Miracle-dz/UniFAR-FTUs huggingface dataset:
Hot gas in Milky Way size galaxies at z=0

Abstract We present a new set of cosmological Milky Way size galaxy simulations using ART. In our simulations the main system has been evolved inside a 28 Mpc cosmological box with a spatial resolution of 109 pc. At z=0 our systems have an M vir = 6 − 8 × 1011 M⊙. In several of out models we have observed how a well defined disk is formed inside the dark matter halo and the overall amount of gas and stars is comparable with MW observations. Several non-axisymmetric structures arise out of the disk: spirals, bars and also a warp. We have also observed that a huge reservoir of hot gas is present at large distances from the disk, embedded in the dark matter halo region, accounting for only a fraction of the ”missing baryons”. Gas column density, emission (EM) and dispersion (DM) measure have been computed from inside the simulated disk at a position of 8 kpc from the center and in several directions. Our preliminary results reveal that the distribution of hot gas is non-isotropic according with observations (Gupta et al. 2012, Gupta et al. 2014). Also its metallic content presents a clear bimodality what is a consequence of a recent accretion of a satellite galaxy among others. After a careful analysis we confirm that due to the anisotropy in the gas distribution a new observational parameter needs to be defined to recover the real distribution of hot gas in the galactic halo (Roca-Fàbrega et al. 2016).
Scaling Relationship on Learning Mathematical Reasoning with Large Language Models

Mathematical reasoning is a challenging task for large language models (LLMs), while the scaling relationship of it with respect to LLM capacity is under-explored. In this paper, we investigate how the pre-training loss, supervised data amount, and augmented data amount influence the reasoning performances of a supervised LLM. We find that pre-training loss is a better indicator of the model's performance than the model's parameter count. We apply supervised fine-tuning (SFT) with different amounts of supervised data and empirically find a log-linear relation between data amount and model performance, and we find better models improve less with enlarged supervised datasets. To augment more data samples for improving model performances without any human effort, we propose to apply Rejection sampling Fine-Tuning (RFT). RFT uses supervised models to generate and collect correct reasoning paths as augmented fine-tuning datasets. We find with augmented samples containing more distinct reasoning paths, RFT improves mathematical reasoning performance more for LLMs. We also find RFT brings more improvement for less performant LLMs. Furthermore, we combine rejection samples from multiple models which push LLaMA-7B to an accuracy of 49.3\% on GSM8K which outperforms the supervised fine-tuning (SFT) accuracy of 35.9\% significantly.
Rotation-Invariant Transformer for Point Cloud Matching

In this appendix, we first provide the implementation details in Sec. A. Then the network architecture is detailed in Sec. B. Moreover, the definition and ablation study of the geometry representation are illustrated in Sec. C. The details of the loss function and the evaluation metrics are given in Sec. D and Sec. E, respectively. Furthermore, more quantitative and qualitative results are demonstrated in Sec. F and Sec. G, respectively. Finally, the runtime is analyzed in Sec. H, and the limitations are discussed in Sec. I.
Signatures of primordial energy injection from axion strings

Axion strings are horizon-size topological defects that may be produced in the early Universe. Ultra-light axion-like particles may form strings that persist to temperatures below that of big bang nucleosynthesis. Such strings have been considered previously as sources of gravitational waves and cosmic microwave background (CMB) polarization rotation. In this work we show, through analytic arguments and dedicated adaptive mesh refinement cosmological simulations, that axion strings deposit a sub-dominant fraction of their energy into high-energy Standard Model (SM) final states, for example, by the direct production of heavy radial modes that subsequently decay to SM particles. This high-energy SM radiation is absorbed by the primordial plasma, leading to novel signatures in precision big bang nucleosynthesis, the CMB power spectrum, and gamma-ray surveys. In particular, we show that CMB power spectrum data constrains axion strings with decay constants $f_a \lesssim 10^{12}$ GeV, up to model dependence on the ultraviolet completion, for axion masses $m_a \lesssim 10^{-29}$ eV; future CMB surveys could find striking evidence of axion strings with lower decay constants.
Tadpole cosmology: Milne solution as a cosmological constant hideout

