Miracle-dz/UniFAR-FTUs huggingface dataset:
A tale of two sites – I. Inferring the properties of minihalo-hosted galaxies from current observations

The very first galaxies that started the cosmic dawn likely resided in so-called ‘minihaloes’, with masses of ∼105–$10^8{\, {\rm M}_\odot }$, accreting their gas from the intergalactic medium through H2 cooling. Such molecularly cooled galaxies (MCGs) mostly formed in pristine environments, hosted massive, metal-free stars, and were eventually sterilized by the build-up of a disassociating (Lyman–Werner; LW) background. Therefore, their properties might be very different from the galaxies we see in the later Universe. Although MCGs are probably too faint to be observed directly, we could nevertheless infer their properties from the imprint they leave in the cosmic 21-cm signal. Here we quantify this imprint by extending the public simulation code 21cmFAST to allow for a distinct population of MCGs. We allow MCGs to have different properties from other galaxies, including unique scaling relations for their stellar-to-halo mass ratios, ionizing escape fractions, and spectral energy distributions. We track inhomogeneous recombinations, disassociative LW feedback, and photoheating from reionization. After demonstrating how MCGs can shape the 21-cm signal, we explore to what extent current observations can already place constraints on their properties. The cosmic microwave background optical depth from Planck sets an upper limit on the product of the ionizing escape fraction and the stellar mass in MCGs. When including also the timing of the putative EDGES absorption signal, we find an additional strong degeneracy between the stellar mass and the X-ray luminosity of MCGs. If proven to be of cosmic origin, the timing of the EDGES signal would have been set by MCGs.
Mobility and Safety Benefits of Connectivity in CACC Vehicle Strings

In this paper, we re-examine the notion of string stability as it relates to safety by providing an upper bound on the maximum spacing error of any vehicle in a homogeneous platoon in terms of the input of the leading vehicle. We reinforce our previous work on lossy CACC platoons by accommodating for burst-noise behavior in the V2V link. Further, through Monte Carlo type simulations, we demonstrate that connectivity can enhance traffic mobility and safety in a CACC string even when the deceleration capabilities of the vehicles in the platoon are heterogeneous.
Multiscale structural complexity of natural patterns

Significance Structural complexity of patterns, systems, and processes is a very basic and intuitively clear concept in human’s perception of reality that is very difficult to describe quantitatively. A demand in a mathematical notion that properly reflects complexity of hierarchical nonrandom structures exists in many areas of science, from geology to social sciences. Here, we propose an easy to compute, robust, and universal definition of complexity based on interscale dissimilarity of patterns. Using classical magnetic patterns as an example, we demonstrate that our approach leads to maximization of complexity for the most visually nontrivial patterns and can be used to detect phase transitions with high accuracy, making it a promising tool for studying pattern formation in a variety of systems. Complexity of patterns is key information for human brain to differ objects of about the same size and shape. Like other innate human senses, the complexity perception cannot be easily quantified. We propose a transparent and universal machine method for estimating structural (effective) complexity of two-dimensional and three-dimensional patterns that can be straightforwardly generalized onto other classes of objects. It is based on multistep renormalization of the pattern of interest and computing the overlap between neighboring renormalized layers. This way, we can define a single number characterizing the structural complexity of an object. We apply this definition to quantify complexity of various magnetic patterns and demonstrate that not only does it reflect the intuitive feeling of what is “complex” and what is “simple” but also, can be used to accurately detect different phase transitions and gain information about dynamics of nonequilibrium systems. When employed for that, the proposed scheme is much simpler and numerically cheaper than the standard methods based on computing correlation functions or using machine learning techniques.
Transition from Ordered Pinched to Warped Magnetic Field on a 100 au Scale in the Class 0 Protostar B335

We present our observational results of the 0.87 mm polarized dust emission in the Class 0 protostar B335 obtained with the Atacama Large Millimeter/submillimeter Array (ALMA) at a 0. ″2 (20 au) resolution. We compared our data at 0.87 mm with those at 1.3 mm from the ALMA archive. The observed polarization orientations at the two wavelengths are consistent within the uncertainty, and the polarization percentages are systematically higher at 1.3 mm than 0.87 mm by a factor of ∼1.7, suggesting that the polarized emission originates from magnetically aligned dust grains. We inferred the magnetic field orientations from the observed polarization orientations. We found that the magnetic field changes from ordered and highly pinched to more complicated and asymmetric structures within the inner 100 au scale of B335, and the magnetic field connects to the center along the equatorial plane as well as along the directions that are ∼40°–60° from the equatorial plane. We performed nonideal MHD simulations of collapsing dense cores. We found that similar magnetic field structures appear in our simulations of dense cores with the magnetic field and rotational axis slightly misaligned by 15° but not in those with the aligned magnetic field and rotational axis. Our results suggest that the midplane of the inner envelope within the inner 100 au scale of B335 could be warped because of the misaligned magnetic field and rotational axis, and the magnetic field could be dragged by the warped accretion flows.
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift

