Miracle-dz/UniFAR-FTUs huggingface dataset:
Calibration of a star formation and feedback model for cosmological simulations with enzo

We introduce a new methodology for efficiently tuning sub-grid models of star formation and supernovae feedback in cosmological simulations and at the same time understanding their physical implications. Based on a set of 71 zoom simulations of a Milky Way (MW)-sized halo, we explore the feasibility of calibrating a widely used star formation and feedback model in the enzo simulation code. We propose a novel way to match observations, using functional fits to the observed baryon makeup over a wide range of halo masses. The model MW galaxy is calibrated using three parameters: the star formation efficiency (f*), the efficiency of thermal energy from stellar feedback (ϵ), and the region into which feedback is injected (r and s). We find that changing the amount of feedback energy affects the baryon content most significantly. We then identify two sets of feedback parameter values that are both able to reproduce the baryonic properties for haloes between $10^{10}\, \mathrm{M_\odot }$ and $10^{12}\, \mathrm{M_\odot }$. We can potentially improve the agreement by incorporating more parameters or physics. If we choose to focus on one property at a time, we can obtain a more realistic halo baryon makeup. Contrasting both star formation criteria and the corresponding combination of optimal feedback parameters, we also highlight that feedback effects can be complementary: to match the same baryonic properties, with a relatively higher gas-to-stars conversion efficiency, the feedback strength required is lower, and vice versa. Lastly, we demonstrate that chaotic variance in the code can cause deviations of approximately 10 per cent and 25 per cent in the stellar and baryon mass in simulations evolved from identical initial conditions.
Temperature scaling of two-ion anisotropy in pure and mixed anisotropy systems

Magnetic anisotropy plays an essential role in information technology applications of magnetic materials, providing a means to retain the long-term stability of a magnetic state in the presence of thermal fluctuations. Anisotropy consists of a single-ion contribution stemming from the crystal structure and two-ion terms attributed to the exchange interactions between magnetic atoms. A lack of robust theory crucially limits the understanding of the temperature dependence of the anisotropy in pure two-ion and mixed single-ion and two-ion systems. Here, we use Green's function theory and atomistic Monte Carlo simulations to determine the temperature scaling of the effective anisotropy in ferromagnets in these pure and mixed cases, from saturated to vanishing magnetization. At low temperature, we find that the pure two-ion anisotropy scales with the reduced magnetization as $k(m) \sim m^{2.28}$, while the mixed scenario describes the diversity of the temperature dependence of the anisotropy observed in real materials. The temperature dependence of the mixed anisotropy determined here is expected to considerably contribute to the understanding and the control of the thermal properties of magnetic materials.
Consistency of a Recurrent Language Model with Respect to Incomplete Decoding

Despite strong performance on a variety of tasks, neural sequence models trained with maximum likelihood have been shown to exhibit issues such as length bias and degenerate repetition. We study the related issue of receiving infinite-length sequences from a recurrent language model when using common decoding algorithms. To analyze this issue, we first define inconsistency of a decoding algorithm, meaning that the algorithm can yield an infinite-length sequence that has zero probability under the model. We prove that commonly used incomplete decoding algorithms - greedy search, beam search, top-k sampling, and nucleus sampling - are inconsistent, despite the fact that recurrent language models are trained to produce sequences of finite length. Based on these insights, we propose two remedies which address inconsistency: consistent variants of top-k and nucleus sampling, and a self-terminating recurrent language model. Empirical results show that inconsistency occurs in practice, and that the proposed methods prevent inconsistency.
Synthetic Magnetic Resonance Images with Generative Adversarial Networks

Data augmentation is essential for medical research to increase the size of training datasets and achieve better results. In this work, we experiment three GAN architectures with different loss functions to generate new brain MRIs. The results show the importance of hyperparameter tuning and the use of mini-batch similarity layer in the Discriminator and gradient penalty in the loss function to achieve convergence with high quality and realism. Moreover, huge computation time is needed to generate indistinguishable images from the original dataset.
Modeling engagement in long-term, in-home socially assistive robot interventions for children with autism spectrum disorders

Supervised machine-learning algorithms accurately modeled user engagement in long-term, in-home socially assistive robot interventions. Socially assistive robotics (SAR) has great potential to provide accessible, affordable, and personalized therapeutic interventions for children with autism spectrum disorders (ASD). However, human-robot interaction (HRI) methods are still limited in their ability to autonomously recognize and respond to behavioral cues, especially in atypical users and everyday settings. This work applies supervised machine-learning algorithms to model user engagement in the context of long-term, in-home SAR interventions for children with ASD. Specifically, we present two types of engagement models for each user: (i) generalized models trained on data from different users and (ii) individualized models trained on an early subset of the user’s data. The models achieved about 90% accuracy (AUROC) for post hoc binary classification of engagement, despite the high variance in data observed across users, sessions, and engagement states. Moreover, temporal patterns in model predictions could be used to reliably initiate reengagement actions at appropriate times. These results validate the feasibility and challenges of recognition and response to user disengagement in long-term, real-world HRI settings. The contributions of this work also inform the design of engaging and personalized HRI, especially for the ASD community.
Hydrodynamic Response of the Intergalactic Medium to Reionization