Dynamical cancellation frameworks present a potential means of mitigating the effect of a large vacuum energy, that would otherwise ruin the late-time, low energy dynamics of the Universe. Certain models in the literature, such as the Fab Four and Well Tempering, realize this idea by introducing some degeneracy in the dynamical equations. In this paper, we introduce a third potential route to self-tuning, and infer the existence of a new, exact Milne solution in the simplest tadpole plus cubic-Galileon scalar-tensor theory. We study the dynamics of the scalar field and metric in the vicinity of the Milne coordinate singularity, and find that the vacuum solution belongs to a more general family of Milne-like metrics. By numerically evolving the field equations for a range of initial conditions, we show that the Milne solution is not an attractor, and varying the initial scalar field data can lead to completely different asymptotic states; exponential growth of the scale factor, a static non-spatially flat metric or a severe finite-time instability in the scalar field and metric. We generalise the Milne solution to a class of FLRW spacetimes, finding that the tadpole-cubic Galileon model admits perfect-fluid-like solutions in the presence of matter. Finally, we present a second Horndeski model which also admits an exact Milne solution, hinting at the existence of a larger undiscovered model space containing vacuum-energy-screened solutions.
Change point analysis in non-stationary processes - a mass excess approach

This paper considers the problem of testing if a sequence of means $(\mu_t)_{t =1,\ldots ,n }$ of a non-stationary time series $(X_t)_{t =1,\ldots ,n }$ is stable in the sense that the difference of the means $\mu_1$ and $\mu_t$ between the initial time $t=1$ and any other time is smaller than a given level, that is $ | \mu_1 - \mu_t | \leq c $ for all $t =1,\ldots ,n $. A test for hypotheses of this type is developed using a biascorrected monotone rearranged local linear estimator and asymptotic normality of the corresponding test statistic is established. As the asymptotic variance depends on the location and order of the critical roots of the equation $| \mu_1 - \mu_t | = c$ a new bootstrap procedure is proposed to obtain critical values and its consistency is established. As a consequence we are able to quantitatively describe relevant deviations of a non-stationary sequence from its initial value. The results are illustrated by means of a simulation study and by analyzing data examples.
Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance

However, correlatedness is only defined when random elements have finite variance. The following lemma provides an infinite-variance version of expansion (A.1), stating that the p-th moment (p < 2) of a martingale without square-integrability assumption can also be bounded simpliciter by the sum of the p-th moments of its differences, at the cost of a multiplicative constant that may depend only on p and the dimension n. It is a generalization of the recent study Cherapanamjeri et al. [2020, Lemma 4.2].
Distributed control of multi-robot systems with global connectivity maintenance

In this paper we present a decentralized control strategy for the connectivity maintenance for groups of mobile robots performing some desired task. Exploiting a completely decentralized estimation strategy for the algebraic connectivity of the graph, we prove the connectivity maintenance property in the general case, i.e. in presence of a generic (bounded) additional control term. Then, we address two specific decentralized control applications: rendezvous and formation control. We analytically prove that our control strategy ensures the global connectivity maintenance, while preserving the convergence properties of the rendezvous and formation controllers respectively. Simulations and experimental results are presented as well, in order to show the effectiveness of the proposed control law.
CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition Supplementary

Section 1 shows how using clustering has a regularization effect on the seen classes with all samples as well as seen classes with a subset of samples. Section 2 shows that our approach outperforms prior work with a high statistical significance in most scenarios. Section 3 shows that our approach is consistently outperforming prior work when evaluated on the same split. Section 4 and Figure 3 shows the performance w.r. t. the number of clusters, which is stable across a wide range of values for this hyper parameter. Section 5 shows the comparison of aggregation strategies and interaction between visual and semantic features. Section 6 and Table 4 report the performance of seen and unseen classes separately for the case of GZSL.
LiDAR View Synthesis for Robust Vehicle Navigation Without Expert Labels

