Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Low-Frequency Motion Control for Robust and Dynamic Robot Locomotion

Robotic locomotion is often approached with the goal of maximizing robustness and reactivity by increasing motion control frequency. We challenge this intuitive notion by demonstrating robust and dynamic locomotion with a learned motion controller executing at as low as 8 Hz on a real ANYmal C quadruped. The robot is able to robustly and repeatably achieve a high heading velocity of 1.5 ms-1, traverse uneven terrain, and resist unexpected external perturbations. We further present a comparative analysis of deep reinforcement learning (RL) based motion control policies trained and executed at frequencies ranging from 5 Hz to 200 Hz. We show that low-frequency policies are less sensitive to actuation latencies and variations in system dynamics. This is to the extent that a successful sim- to-real transfer can be performed even without any dynamics randomization or actuation modeling. We support this claim through a set of rigorous empirical evaluations. Moreover, to assist reproducibility, we provide the training and deployment code along with an extended analysis at https://ori-drs.github. io/lfmc/.
Diffusion Posterior Sampling for General Noisy Inverse Problems

Diffusion models have been recently studied as powerful generative inverse problem solvers, owing to their high quality reconstructions and the ease of combining existing iterative solvers. However, most works focus on solving simple linear inverse problems in noiseless settings, which significantly under-represents the complexity of real-world problems. In this work, we extend diffusion solvers to efficiently handle general noisy (non)linear inverse problems via approximation of the posterior sampling. Interestingly, the resulting posterior sampling scheme is a blended version of diffusion sampling with the manifold constrained gradient without a strict measurement consistency projection step, yielding a more desirable generative path in noisy settings compared to the previous studies. Our method demonstrates that diffusion models can incorporate various measurement noise statistics such as Gaussian and Poisson, and also efficiently handle noisy nonlinear inverse problems such as Fourier phase retrieval and non-uniform deblurring. Code available at https://github.com/DPS2022/diffusion-posterior-sampling
Learning to Explain Graph Neural Networks

Graph Neural Networks (GNNs) are a popular class of machine learning models. Inspired by the learning to explain (L2X) paradigm, we propose L2XGNN, a framework for explainable GNNs which provides faithful explanations by design. L2XGNN learns a mechanism for selecting explanatory subgraphs (motifs) which are exclusively used in the GNNs message-passing operations. L2XGNN is able to select, for each input graph, a subgraph with specific properties such as being sparse and connected. Imposing such constraints on the motifs often leads to more interpretable and effective explanations. Experiments on several datasets suggest that L2XGNN achieves the same classification accuracy as baseline methods using the entire input graph while ensuring that only the provided explanations are used to make predictions. Moreover, we show that L2XGNN is able to identify motifs responsible for the graph's properties it is intended to predict.
Human-AI Shared Control via Policy Dissection

Human-AI shared control allows human to interact and collaborate with AI to accomplish control tasks in complex environments. Previous Reinforcement Learning (RL) methods attempt the goal-conditioned design to achieve human-controllable policies at the cost of redesigning the reward function and training paradigm. Inspired by the neuroscience approach to investigate the motor cortex in primates, we develop a simple yet effective frequency-based approach called \textit{Policy Dissection} to align the intermediate representation of the learned neural controller with the kinematic attributes of the agent behavior. Without modifying the neural controller or retraining the model, the proposed approach can convert a given RL-trained policy into a human-interactive policy. We evaluate the proposed approach on the RL tasks of autonomous driving and locomotion. The experiments show that human-AI shared control achieved by Policy Dissection in driving task can substantially improve the performance and safety in unseen traffic scenes. With human in the loop, the locomotion robots also exhibit versatile controllable motion skills even though they are only trained to move forward. Our results suggest the promising direction of implementing human-AI shared autonomy through interpreting the learned representation of the autonomous agents. Demo video and code will be made available at https://metadriverse.github. io/policydissect.
On the general position numbers of maximal outerplanar graphs