The intergalactic medium is expected to clump on scales down to M⊙ before the onset of reionization. The impact of these small-scale structures on reionization is poorly understood despite the modern understanding that gas clumpiness limits the growth of regions. We use a suite of radiation-hydrodynamics simulations that capture the Jeans mass of unheated gas to study density fluctuations during reionization. Our simulations track the complex ionization and hydrodynamical response of gas in the wake of ionization fronts. The clumping factor of ionized gas (proportional to the recombination rate) rises to a peak value of 5–20 approximately Δt = 10 Myr after ionization front passage, depending on the incident intensity, redshift, and degree to which the gas had been preheated by the first X-ray sources. The clumping factor reaches its relaxed value of ≈3 by Δt = 300 Myr. The mean free path of Lyman-limit photons evolves in unison, being up to several times shorter in unrelaxed, recently reionized regions compared to those that were reionized much earlier. Assessing the impact of this response on the global reionization process, we find that unrelaxed gaseous structures boost the total number of recombinations by ≈50% and lead to spatial fluctuations in the mean free path that persist appreciably for several hundred million years after the completion of reionization.
Short sighted deep learning

A theory explaining how deep learning works is yet to be developed. Previous work suggests that deep learning performs a coarse graining, similar in spirit to the renormalization group (RG). This idea has been explored in the setting of a local (nearest-neighbor interactions) Ising spin lattice. We extend the discussion to the setting of a long-range spin lattice. Markov-chain Monte Carlo (MCMC) simulations determine both the critical temperature and scaling dimensions of the system. The model is used to train both a single restricted Boltzmann machine (RBM) network, as well as a stacked RBM network. Following earlier Ising model studies, the trained weights of a single-layer RBM network define a flow of lattice models. In contrast to results for nearest-neighbor Ising, the RBM flow for the long-ranged model does not converge to the correct values for the spin and energy scaling dimension. Further, correlation functions between visible and hidden nodes exhibit key differences between the stacked RBM and RG flows. The stacked RBM flow appears to move toward low temperatures, whereas the RG flow moves toward high temperature. This again differs from results obtained for nearest-neighbor Ising.
5G New Radio Evolution Towards Sub-THz Communications

In this paper, the potential of extending 5G New Radio physical layer solutions to support communications in sub- THz frequencies is studied. More specifically, we introduce the status of third generation partnership project studies related to operation on frequencies beyond 52.6 GHz and note also the recent proposal on spectrum horizons provided by federal communications commission (FCC) related to experimental licenses on 95 GHz–3 THz frequency band. Then, we review the power amplifier (PA) efficiency and output power challenge together with the increased phase noise (PN) distortion effect in terms of the supported waveforms. As a practical example on the waveform and numerology design from the perspective of the PN robustness, link performance results using 90 GHz carrier frequency are provided. The numerical results demonstrate that new, higher subcarrier spacings are required to support high throughput, which requires larger changes in the physical layer design. It is also observed that new phase-tracking reference signal designs are required to make the system robust against PN. The results illustrate that single-carrier frequency division multiple access is significantly more robust against PN and can provide clearly larger PA output power than cyclic-prefix orthogonal frequency division multiplexing, and is therefore a highly potential waveform for sub- THz communications.
Dynamic Energy Dispatch in Isolated Microgrids Based on Deep Reinforcement Learning

This paper focuses on deep reinforcement learning (DRL)-based energy dispatch for isolated microgrids (MGs) with diesel generators (DGs), photovoltaic (PV) panels, and a battery. A finite-horizon Partial Observable Markov Decision Process (POMDP) model is formulated and solved by learning from historical data to capture the uncertainty in future electricity consumption and renewable power generation. In order to deal with the instability problem of DRL algorithms and unique characteristics of finite-horizon models, two novel DRL algorithms, namely, FH-DDPG and FH-RDPG, are proposed to derive energy dispatch policies with and without fully observable state information. A case study using real isolated microgrid data is performed, where the performance of the proposed algorithms are compared with the myopic algorithm as well as other baseline DRL algorithms. Moreover, the impact of uncertainties on MG performance is decoupled into two levels and evaluated respectively.
Perturbation Validation: A New Heuristic to Validate Machine Learning Models