Deep learning models for self-driving cars require a diverse training dataset to manage critical driving scenarios on public roads safely. This includes having data from divergent trajectories, such as the oncoming traffic lane or sidewalks. Such data would be too dangerous to collect in the real world. Data augmentation approaches have been proposed to tackle this issue using RGB images. However, solutions based on LiDAR sensors are scarce. Therefore, we propose synthesizing additional LiDAR point clouds from novel viewpoints without physically driving at dangerous positions. The LiDAR view synthesis is done using mesh reconstruction and ray casting. We train a deep learning model, which takes a LiDAR scan as input and predicts the future trajectory as output. A waypoint controller is then applied to this predicted trajectory to determine the throttle and steering labels of the ego-vehicle. Our method neither requires expert driving labels for the original nor the synthesized LiDAR sequence. Instead, we infer labels from LiDAR odometry. We demonstrate the effectiveness of our approach in a comprehensive online evaluation and with a comparison to concurrent work. Our results show the importance of synthesizing additional LiDAR point clouds, particularly in terms of model robustness. Code and supplementary visualizations are available at: https://jonathsch.github. io/lidar-synthesis/
Self-Adaptive Reconfigurable Arrays (SARA): Using ML to Assist Scaling GEMM Acceleration

—With increasing diversity in Deep Neural Network (DNN) models in terms of layer shapes and sizes, the research community has been investigating ﬂexible/reconﬁgurable accelerator substrates. This line of research has opened up two challenges. The ﬁrst is to determine the appropriate amount of ﬂexibility within an accelerator array that that can trade-off the performance beneﬁts versus the area overheads of the reconﬁgurability. The second is being able to determine the right conﬁguration of the array for the current DNN model and/or layer and reconﬁgure the accelerator at runtime. This work introduces a new class of accelerators that we call Self Adaptive Reconﬁgurable Array (SARA) . SARA architectures comprise of both a reconﬁgurable array and a hardware unit capable of determining an optimized conﬁguration for the array at runtime. We demonstrate an instance of SARA with an accelerator we call SAGAR that introduces a novel reconﬁgurable systolic array that can be conﬁgured to work as a distributed collection of smaller arrays of various sizes or as a single array with ﬂexible aspect ratios. We also develop a novel recommendation neural network called A DAPT N ET which recommends an array conﬁguration and dataﬂow for the current layer parameters. An integrated custom hardware A DAPT N ET X runs A DAPT N ET at runtime and reconﬁgures the array, making the entire accelerator self sufﬁcient. SAGAR is capable of providing the same mapping ﬂexibility as a collection of 1024 4 × 4 arrays working as a distributed system while achieving 3.5 × more power efﬁciency and 3.2 × higher compute density Furthermore, when tested over 2 × 10 5 cases, the runtimes (GeoMean) achieved on the recommended parameters from A DAPT N
Fast rates in structured prediction

Discrete supervised learning problems such as classiﬁcation are often tackled by introducing a continuous surrogate problem akin to regression. Bounding the original error, between estimate and solution, by the surrogate error endows discrete problems with convergence rates already shown for continuous instances. Yet, current approaches do not leverage the fact that discrete problems are essentially predicting a discrete output when continuous problems are predicting a continuous value. In this paper, we tackle this issue for general structured prediction problems, opening the way to “super fast” rates, that is, convergence rates for the excess risk faster than 𝑛 − 1 , where 𝑛 is the number of observations, with even exponential rates with the strongest assumptions. We ﬁrst illustrate it for predictors based on nearest neighbors, generalizing rates known for binary classiﬁcation to any discrete problem within the framework of structured prediction. We then consider kernel ridge regression where we improve known rates in 𝑛 − 1 / 4 to arbitrarily fast rates, depending on a parameter characterizing the hardness of the problem, thus allowing, under smoothness assumptions, to bypass the curse of dimensionality.
Fusion modules and amenability of coideals of compact and discrete quantum groups

