Miracle-dz/UniFAR-FTUs huggingface dataset:
Compact groups from semi-analytical models of galaxy formation – II. Different assembly channels

We study the formation of over 6000 compact groups (CGs) of galaxies identified in mock redshift-space galaxy catalogues built from semi-analytical models of galaxy formation (SAMs) run on the Millennium Simulations. We select CGs of four members in our mock SDSS galaxy catalogues and, for each CG, we trace back in time the real-space positions of the most massive progenitors of their four galaxies. By analysing the evolution of the distance of the galaxy members to the centre of mass of the group, we identify four channels of CG formation. The classification of these assembly channels is performed with an automatic recipe inferred from a preliminary visual inspection and based on the orbit of the galaxy with the fewest number of orbits. Most CGs show late assembly, with the last galaxy arriving on its first or second passage, while only 10–20 per cent form by the gradual contraction of their orbits by dynamical friction, and only a few per cent forming early with little subsequent contraction. However, a SAM from a higher resolution simulation leads to earlier assembly. Assembly histories of CGs also depend on cosmological parameters. At similar resolution, CGs assemble later in SAMs built on parent cosmological simulations of high density parameter. Several observed properties of mock CGs correlate with their assembly history: early-assembling CGs are smaller, with shorter crossing times, and greater magnitude gaps between their brightest two members, and their brightest galaxies have smaller spatial offsets and are more passive.
High‐resolution, ion‐beam processes for microstructure fabrication

Ion beams have increased usefulness for high‐resolution microstructure fabrication if they are patterned to small dimensions before they strike a target. First, results are presented of maskless micromachining, doping, and resist exposure with a scanning focused gallium ion beam of sub‐1000‐A diameter. Secondly, an ion‐beam‐transmission mask is described, and results are presented showing 1X replicated mask patterns with 0.6‐μm features that were exposed in PMMA resist by irradiating the mask with a conventional size 150‐kV proton beam. The potentials of ion‐beam lithography using masks and focused ion beams are discussed.
Nonlocal Variable-Strength Measurements of N Qubits Using GHZ-like Entanglement

The direct measurement of nonlocal properties of entangled quantum systems has been the subject of several recent experimental investigations. Of particular interest is the implementation of nonlocal measurements via indirect measurement schemes, which allow for greater flexibility in the control of the measurement strength. Building on previous results established in the bipartite case, we present a scheme to implement genuine nonlocal measurements of N-qubit systems with variable strength, using GHZ-like entangled qubit meters. This method can be applied to the joint measurement of commuting product observables, enabling us to distinguish between orthogonal nonlocal states, such as Bell states, with minimal disturbance and arbitrary resolution. An explicit relation between the overall measurement strength and the meter entanglement as quantified by the n-tangle is derived, opening the door to a new interpretation of the n-tangle as a resource for nonlocal measurements.
TSSOS: a Julia library to exploit sparsity for large-scale polynomial optimization

The Julia library TSSOS aims at helping polynomial optimizers to solve large-scale problems with sparse input data. The underlying algorithmic framework is based on exploiting correlative and term sparsity to obtain a new moment-SOS hierarchy involving potentially much smaller positive semidefinite matrices. TSSOS can be applied to numerous problems ranging from power networks to eigenvalue and trace optimization of noncommutative polynomials, involving up to tens of thousands of variables and constraints.
Curvature-driven homogeneous Dzyaloshinskii–Moriya interaction and emergent weak ferromagnetism in anisotropic antiferromagnetic spin chains

Chiral antiferromagnets are currently considered for broad range of applications in spintronics, spin-orbitronics and magnonics. In contrast to the established approach relying on materials screening, the anisotropic and chiral responses of low-dimensional antifferromagnets can be tailored relying on the geometrical curvature. Here, we consider an achiral, anisotropic antiferromagnetic spin chain and demonstrate that these systems possess geometry-driven effects stemming not only from the exchange interaction but also from the anisotropy. Peculiarly, the anisotropy-driven effects are complementary to the curvature effects stemming from the exchange interaction and rather strong as they are linear in curvature. These effects are responsible for the tilt of the equilibrium direction of vector order parameters and the appearance of the homogeneous Dzyaloshinskii-Moriya interaction. The latter is a source of the geometry-driven weak ferromagnetism emerging in curvilinear antiferromagnetic spin chains. Our findings provide a deeper fundamental insight into the physics of curvilinear antiferromagnets beyond the $\sigma$-model and offer an additional degree of freedom in the design of spintronic and magnonic devices.
Moment-Based Variational Inference for Stochastic Differential Equations