This paper introduces Perturbation Validation (PV), a new heuristic to validate machine learning models. PV does not rely on test data. Instead, it perturbs training data labels, re-trains the model against the perturbed data, then uses the consequent training accuracy decrease rate to assess model fit. PV also differs from traditional statistical approaches, which make judgements without considering label distribution. We evaluate PV on 10 real-world datasets and 6 synthetic datasets. Our results demonstrate that PV is more discriminating about model fit than existing validation approaches and it accords well with widely-held intuitions concerning the properties of a good model fit measurement. We also show that PV complements existing validation approaches, allowing us to give explanations for some of the issues present in the recently-debated "apparent paradox" that high capacity (potentially "overfitted") models may, nevertheless, exhibit good generalisation ability.
Accelerating Reinforcement Learning for Reaching Using Continuous Curriculum Learning

Reinforcement learning has shown great promise in the training of robot behavior due to the sequential decision making characteristics. However, the required enormous amount of interactive and informative training data provides the major stumbling block for progress. In this study, we focus on accelerating reinforcement learning (RL) training and improving the performance of multi-goal reaching tasks. Specifically, we propose a precision-based continuous curriculum learning (PCCL) method in which the requirements are gradually adjusted during the training process, instead of fixing the parameter in a static schedule. To this end, we explore various continuous curriculum strategies for controlling a training process. This approach is tested using a Universal Robot 5e in both simulation and real-world multi-goal reach experiments. Experimental results support the hypothesis that a static training schedule is suboptimal, and using an appropriate decay function for curriculum learning provides superior results in a faster way.
A time delay dynamic system with external source for the local outbreak of 2019-nCoV

How to model the 2019 CoronaVirus (2019-nCov) spread in China is one of the most urgent and interesting problems in applied mathematics. In this paper, we propose a novel time delay dynamic system with external source to describe the trend of local outbreak for the 2019-nCoV. The external source is introduced in the newly proposed dynamic system, which can be considered as the suspected people travel to different areas. The numerical simulations exhibit the dynamic system with the external source is more reliable than the one without it, and the rate of isolation is extremely important for controlling the increase of cumulative confirmed people of 2019-nCoV. Based on our numerical simulation results with the public data, we suggest that the local government should have some more strict measures to maintain the rate of isolation. Otherwise the local cumulative confirmed people of 2019-nCoV might be out of control.
Switchable Precision Neural Networks

Instantaneous and on demand accuracy-efficiency trade-off has been recently explored in the context of neural networks slimming. In this paper, we propose a flexible quantization strategy, termed Switchable Precision neural Networks (SP-Nets), to train a shared network capable of operating at multiple quantization levels. At runtime, the network can adjust its precision on the fly according to instant memory, latency, power consumption and accuracy demands. For example, by constraining the network weights to 1-bit with switchable precision activations, our shared network spans from BinaryConnect to Binarized Neural Network, allowing to perform dot-products using only summations or bit operations. In addition, a self-distillation scheme is proposed to increase the performance of the quantized switches. We tested our approach with three different quantizers and demonstrate the performance of SP-Nets against independently trained quantized models in classification accuracy for Tiny ImageNet and ImageNet datasets using ResNet-18 and MobileNet architectures.
MUSE Observations of NGC330 in the Small Magellanic Cloud: Helium Abundance of Bright Main-sequence Stars

We present observations of the most bright main-sequence stars in the Small Magellanic Cloud stellar cluster NGC 330 obtained with the integral-field spectrograph, the Multi Unit Spectroscopic Explorer, at the Very Large Telescope. The use of this valuable instrument allows us to study both photometric and spectroscopic properties of stellar populations of this young star cluster. The photometric data provide us a precise color–magnitude diagram, which seems to support the presence of two stellar populations of ages of ≈18 Myr and ≈30 Myr assuming a metallicity of Z = 0.002. Thanks to the spectroscopic data, we derive a helium abundance of 10 main-sequence stars within the effective radius of Reff = 20″ of NGC 330, thus leading to an estimation of = 10.93 ± 0.05(1σ). The helium elemental abundances of stars likely belonging to the two possible stellar populations do not show differences or dichotomy within the uncertainties. Thus, our results suggest that the two stellar populations of NGC 330, if they exist, share similar original He abundances. If we consider stellar rotation velocity in our analysis, a coeval (30 Myr) stellar population, experiencing different values of rotation, cannot be excluded. In this case, the mean helium abundance obtained in our analysis is 11.00 ± 0.05 dex. We also verified that possible non-LTE (NLTE) effects cannot be identified with our analysis because of the spectral resolution and they are within our derived abundance He uncertainties. Moreover, the analysis of the He abundance as a function of the distance from the cluster center of the observed stars does not show any correlation.
Multilayer Routing and Resource Assignment in Spatial Channel Networks (SCNs): Oriented Toward the Massive SDM Era