We give a definition of an amenable fusion module over a fusion algebra. A notion of relative integrability for the `coduals' of coideals of compact quantum groups was recently introduced in the joint work of de Commer and Dzokou Talla. We use this property to construct an analogue of the quasi-regular representation. Then, we characterize a certain coamenability property of quasi-regular representations with amenability of their associated fusion modules. Afterwards, we obtain a duality result that generalizes Tomatsu's theorem for this coamenability property and an amenability property of their `codual' coideals (under an additional assumption). As an example, we apply this result to show the fusion modules associated to certain non-standard Podle\'s spheres are amenable.
Survival After Out‐of‐Hospital Cardiac Arrest in Children

Background Little is known about survival after out‐of‐hospital cardiac arrest (OHCA) in children. We examined whether OHCA survival in children differs by age, sex, and race, as well as recent survival trends. Methods and Results Within the prospective Cardiac Arrest Registry to Enhance Survival (CARES), we identified children (age <18 years) with an OHCA from October 2005 to December 2013. Survival to hospital discharge by age (categorized as infants [0 to 1 year], younger children [2 to 7 years], older children [8 to 12 years], and teenagers [13 to 17 years]), sex, and race was assessed using modified Poisson regression. Additionally, we assessed whether survival has improved over 3 time periods: 2005–2007, 2008–2010, and 2011–2013. Of 1980 children with an OHCA, 429 (21.7%) were infants, 952 (48.1%) younger children, 276 (13.9%) older children, and 323 (16.3%) teenagers. Fifty‐nine percent of the study population was male and 31.8% of black race. Overall, 162 (8.2%) children survived to hospital discharge. After multivariable adjustment, infants (rate ratio: 0.56; 95% CI: 0.35, 0.90) and younger children (rate ratio: 0.42; 95% CI: 0.27, 0.65) were less likely to survive compared with teenagers. In contrast, there were no differences in survival by sex or race. Finally, there were no temporal trends in survival across the study periods (P=0.21). Conclusions In a large, national registry, we found no evidence for racial or sex differences in survival among children with OHCA, but survival was lower in younger age groups. Unlike in adults with OHCA, survival rates in children have not improved in recent years.
Early identification of patients at risk for difficult intubation in the intensive care unit: development and validation of the MACOCHA score in a multicenter cohort study.

RATIONALE Difficult intubation in the intensive care unit (ICU) is a challenging issue. OBJECTIVES To develop and validate a simplified score for identifying patients with difficult intubation in the ICU and to report related complications. METHODS Data collected in a prospective multicenter study from 1,000 consecutive intubations from 42 ICUs were used to develop a simplified score of difficult intubation, which was then validated externally in 400 consecutive intubation procedures from 18 other ICUs and internally by bootstrap on 1,000 iterations. MEASUREMENTS AND MAIN RESULTS In multivariate analysis, the main predictors of difficult intubation (incidence = 11.3%) were related to patient (Mallampati score III or IV, obstructive sleep apnea syndrome, reduced mobility of cervical spine, limited mouth opening); pathology (severe hypoxia, coma); and operator (nonanesthesiologist). From the β parameter, a seven-item simplified score (MACOCHA score) was built, with an area under the curve (AUC) of 0.89 (95% confidence interval [CI], 0.85-0.94). In the validation cohort (prevalence of difficult intubation = 8%), the AUC was 0.86 (95% CI, 0.76-0.96), with a sensitivity of 73%, a specificity of 89%, a negative predictive value of 98%, and a positive predictive value of 36%. After internal validation by bootstrap, the AUC was 0.89 (95% CI, 0.86-0.93). Severe life-threatening events (severe hypoxia, collapse, cardiac arrest, or death) occurred in 38% of the 1,000 cases. Patients with difficult intubation (n = 113) had significantly higher severe life-threatening complications than those who had a nondifficult intubation (51% vs. 36%; P < 0.0001). CONCLUSIONS Difficult intubation in the ICU is strongly associated with severe life-threatening complications. A simple score including seven clinical items discriminates difficult and nondifficult intubation in the ICU. Clinical trial registered with www.clinicaltrials. gov (NCT 01532063).
Mueller Matrix Polarimetry—An Emerging New Tool for Characterizing the Microstructural Feature of Complex Biological Specimen