Existing deterministic variational inference approaches for diffusion processes use simple proposals and target the marginal density of the posterior. We construct the variational process as a controlled version of the prior process and approximate the posterior by a set of moment functions. In combination with moment closure, the smoothing problem is reduced to a deterministic optimal control problem. Exploiting the path-wise Fisher information, we propose an optimization procedure that corresponds to a natural gradient descent in the variational parameters. Our approach allows for richer variational approximations that extend to state-dependent diffusion terms. The classical Gaussian process approximation is recovered as a special case.
Recent Progresses and Future Directions of Lagging Heat Models in Thermodynamics and Bioheat Transfer

The accuracy of the classical heat conduction model, known as Fourier’s law, is highly questioned, dealing with the micro/nanosystems and biological tissues. In other words, the results obtained from the classical equations deviates from the available experimental data. It means that the continuum heat diffusion equation is insufficient and inappropriate for modelling heat transport in these cases. Consequently, the development of novel models to improve the results of the classical equation while being less computationally expensive and more simple to use is always a topic of interest. There are several techniques for modelling non-Fourier heat conduction. The Dual-phase-lag (DPL) model as an accurate modified constitutive equation replacing the Fourier law to simulate the heat transport in some special cases such as micro/nanoscales, ultra-fast laser-pulsed processes, living tissues, and carbon nanotube have been trendy. There has been a sharp increase in the amount of research on non-Fourier heat conduction in recent years. Several studies have been performed in the fields of thermoelasticity, thermodynamics, transistor modelling, and bioheat transport. In the present review, the most recent non-Fourier bioheat conduction works and the related thermodynamics background will be presented. The various mathematical tools, modelling different thermal therapies and relevant criticisms and disputes, will be discussed. Finally, the novel other possible studies will be suggested, and the roadmap to the future researches and challenges ahead will be drawn up.
Deep Colormap Extraction From Visualizations

This article presents a new approach based on deep learning to automatically extract colormaps from visualizations. After summarizing colors in an input visualization image as a Lab color histogram, we pass the histogram to a pre-trained deep neural network, which learns to predict the colormap that produces the visualization. To train the network, we create a new dataset of <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math><alternatives><mml:math><mml:mo>∼</mml:mo></mml:math><inline-graphic xlink:href="zeng-ieq1-3070876.gif"/></alternatives></inline-formula>64K visualizations that cover a wide variety of data distributions, chart types, and colormaps. The network adopts an atrous spatial pyramid pooling module to capture color features at multiple scales in the input color histograms. We then classify the predicted colormap as discrete or continuous, and refine the predicted colormap based on its color histogram. Quantitative comparisons to existing methods show the superior performance of our approach on both synthetic and real-world visualizations. We further demonstrate the utility of our method with two use cases, i.e., color transfer and color remapping.
Brain Signals to Rescue Aphasia, Apraxia and Dysarthria Speech Recognition

In this paper, we propose a deep learning-based algorithm to improve the performance of automatic speech recognition (ASR) systems for aphasia, apraxia, and dysarthria speech by utilizing electroencephalography (EEG) features recorded synchronously with aphasia, apraxia, and dysarthria speech. We demonstrate a significant decoding performance improvement by more than 50% during test time for isolated speech recognition task and we also provide preliminary results indicating performance improvement for the more challenging continuous speech recognition task by utilizing EEG features. The results presented in this paper show the first step towards demonstrating the possibility of utilizing non-invasive neural signals to design a real-time robust speech prosthetic for stroke survivors recovering from aphasia, apraxia, and dysarthria. Our aphasia, apraxia, and dysarthria speech-EEG data set will be released to the public to help further advance this interesting and crucial research.
2MASX J00423991 + 3017515: an offset active galactic nucleus in an interacting system

We present a spectroscopic and imaging study of an abnormal active galactic nucleus (AGN), 2MASX J00423991+3017515. This AGN is newly identified in the hard X-rays by the Swift BAT All-Sky survey and found in an edge-on disk galaxy interacting with a nearby companion. Here, we analyze the first optical spectra obtained for this system (taken in 2011 and 2016), high-resolution imaging taken with the Hubble Space Telescope and Chandra X-ray Observatory, and 1′′ imaging with the Very Large Array. Two unique properties are revealed: the peaks of the broad Balmer emission lines (associated with gas orbiting very near the supermassive black hole) are blue shifted from the corresponding narrow line emission and host galaxy absorption by 1540 km s−1, and the AGN is spatially displaced from the apparent center of its host galaxy by 3.8 kpc. We explore several scenarios to explain these features, along with other anomalies, and propose that 2MASX J00423991+3017515 may be an AGN with an unusually strong wind residing in a uniquely configured major merger, or that it is an AGN recoiling from either a gravitational “slingshot” in a three-body interaction or from a kick due to the asymmetric emission of gravitational waves following the coalescence of two progenitor supermassive black holes.
Automatic evaluation of human oocyte developmental potential from microscopy images