A subset $R\subseteq V(G)$ of a graph $G$ is a general position set if any triple set $R_0$ of $R$ is non-geodesic in $G$, that is, no vertex of $R_0$ lies on any geodesic between the other two vertices of $R_0$ in $G$. Let $\mathcal{R}$ be the set of general position sets of a graph $G$. The general position number of a graph $G$, denoted by $gp(G)$, is defined as $gp(G)=\max\{|R|:R\in\mathcal{R}\}$. In this paper, we determine the bounds on the gp-numbers for any maximal outerplane graph and characterize the corresponding extremal graphs.
Working with Convex Responses: Antifragility from Finance to Oncology

We extend techniques and learnings about the stochastic properties of nonlinear responses from finance to medicine, particularly oncology, where it can inform dosing and intervention. We define antifragility. We propose uses of risk analysis for medical problems, through the properties of nonlinear responses (convex or concave). We (1) link the convexity/concavity of the dose-response function to the statistical properties of the results; (2) define “antifragility” as a mathematical property for local beneficial convex responses and the generalization of “fragility” as its opposite, locally concave in the tails of the statistical distribution; (3) propose mathematically tractable relations between dosage, severity of conditions, and iatrogenics. In short, we propose a framework to integrate the necessary consequences of nonlinearities in evidence-based oncology and more general clinical risk management.
State-dependent Trotter limits and their approximations

The Trotter product formula is a key instrument in numerical simulations of quantum systems. However, computers cannot deal with continuous degrees of freedom, such as the position of particles in molecules, or the amplitude of electromagnetic fields. It is therefore necessary to discretize these variables to make them amenable to digital simulations. Here, we give sufficient conditions to conclude the validity of this approximate discretized physics. Essentially, it depends on the state-dependent Trotter error, for which we establish explicit bounds that are also of independent interest.
Optical/IR studies of Be stars in NGC 6834 with emphasis on two specific stars

We present optical and infrared photometric and spectroscopic studies of two Be stars in the 70–80-Myr-old open cluster NGC 6834. NGC 6834(1) has been reported as a binary from speckle interferometric studies whereas NGC 6834(2) may possibly be a γ Cas-like variable. Infrared photometry and spectroscopy from the United Kingdom Infrared Telescope (UKIRT), and optical data from various facilities are combined with archival data to understand the nature of these candidates. High signal-to-noise near-IR spectra obtained from UKIRT have enabled us to study the optical depth effects in the hydrogen emission lines of these stars. We have explored the spectral classification scheme based on the intensity of emission lines in the H and K bands and contrasted it with the conventional classification based on the intensity of hydrogen and helium absorption lines. This work also presents hitherto unavailable UBV CCD photometry of NGC 6834, from which the evolutionary state of the Be stars is identified.
A Causal Approach to Detecting Multivariate Time-series Anomalies and Root Causes

Detecting anomalies and the corresponding root causes in multivariate time series plays an important role in monitoring the behaviors of various real-world systems, e.g., IT system operations or manufacturing industry. Previous anomaly detection approaches model the joint distribution without considering the underlying mechanism of multivariate time series, making them computationally hungry and hard to identify root causes. In this paper, we formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data. We then propose a causality-based framework for detecting anomalies and root causes. It first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism whose conditional distribution can be directly estimated from data. In light of the modularity property of causal systems (the causal processes to generate different variables are irrelevant modules), the original problem is divided into a series of separate, simpler, and low-dimensional anomaly detection problems so that where an anomaly happens (root causes) can be directly identified. We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications, showing its efficacy, robustness, and practical feasibility.
An Improved Teaching-Learning-Based Optimization with Differential Learning and Its Application