In the past few decades, optical transport networks (OTNs) have undergone significant evolution, from the earliest wavelength-division multiplexing (WDM) OTNs to elastic optical networks (EONs) and later to space-division multiplexing (SDM) OTNs, to address the continuous growth of Internet traffic. By 2024, Pbps-level OTNs are expected, far exceeding the capacity limit of single-mode fibers. The massive SDM era is on the horizon. In this context, newly designed OTNs called spatial channel networks (SCNs), which achieve high cost efficiency by means of practical hierarchical optical cross-connects, have recently been proposed. However, the evolution of OTNs will simultaneously present challenges related to resource allocation in networking. For instance, with the evolution from WDM-OTNs to EONs, the resource allocation problem was transformed from the routing and wavelength assignment (RWA) problem to the routing and spectrum assignment (RSA) problem due to the additionally introduced constraint of spectrum contiguity. Similarly, specially designed algorithms are also expected to be essential for addressing the resource allocation problem in SCNs. In this paper, we define this new problem as the routing, spatial channel, and spectrum assignment (RSCSA) problem. We propose an integer linear programming (ILP) model and a heuristic algorithm to solve the RSCSA problem. We examine the performance of the proposed approaches via simulation experiments. The results show that both proposed approaches are effective in finding the optimal solutions or solutions close to the lower bounds. To the best of our knowledge, this is the first work to focus on the problem of resource allocation in SCNs.
A Survey on Causal Inference

Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy, and economics, for decades. Nowadays, estimating causal effect from observational data has become an appealing research direction owing to the large amount of available data and low budget requirement, compared with randomized controlled trials. Embraced with the rapidly developed machine learning area, various causal effect estimation methods for observational data have sprung up. In this survey, we provide a comprehensive review of causal inference methods under the potential outcome framework, one of the well-known causal inference frameworks. The methods are divided into two categories depending on whether they require all three assumptions of the potential outcome framework or not. For each category, both the traditional statistical methods and the recent machine learning enhanced methods are discussed and compared. The plausible applications of these methods are also presented, including the applications in advertising, recommendation, medicine, and so on. Moreover, the commonly used benchmark datasets as well as the open-source codes are also summarized, which facilitate researchers and practitioners to explore, evaluate and apply the causal inference methods.
Discovering Mathematical Objects of Interest—A Study of Mathematical Notations

Mathematical notation, i.e., the writing system used to communicate concepts in mathematics, encodes valuable information for a variety of information search and retrieval systems. Yet, mathematical notations remain mostly unutilized by today’s systems. In this paper, we present the first in-depth study on the distributions of mathematical notation in two large scientific corpora: the open access arXiv (2.5B mathematical objects) and the mathematical reviewing service for pure and applied mathematics zbMATH (61M mathematical objects). Our study lays a foundation for future research projects on mathematical information retrieval for large scientific corpora. Further, we demonstrate the relevance of our results to a variety of use-cases. For example, to assist semantic extraction systems, to improve scientific search engines, and to facilitate specialized math recommendation systems. The contributions of our presented research are as follows: (1) we present the first distributional analysis of mathematical formulae on arXiv and zbMATH; (2) we retrieve relevant mathematical objects for given textual search queries (e.g., linking with ‘Jacobi polynomial’); (3) we extend zbMATH’s search engine by providing relevant mathematical formulae; and (4) we exemplify the applicability of the results by presenting auto-completion for math inputs as the first contribution to math recommendation systems. To expedite future research projects, we have made available our source code and data.
A PDE hierarchy for directed polymers in random environments

For a Brownian directed polymer in a Gaussian random environment, with q(t, ⋅) denoting the quenched endpoint density and Qn(t,x1,…,xn)=E[q(t,x1)…q(t,xn)], we derive a hierarchical PDE system satisfied by {Qn}n⩾1 . We present two applications of the system: (i) we compute the generator of {μt(dx)=q(t,x)dx}t⩾0 for some special functionals, where {μt(dx)}t⩾0 is viewed as a Markov process taking values in the space of probability measures; (ii) in the high temperature regime with d ⩾ 3, we prove a quantitative central limit theorem for the annealed endpoint distribution of the diffusively rescaled polymer path. We also study a nonlocal diffusion-reaction equation motivated by the generator and establish a super-diffusive O(t 2/3) scaling.