Infertility is becoming an issue for an increasing number of couples. The most common solution, in vitro fertilization, requires embryologists to carefully examine light microscopy images of human oocytes to determine their developmental potential. We propose an automatic system to improve the speed, repeatability, and accuracy of this process. We first localize individual oocytes and identify their principal components using CNN (U-Net) segmentation. Next, we calculate several descriptors based on geometry and texture. The final step is an SVM classifier. Both the segmentation and classification training is based on expert annotations. The presented approach leads to a classification accuracy of 70%.
EKMP: Generalized Imitation Learning with Adaptation, Nonlinear Hard Constraints and Obstacle Avoidance

As a user-friendly and straightforward solution for robot trajectory generation, imitation learning has been viewed as a vital direction in the context of robot skill learning. In contrast to unconstrained imitation learning which ignores possible internal and external constraints arising from environments and robot kinematics/dynamics, recent works on constrained imitation learning allow for transferring human skills to unstructured scenarios, further enlarging the application domain of imitation learning. While various constraints have been studied, e.g., joint limits, obstacle avoidance and plane constraints, the problem of nonlinear hard constraints has not been well-addressed. In this paper, we propose extended kernelized movement primitives (EKMP) to cope with most of the key problems in imitation learning, including nonlinear hard constraints. Specifically, EKMP is capable of learning the probabilistic features of multiple demonstrations, adapting the learned skills towards arbitrary desired points in terms of joint position and velocity, avoiding obstacles at the level of robot links, as well as satisfying arbitrary linear and nonlinear, equality and inequality hard constraints. Besides, the connections between EKMP and state-of-the-art motion planning approaches are discussed. Several evaluations including the planning of joint trajectories for a 7-DoF robotic arm are provided to verify the effectiveness of our framework.
Sandglasset: A Light Multi-Granularity Self-Attentive Network for Time-Domain Speech Separation

One of the leading single-channel speech separation (SS) models is based on a TasNet with a dual-path segmentation technique, where the size of each segment remains unchanged throughout all layers. In contrast, our key finding is that multi-granularity features are essential for enhancing contextual modeling and computational efficiency. We introduce a self-attentive network with a novel sandglass-shape, namely Sandglasset, which advances the state-of-the-art (SOTA) SS performance at significantly smaller model size and computational cost. Forward along each block inside Sandglasset, the temporal granularity of the features gradually becomes coarser until reaching half of the network blocks, and then successively turns finer towards the raw signal level. We also unfold that residual connections between features with the same granularity are critical for preserving information after passing through the bottleneck layer. Experiments show our Sandglasset with only 2.3M parameters has achieved the best results on two benchmark SS datasets – WSJ0-2mix and WSJ0-3mix, where the SI-SNRi scores have been improved by absolute 0.6 dB and 2.4 dB, respectively, comparing to the prior SOTA results.
Generative chemical transformer: attention makes neural machine learn molecular geometric structures via text

s Chemical formula is an artificial language that expresses molecules as text. Neural machines that have learned chemical language can be used as a tool for inverse molecular design. Here, we propose a neural machine that creates molecules that meet some desired conditions based on a deep understanding of chemical language (generative chemical Transformer, GCT). Attention-mechanism in GCT allows a deeper understanding of molecular structures, beyond the limitations of chemical language itself that cause semantic discontinuity, by paying attention to characters sparsely. We investigate the significance of language models to inverse molecular design problems by quantitatively evaluating the quality of generated molecules. GCT generates highly realistic chemical strings that satisfy both a chemical rule and grammars of a language. Molecules parsed from generated strings simultaneously satisfy the multiple target properties and are various for a single condition set. GCT generates de novo molecules, and this is done in a short time that human experts cannot. These advances will contribute to improving the quality of human life by accelerating the process of desired material discovery.
Recovery from self-assembly: a composite material for lithium–sulfur batteries

A highly ordered mesoporous carbon, AlCMK, with an assembly of carbon rods and a bimodal pore system is used for sulfur encapsulation, and the AlCMK/S composite is observed to be able to accommodate volume expansion during a discharge process and recover its original structure when recharged. It is proved that the assembly structure of AlCMK makes it “breath” during the redox reaction of lithium and sulfur. Such a novel ability greatly benefits the maintenance of electrode construction during a repeated discharge–charge process. Moreover, a long heating time (e.g. 20 h) at 300 °C in a two-step melt diffusion method is found to contribute to the uniform dispersion of sulfur in the AlCMK carbon matrix. Using this featured composite, a Li–S cell retains 627 mA h g−1 after 450 cycles at 0.1 C-rate with a coulombic efficiency close to 100% and also shows good rate capability up to 5 C-rate, demonstrating a significant improvement of reversible capacity and cycle stability of Li–S cells.
Instrumental variables, spatial confounding and interference

