundation for multimodal research, an important component of language grounding and a crucial milestone towards NLU.
Narrow resonances revisited: Simplifying multidimensional constraints

: As we amass more LHC data, we continue to search for new and improved methods of visualizing search results, in ways that are as model-independent as possible. The simpliﬁed limits framework is an approach developed to recast limits on searches for narrow resonances in terms of products of branching ratios (BRs) corresponding to the resonance’s production and decay modes. In this work, we extend the simpliﬁed limits framework to a multidimensional parameter space of BRs, which can be used to unfold an ambiguity in the simpliﬁed parameter ζ introduced when more than one channel con-tributes to the production of the resonance. It is also naturally applicable to combining constraints from experimental searches with diﬀerent observed ﬁnal states. Constraints can be visualized in a three-dimensional space of branching ratios by employing ternary diagrams, triangle plots which utilize the inherent unitarity of the sum of the resonance’s BRs. To demonstrate this new methodology, we recast constraints from recent ATLAS searches in diboson ﬁnal states for spin-0, 1, and 2 narrow resonances into constraints on the resonance’s width-to-mass ratio and display them in the space of relevant branching ratios. We also demonstrate how to generalize the method to cases where more than three branching ratios are relevant by using N-simplex diagrams, and we suggest a broader application of the general method to digital data sets.
Preparation and characterization of high‐performance dehydrating pervaporation alginate membranes

In a previous work, sodium alginate dense membranes demonstrated a good combination of selectivity and permeation flux for dehydrating organic solvents by pervaporation. In this article, the pervaporation performances of alginate composite membranes has been investigated to find out the best condition of membrane formation and the optimum operating conditions. Some ultrafiltration membranes made of poly-(vinylidene fluoride), polyacrylonitrile, and hydrolyzed polyacrylonitrile—either commercially available or prepared in our laboratories—were used as supports for the composite membranes. Sodium alginate dense membranes, modified through ion exchange of sodium with multivalent metal ions (such as Al3+, Cr3+, Fe3+, and Mg2+) have also been prepared, and their permselectivities have been tested for the water–ethanol mixture. An interesting stability of the modified membranes in long-term operation is expected. © 1998 John Wiley & Sons, Inc. J Appl Polym Sci 68: 959–968, 1998