The teaching-learning-based optimization (TLBO) algorithm is a population-based optimization algorithm which is based on the effect of the influence of a teacher on the output of learners in a class. A variant of teaching-learning-based optimization (TLBO) algorithm with differential learning (DLTLBO) is proposed in the paper. In this method, DLTLBO utilizes a learning strategy based on neighborhood search of teacher phase in the standard TLBO to generate a new mutation vector, while utilizing a differential learning to generate another new mutation vector. Then DLTLBO employs the crossover operation to generate new solutions so as to increase the diversity of the population. By the integration of the local search and the global search, DLTLBO achieves a tradeoff between exploration and exploitation. To demonstrate the effectiveness of our approaches, 24 benchmark functions are used for simulating and testing. Moreover, DLTLBO is used for parameter estimation of digital IIR filter and experimental results show that DLTLBO is superior or comparable to other given algorithms for the employed examples.
On the influence of stochastic roundoff errors and their bias on the convergence of the gradient descent method with low-precision floating-point computation

When implementing the gradient descent method in low precision, the employment of stochastic rounding schemes helps to prevent stagnation of convergence caused by the vanishing gradient effect. Unbiased stochastic rounding yields zero bias by preserving small updates with probabilities proportional to their relative magnitudes. This study provides a theoretical explanation for the stagnation of the gradient descent method in low-precision computation. Additionally, we propose two new stochastic rounding schemes that trade the zero bias property with a larger probability to preserve small gradients. Our methods yield a constant rounding bias that, on average, lies in a descent direction. For convex problems, we prove that the proposed rounding methods typically have a beneficial effect on the convergence rate of gradient descent. We validate our theoretical analysis by comparing the performances of various rounding schemes when optimizing a multinomial logistic regression model and when training a simple neural network with an 8-bit floating-point format.
MONTE CARLO CALCULATION OF THE TRANSLATIONAL RELAXATION OF SUPERTHERMAL H ATOMS IN THERMAL H2 GAS

A simple and reliable method to study the translational relaxation of “hot” H atoms following their production by chemical mechanisms is proposed. The problem is relevant to interstellar medium, shocks, photospheres, and atmospheric entry problems. It is shown that the thermalization of H atoms can be conveniently studied by the Monte Carlo method, including the thermal distribution of background molecules, and sets the basis for further investigations. The transport cross section is determined by the inversion of transport data. The collision density of H atoms in H2 gas is also calculated and discussed in the context of simple theories. The application of the results to astrophysical problems is outlined, including numerical results for the reaction H + H2O → H2 + OH. An approximate analytical formula for the reaction probability during H atom thermalization is proposed.
EFFECTS OF ULTRAVIOLET BACKGROUND AND LOCAL STELLAR RADIATION ON THE H i COLUMN DENSITY DISTRIBUTION

We study the impact of ultraviolet background (UVB) radiation field and the local stellar radiation on the H i column density distribution f(NH i) of damped Lyα systems (DLAs) and sub-DLAs at z = 3 using cosmological smoothed particle hydrodynamics simulations. We find that, in the previous simulations with an optically thin approximation, the UVB was sinking into the H i cloud too deeply, and therefore we underestimated the f(NH i) at 19 < log NH i < 21.2 compared to the observations. If the UVB is shut off in the high-density regions with ngas>6 × 10−3 cm−3, then we reproduce the observed f(NH i) at z = 3 very well. We also investigate the effect of local stellar radiation by postprocessing our simulation with a radiative transfer code and find that the local stellar radiation does not change the f(NH i) very much. Our results show that the shape of f(NH i) is determined primarily by the UVB with a much weaker effect by the local stellar radiation and that the optically thin approximation often used in cosmological simulation is inadequate to properly treat the ionization structure of neutral gas in and out of DLAs. Our result also indicates that the DLA gas is closely related to the transition region from optically thick neutral gas to optically thin ionized gas within dark matter halos.
Liminal Lives in Uncertain Times: Health Management During the COVID-19 Pandemic Among Transgender and Non-Binary Older Adults