Adversarial learning has demonstrated good performance in the unsupervised domain adaptation setting, by learning domain-invariant representations that perform well on the source domain. However, recent work has underlined limitations of existing methods in the presence of mismatched label distributions between the source and target domains. In this paper, we extend a recent upper-bound on the performance of adversarial domain adaptation to multi-class classification and more general discriminators. We then propose generalized label shift (GLS) as a way to improve robustness against mismatched label distributions. GLS states that, conditioned on the label, there exists a representation of the input that is invariant between the source and target domains. Under GLS, we provide theoretical guarantees on the transfer performance of any classifier. We also devise necessary and sufficient conditions for GLS to hold. The conditions are based on the estimation of the relative class weights between domains and on an appropriate reweighting of samples. Guided by our theoretical insights, we modify three widely used algorithms, JAN, DANN and CDAN and evaluate their performance on standard domain adaptation tasks where our method outperforms the base versions. We also demonstrate significant gains on artificially created tasks with large divergences between their source and target label distributions.
Lung Infection Quantification of COVID-19 in CT Images with Deep Learning

CT imaging is crucial for diagnosis, assessment and staging COVID-19 infection. Follow-up scans every 3-5 days are often recommended for disease progression. It has been reported that bilateral and peripheral ground glass opacification (GGO) with or without consolidation are predominant CT findings in COVID-19 patients. However, due to lack of computerized quantification tools, only qualitative impression and rough description of infected areas are currently used in radiological reports. In this paper, a deep learning (DL)-based segmentation system is developed to automatically quantify infection regions of interest (ROIs) and their volumetric ratios w.r. t. the lung. The performance of the system was evaluated by comparing the automatically segmented infection regions with the manually-delineated ones on 300 chest CT scans of 300 COVID-19 patients. For fast manual delineation of training samples and possible manual intervention of automatic results, a human-in-the-loop (HITL) strategy has been adopted to assist radiologists for infection region segmentation, which dramatically reduced the total segmentation time to 4 minutes after 3 iterations of model updating. The average Dice simiarility coefficient showed 91.6% agreement between automatic and manual infaction segmentations, and the mean estimation error of percentage of infection (POI) was 0.3% for the whole lung. Finally, possible applications, including but not limited to analysis of follow-up CT scans and infection distributions in the lobes and segments correlated with clinical findings, were discussed.
DymSLAM: 4D Dynamic Scene Reconstruction Based on Geometrical Motion Segmentation

Most SLAM (Simultaneous Localization and Mapping) algorithms are based on the assumption that the scene is static. However, in practice, most real scenes usually contain moving objects. In this letter, we introduce DymSLAM, a dynamic stereo visual SLAM system being capable of reconstructing a 4D (3D + time) dynamic scene with rigid moving objects. Unlike previous attempts that have considered moving objects as outliers and ignored them, DymSLAM obtains the 6DoF motion trajectory and 3D models about the dynamic objects. We segment motion models of different moving objects by a multi-motion segmentation approach and obtain the accurate masks of moving objects. Besides ego-motion, our system can obtain the 4D (3D + time) model and 6DoF trajectory of the moving object in the global reference frame while simultaneously reconstructing the dense map of the static background. Meanwhile, DymSLAM does not rely on semantic cues or prior knowledge and is suitable for unknown rigid objects. We conducted experiments in a real-world indoor environment where both the camera and the objects were moving in a wide range. The results proved that our proposed method is a state-of-the-art SLAM system for use in this dynamic environment.
Lagrangian Neural Networks

Accurate models of the world are built upon notions of its underlying symmetries. In physics, these symmetries correspond to conservation laws, such as for energy and momentum. Yet even though neural network models see increasing use in the physical sciences, they struggle to learn these symmetries. In this paper, we propose Lagrangian Neural Networks (LNNs), which can parameterize arbitrary Lagrangians using neural networks. In contrast to models that learn Hamiltonians, LNNs do not require canonical coordinates, and thus perform well in situations where canonical momenta are unknown or difficult to compute. Unlike previous approaches, our method does not restrict the functional form of learned energies and will produce energy-conserving models for a variety of tasks. We test our approach on a double pendulum and a relativistic particle, demonstrating energy conservation where a baseline approach incurs dissipation and modeling relativity without canonical coordinates where a Hamiltonian approach fails. Finally, we show how this model can be applied to graphs and continuous systems using a Lagrangian Graph Network, and demonstrate it on the 1D wave equation.
Circulation Control for Faster Minimum Cost Flow in Unit-Capacity Graphs