Unobserved spatial confounding variables are prevalent in environmental and ecological applications where the system under study is complex and the data are often observational. Instrumental variables (IVs) are a common way to address unobserved confounding; however, the efficacy of using IVs on spatial confounding is largely unknown. This paper explores the effectiveness of IVs in this situation -- with particular attention paid to the spatial scale of the instrument. We show that, in case of spatially-dependent treatments, IVs are most effective when they vary at a finer spatial resolution than the treatment. We investigate IV performance in extensive simulations and apply the model in the example of long term trends in the air pollution and cardiovascular mortality in the United States over 1990-2010. Finally, the IV approach is also extended to the spatial interference setting, in which treatments can affect nearby responses.
Generalization Through Hand-Eye Coordination: An Action Space for Learning Spatially-Invariant Visuomotor Control

Imitation Learning (IL) is an effective framework to learn visuomotor skills from offline demonstration data. However, IL methods often fail to generalize to new scene configurations not covered by training data. On the other hand, humans can manipulate objects in varying conditions. Key to such capability is hand-eye coordination, a cognitive ability that enables humans to adaptively direct their movements at task-relevant objects and be invariant to the objects’ absolute spatial location. In this work, we present a learnable action space, Hand-eye Action Networks (HAN) that learns coordinated hand-eye movements from human teleoperated demonstrations. Through a set of challenging multi-stage manipulation tasks, we show that a visuomotor policy equipped with HAN is able to inherit the key spatial invariance property of handeye coordination and achieve generalization to new scene configurations. Additional materials available at https://sites.google. com/stanford.edu/han
Learning Monopoly Gameplay: A Hybrid Model-Free Deep Reinforcement Learning and Imitation Learning Approach

—Learning how to adapt and make real-time informed decisions in dynamic and complex environments is a challenging problem. To learn this task, Reinforcement Learning (RL) relies on an agent interacting with an environment and learning through trial and error to maximize the cumulative sum of rewards received by it. In multi-player Monopoly game, players have to make several decisions every turn which involves complex actions, such as making trades. This makes the decision-making harder and thus, introduces a highly complicated task for an RL agent to play and learn its winning strategies. In this paper, we introduce a Hybrid Model-Free Deep RL (DRL) approach that is capable of playing and learning winning strategies of the popular board game, Monopoly. To achieve this, our DRL agent (1) starts its learning process by imitating a rule-based agent (that resembles the human logic) to initialize its policy, (2) learns the successful actions, and improves its policy using DRL. Experimental results demonstrate an intelligent behavior of our proposed agent as it shows high win rates against different types of agent-players.
Polynesia: Enabling Effective Hybrid Transactional/Analytical Databases with Specialized Hardware/Software Co-Design

An exponential growth in data volume, combined with increasing demand for real-time analysis (i.e., using the most recent data), has resulted in the emergence of database systems that concurrently support transactions and data analytics. These hybrid transactional and analytical processing (HTAP) database systems can support real-time data analysis without the high costs of synchronizing across separate single-purpose databases. Unfortunately, for many applications that perform a high rate of data updates, state-of-the-art HTAP systems incur significant drops in transactional (up to 74.6%) and/or analytical (up to 49.8%) throughput compared to performing only transactions or only analytics in isolation, due to (1) data movement between the CPU and memory, (2) data update propagation, and (3) consistency costs. We propose Polynesia, a hardware-software co-designed system for in-memory HTAP databases. Polynesia (1) divides the HTAP system into transactional and analytical processing islands, (2) implements custom algorithms and hardware to reduce the costs of update propagation and consistency, and (3) exploits processing-in-memory for the analytical islands to alleviate data movement. Our evaluation shows that Polynesia outperforms three state-of-the-art HTAP systems, with average transactional/analytical throughput improvements of 1.70X/3.74X, and reduces energy consumption by 48% over the prior lowest-energy system.
Hierarchical Inducing Point Gaussian Process for Inter-domain Observations

We examine the general problem of inter-domain Gaussian Processes (GPs): problems where the GP realization and the noisy observations of that realization lie on different domains. When the mapping between those domains is linear, such as integration or differentiation, inference is still closed form. However, many of the scaling and approximation techniques that our community has developed do not apply to this setting. In this work, we introduce the hierarchical inducing point GP (HIP-GP), a scalable inter-domain GP inference method that enables us to improve the approximation accuracy by increasing the number of inducing points to the millions. HIP-GP, which relies on inducing points with grid structure and a stationary kernel assumption, is suitable for low-dimensional problems. In developing HIP-GP, we introduce (1) a fast whitening strategy, and (2) a novel preconditioner for conjugate gradients which can be helpful in general GP settings. Our code is available at https: //github.com/cunningham-lab/hipgp.