Older adulthood is a crucial time in the health management journeys of transgender and non-binary (TNB) people. Understanding how the COVID-19 pandemic has impacted TNB older patients (65 years and over) offers critical guidance for successful health services reform and continued delivery systems change. Using qualitative data from 47 semi-structured individual interviews, I investigate how TNB older Americans—as a medically and socially vulnerable population in the United States—manage their health during the COVID-19 pandemic. Results indicated a need for enhancing health services and resources for TNB older adults while creating a culture of age-friendly and gender-affirming healthcare. Four primary themes emerged: (1) exacerbated mental health challenges, (2) disrupted social relationships and support, (3) adopting cost-effective health management strategies, and (4) incorporating family care partners in health management. Such themes were shaped by respondents’ privileged and marginalized social locations, such as access to financial security, social support, and adequate medical care. Although these research findings should not be generalized to the TNB older adult population, they suggest that broader patterns of inequity affect how TNB older Americans manage their health during the COVID-19 pandemic.
GIANT H ii REGIONS IN M101. I. X-RAY ANALYSIS OF HOT GAS

We performed a Chandra X-ray study of three giant H ii regions (GHRs), NGC 5461, NGC 5462, and NGC 5471, in the spiral galaxy M101. The X-ray spectra of the three GHRs all contain a prominent thermal component with a temperature of ∼0.2 keV. In NGC 5461, the spatial distribution of the soft (<1.5 keV) X-ray emission is generally in agreement with the extent of H1105, the most luminous H ii region therein, but extends beyond its southern boundary, which could be attributed to outflows from the star cloud between H1105 and H1098. In NGC 5462, the X-ray emission is displaced from the H ii regions and a ridge of blue stars; the Hα filaments extending from the ridge of star cloud to the diffuse X-rays suggest that hot gas outflows have occurred. The X-rays from NGC 5471 are concentrated at the B-knot, a “hypernova remnant” candidate. Assuming a Sedov–Taylor evolution, the derived explosion energy, on the order of 1052 erg, is consistent with a hypernova origin. In addition, a bright source in the field of NGC 5462 has been identified as a background active galactic nucleus, instead of a black hole X-ray binary in M101.
Label driven Knowledge Distillation for Federated Learning with non-IID Data

In real-world applications, Federated Learning (FL) meets two challenges: (1) scalability, especially when applied to massive IoT networks; and (2) how to be robust against an environment with heterogeneous data. Realizing the first problem, we aim to design a novel FL framework named Full-stack FL (F2L). More specifically, F2L utilizes a hierarchical network architecture, making extending the FL network accessible without reconstructing the whole network system. Moreover, leveraging the advantages of hierarchical network design, we propose a new labeldriven knowledge distillation (LKD) technique at the global server to address the second problem. As opposed to current knowledge distillation techniques, LKD is capable of training a student model, which consists of good knowledge from all teachers’ models. Therefore, our proposed algorithm can effectively extract the knowledge of the regions’ data distribution (i.e., the regional aggregated models) to reduce the divergence between clients’ models when operating under the FL system with non-independent identically distributed data. Extensive experiment results reveal that: (i) our F2L method can significantly improve the overall FL efficiency in all global distillations, and (ii) F2L rapidly achieves convergence as global distillation stages occur instead of increasing on each communication cycle.
V5852 Sgr: an unusual nova possibly associated with the Sagittarius stream

We report spectroscopic and photometric follow-up of the peculiar nova V5852~Sgr (discovered as OGLE-2015-NOVA-01), which exhibits a combination of features from different nova classes. The photometry shows a flat-topped light curve with quasi-periodic oscillations, then a smooth decline followed by two fainter recoveries in brightness. Spectroscopy with the Southern African Large Telescope shows first a classical nova with an Fe II or Fe IIb spectral type. In the later spectrum, broad emissions from helium, nitrogen and oxygen are prominent and the iron has faded which could be an indication to the start of the nebular phase. The line widths suggest ejection velocities around $1000\,{\rm km\,s^{-1}}$. The nova is in the direction of the Galactic bulge and is heavily reddened by an uncertain amount. The $V$ magnitude 16 days after maximum enables a distance to be estimated and this suggests that the nova may be in the extreme trailing stream of the Sagittarius dwarf spheroidal galaxy. If so it is the first nova to be detected from that, or from any dwarf spheroidal galaxy. Given the uncertainty of the method and the unusual light curve we cannot rule out the possibility that it is in the bulge or even the Galactic disk behind the bulge.
Probing for Reasons: Presentations, Questions, Phases