We present an $m^{4/3+o(1)}\log W$-time algorithm for solving the minimum cost flow problem in graphs with unit capacity, where $W$ is the maximum absolute value of any edge weight. For sparse graphs, this improves over the best known running time for this problem and, by well-known reductions, also implies improved running times for the shortest path problem with negative weights, minimum cost bipartite $b$-matching when $\Vert b\Vert_{1}=O(m)$, and recovers the running time of the currently fastest algorithm for maximum flow in graphs with unit capacities (Liu-Sidford, 2020). Our algorithm relies on developing an interior point method-based framework which acts on the space of circulations in the underlying graph. From the combinatorial point of view, this framework can be viewed as iteratively improving the cost of a suboptimal solution by pushing flow around circulations. These circulations are derived by computing a regularized version of the standard Newton step, which is partially inspired by previous work on the unit-capacity maximum flow problem (Liu-Sidford, 2019), and subsequently refined based on the very recent progress on this problem (Liu-Sidford, 2020). The resulting step problem can then be computed efficiently using the recent work on $\ell_{p}$-norm minimizing flows (Kyng-Peng-Sachdeva-Wang, 2019). We obtain our faster algorithm by combining this new step primitive with a customized preconditioning method, which aims to ensure that the graph on which these circulations are computed has sufficiently large conductance.
Torsional Anomalies and Bulk-Dislocation Correspondence in Weyl Systems

Based on the supersymmetric quantum mechanical approach, we have systematically studied both the $U\left(1\right)$ gauge anomaly and the diffeomorphism anomaly in Weyl systems with torsion, curvature and external electromagnetic fields. These anomalies relate to the chiral current (or current) non-conservation and chiral energy-momentum (or energy-momentum) non-conservation, respectively, which can be applied to the $^{3}\text{He-A}$ phase, the chiral superconductors and the Weyl semimetals with dislocations and disclinations. In sharp difference with other anomalies, there exist torsional anomalies depending on the position of Weyl nodes in the energy-momentum space. These anomalies originate from particles pumped up through the Weyl nodes and they are thus insensitive to the ultra-violet physics, while the Nieh-Yan anomaly is from the particle inflow through the ultra-violet cut-off. The current non-conservation as well as the energy-momentum non-conservation are found, which stem from the zero modes trapped in the dislocations and they can be understood from the Callan-Harvey mechanism. Finally, by comparing our results with the well-established momentum anomaly in the $^{3}\text{He-A}$ phase, the Nieh-Yan term as well as other cut-off dependent terms are shown to be negligible, because the ratio between the Lorentz symmetry breaking scale and the chemical potential is of order $10^{-5}$.
CMetric: A Driving Behavior Measure using Centrality Functions

We present a new measure, CMetric, to classify driver behaviors using centrality functions. Our formulation combines concepts from computational graph theory and social traffic psychology to quantify and classify the behavior of human drivers. CMetric is used to compute the probability of a vehicle executing a driving style, as well as the intensity used to execute the style. Our approach is designed for realtime autonomous driving applications, where the trajectory of each vehicle or road-agent is extracted from a video. We compute a dynamic geometric graph (DGG) based on the positions and proximity of the road-agents and centrality functions corresponding to closeness and degree. These functions are used to compute the CMetric based on style likelihood and style intensity estimates. Our approach is general and makes no assumption about traffic density, heterogeneity, or how driving behaviors change over time. We present an algorithm to compute CMetric and demonstrate its performance on real-world traffic datasets. To test the accuracy of CMetric, we introduce a new evaluation protocol (called "Time Deviation Error") that measures the difference between human prediction and the prediction made by CMetric.
Predefined-Time Consensus of High-Order Multi-Agent Systems

This paper addresses the distributed predefined-time leader-following consensus problem for a class of high-order multi-agent systems (MAS) with perturbed nonlinear agents’ dynamics and where the topology of the network contains a directed spanning tree, with the leader as the root. The proposed control method exhibits three main advantages: first, to our best knowledge, it is the first time that predefined-time convergence in a consensus problem is achieved for agents with high-order nonlinear dynamics, using a robust leader-following protocol, which allows an effective rejection of matched disturbances in the agents model. Second, the proposed controller provides continuous and smooth control signals of lower magnitude than existing approaches. Third, the proposed consensus protocol does not have parameters to be adjusted depending on the connectivity of the considered communication graph.
A Non-Negative Matrix Factorization-Based Framework for the Analysis of Multi-Class Time-Series Single-Cell RNA-Seq Data