PROBING FOR REASONS: PRESENTATIONS, QUESTIONS, PHASES Kellyn Nicole Farlow Department of Mathematics Education Master of Arts This thesis reports on a research study based on data from experimental teaching. Students were invited, through real-world problem tasks that raised central conceptual issues, to invent major ideas of calculus. This research focuses on work and thinking of these students, as they sought to build key ideas, representations and compelling lines of reasoning. This focus on the students’ and their agency as learners caused a development of the psychological and logical perspectives, and highlighted students’ choices in academic and social roles. Such choices facilitated continued learning among these students.
INFERRING HOST DARK MATTER HALO MASSES OF INDIVIDUAL GALAXIES FROM NEIGHBORING GALAXY COUNTS

How well can we infer host dark matter halo masses of individual galaxies? Based on the halo occupation distribution framework, we analytically compute the number of neighboring galaxies within a cylinder of some redshift interval and radius in transverse comoving distance. The result is used to derive the conditional probability distribution function (PDF) of the host halo mass of a galaxy, given the neighboring galaxy counts. We compare our analytic results with those obtained using a realistic mock galaxy catalog, finding reasonable agreements. We find the optimal cylinder radius to be ∼ 0.5 − 1 h − 1 Mpc ? > for the inference of halo masses. The PDF is generally broad, and sometimes has two peaks at low- and high-mass regimes because of the effect of chance projection along the line of sight. Potential applications and extensions of the new theoretical framework developed herein are also discussed.
Effective equations of motion for constrained quantum systems: A study of the Bianchi I loop quantum cosmology

A new mathematical framework is formulated to derive the effective equations of motion for the constrained quantum system which possesses an internal clock. In the realm close to classical behavior, the quantum evolution is approximated by a finite system of coupled but ordinary differential equations adhered to the weakly imposed Hamiltonian constraint. For the simplified version of loop quantum cosmology in the Bianchi I model with a free massless scalar filed, the resulting effective equations of motion affirm the bouncing scenario predicted by the previous studies: The big bang singularity is resolved and replaced by the big bounces, which take place up to three times, once in each diagonal direction, whenever the directional density approaches the critical value in the regime of Planckian density. It is also revealed that back-reaction arises from the quantum corrections and modifies the precise value of the directional density at the bouncing epoch. Additionally, as an example of symmetry reduction, we study isotropy emerging from the anisotropic Bianchi I model in the context of effective equations of motion.
FERMI LARGE AREA TELESCOPE OBSERVATION OF SUPERNOVA REMNANT S147

We present an analysis of gamma-ray data obtained with the Large Area Telescope on board the Fermi Gamma-ray Space Telescope in the region around supernova remnant (SNR) S147 (G180.0−1.7). A spatially extended gamma-ray source detected in an energy range of 0.2–10 GeV is found to coincide with SNR S147. We confirm its spatial extension at >5σ confidence level. The gamma-ray flux is (3.8 ± 0.6) × 10−8 photons cm−2 s−1, corresponding to a luminosity of 1.3 × 1034 (d/1.3 kpc)2 erg s−1 in this energy range. The gamma-ray emission exhibits a possible spatial correlation with the prominent Hα filaments of SNR S147. There is no indication that the gamma-ray emission comes from the associated pulsar PSR J0538+2817. The gamma-ray spectrum integrated over the remnant is likely dominated by the decay of neutral π mesons produced through the proton–proton collisions in the filaments. The reacceleration of the pre-existing cosmic rays and subsequent adiabatic compression in the filaments is sufficient to provide the energy density required of high-energy protons.