The development of single-cell RNA sequencing (scRNA-seq) has enabled gene expression to be quantified at single-cell resolution. Such advancement is expected to solve important issues that bulk RNA sequencing could not fully answer, such as inferring cell population heterogeneity, genetic variability of cells, detecting rare cell types, accurately predicting cell states and their localization. However, analyzing such large scale data, especially when they are sampled at multiple time points, brings new challenges in data mining informative genes, compared to single snapshot samples. It becomes even more complicated when gene expression patterns are to be mined from time-series scRNA-seq datasets generated from multiple conditions, which will constitute a data with gene, condition and time dimensions. Here, we focused on detecting gene expression patterns that well capture the underlying biological differences between time-series scRNA-seq datasets of three different types of stem cells. The gene expression profile of 2,128 time-series scRNA-seq samples from long-term hematopoietic stem cells (LT-HSC) and two of its progenitor cell types were analyzed using our framework. We have successfully detected condition specific feature genes that were able to achieve 90.03% classification accuracy between the three cell types. Investigating the genes and clusters detected by our framework, we found that cell cycle related genes showed significantly high variance between the three cell types. Such results and transcriptomic characters detected from our analysis were consistent with the original study. Collectively, the framework was able to successfully detect biological meaningful gene sets and expression patterns from multi-condition time-series scRNA-seq samples.
Green Elevator Scheduling Based on IoT Communications

In this paper, we propose an energy-saving elevator scheduling algorithm to reduce the car moving steps to achieve motor energy saving and green wireless communications. The proposed algorithm consisting of six procedures can attain fewer Internet of Things (IoT) message exchanges (i.e. communication transmissions) between the Scheduler subsystem and the Car subsystem via the core function <italic>AssignCar</italic>(<inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula>). The function <italic>AssignCar</italic>(<inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula>) is capable of assigning a request to the nearest car through car search globally. From the emulation results for four cars, this work shows that the proposed algorithm outperforms the previous work named as aggressive car scheduling with initial car distribution (ACSICD) algorithm with energy consumption reductions by 49.43%, 47.68%, 37.89%, and 47.65% for up-peak, inter-floor, down-peak, and all-day request patterns, respectively.
Least Squares Estimation of a Monotone Quasiconvex Regression Function

We develop a new approach for the estimation of a multivariate function based on the economic axioms of monotonicity and quasiconvexity. We prove the existence of the nonparametric least squares estimator (LSE) for a monotone and quasiconvex function and provide two characterizations for it. One of these characterizations is useful from the theoretical point of view, while the other helps in the computation of the estimator. We show that the LSE is almost surely unique and is the solution to a mixed-integer quadratic optimization problem. We prove consistency and find finite sample risk bounds for the LSE under both fixed lattice and random design settings for the covariates. We illustrate the superior performance of the LSE against existing estimators via simulation. Finally, we use the LSE to estimate the production function for the Japanese plywood industry and the cost function for hospitals across the US.
Pricing Interest Rate Derivatives under Volatility Uncertainty

We study the pricing of contracts in fixed income markets in the presence of volatility uncertainty. We consider an arbitrage-free bond market under volatility uncertainty. The uncertainty about the volatility is modeled by a G-Brownian motion, which drives the forward rate dynamics. The absence of arbitrage is ensured by a drift condition. In such a setting we obtain a sublinear pricing measure for additional contracts. Similar to the forward measure approach, we define a forward sublinear expectation to simplify the valuation of cashflows. Under the forward sublinear expectation, we obtain a robust version of the expectations hypothesis and a valuation method for bond options. With these tools, we derive robust pricing rules for the most common interest rate derivatives: fixed coupon bonds, floating rate notes, interest rate swaps, swaptions, caps, and floors. For fixed coupon bonds, floating rate notes, and interest rate swaps, we obtain a single price, which is the same as in traditional models. For swaptions, caps, and floors, we obtain a range of prices, which is bounded by the prices from traditional models with the highest and lowest possible volatility. Due to these pricing formulas, the model naturally exhibits unspanned stochastic volatility.
3-D Quasi-Recurrent Neural Network for Hyperspectral Image Denoising

In this article, we propose an alternating directional 3-D quasi-recurrent neural network for hyperspectral image (HSI) denoising, which can effectively embed the domain knowledge—structural spatiospectral correlation and global correlation along spectrum (GCS). Specifically, 3-D convolution is utilized to extract structural spatiospectral correlation in an HSI, while a quasi-recurrent pooling function is employed to capture the GCS. Moreover, the alternating directional structure is introduced to eliminate the causal dependence with no additional computation cost. The proposed model is capable of modeling spatiospectral dependence while preserving the flexibility toward HSIs with an arbitrary number of bands. Extensive experiments on HSI denoising demonstrate significant improvement over the state-of-the-art under various noise settings, in terms of both restoration accuracy and computation time. Our code is available at https://github.com/Vandermode/QRNN3D.