Recently, with the emergence of new light sources, polarization devices, and detectors, together with a prominent increase in data processing capability, polarization techniques find more and more applications in various areas, one of which is biomedicine. For probing the characteristic features of complex biomedical specimen, Mueller matrix polarimetry has demonstrated distinctive advantages. Mueller matrix polarimetry can be achieved on other optical techniques by adding the polarization state generator and analyzer to their existing optical paths appropriately. Common biomedical optical equipment, such as microscopes and endoscopes, can be upgraded to fulfill Mueller matrix imaging and measurement abilities. Compared with traditional non-polarization optical methods, Mueller matrix polarimetry can provide far more information to characterize the samples, including the anisotropic optical properties, such as birefringence and diattenuation, as well as the distinctive features of various scattering particles and microstructures. Also, Mueller matrix polarimetry is more sensitive to scattering by sub-wavelength microstructures. As a label-free and non-invasive tool, Mueller matrix polarimetry has broad application prospects in biomedical studies and clinical diagnosis. In this review, we provide an introduction to the Mueller matrix methodology, including the Stokes–Mueller formalism, and also the decomposition and transformation methods to derive new parameters. We also summarize the status of the Mueller matrix polarimetric field, including recent improvements, both in instrumentation and data analysis. The current and future applications of Mueller matrix polarimetry in biomedicine are provided and discussed.
Consensus of Discrete-Time Multiagent Systems With State, Input, and Communication Delays

In this paper, we study the consensus problem for high-order discrete-time multiagent systems with state, input, and communication delays, where the input and communication delays can be arbitrarily large yet exactly known. Moreover, the communication delays are different for different agents. Nested predictor-based state feedback protocols and full-order/reduced-order observer-based output feedback protocols are established to solve the problem. It is shown that both the input and communication delays can be compensated completely. Furthermore, linear matrix inequalities-based approaches are provided to design state feedback gains and observer gains. A numerical example is worked out to illustrate the effectiveness of the proposed approaches.
Pseudospectra of Matrix Pencils and their applications in perturbation analysis of Eigenvalues and Eigendecompositions

The main theme of the thesis revolves around pseudospectra of matrix pencils and matrix polynomials and their applications in perturbation theory. More specifically, first, we develop a general framework for defining and analyzing pseudospectra of matrix pencils and matrix polynomials. The framework so developed unifies various definitions of pseudospectra of matrix pencils proposed in the literature. We dispel the perception that there are many nonequivalent ways of defining pseudospectra of matrix pencils/polynomials and put the analysis of pseudospectra of matrix pencils/polynomials on the same footing as that of matrices. Second, we analyze various properties of backward error functions associated with matrix pencils/polynomials. We introduce a notion of critical points of backward error functions and show that a critical point is a multiple eigenvalue of an appropriately perturbed pencil/polynomial. We now show that common boundary points of the components of pseudospectra of a matrix pencil/polynomial are critical points. We show that a minimal critical point can be read off from the pseudospectra of matrix pencils/polynomials. Hence a solution of Wilkinson’s problem for matrix pencils/polynomials can be read off from the pseudospectra of matrix pencils/polynomials. Given a diagonal pencil with distinct eigenvalues, we provide a simple procedure for the construction of nearest defective pencils. Third, we provide various pseudospectra inclusions for matrix pencils and show that pseudospectra inclusions can be gainfully used for analyzing stability of eigendecompositions. We introduce analogues of various notions of separation of matrices to the case of matrix pencils and show their utility in analyzing stability of eigendecompositions. We show that the separations such as sep, sepλ and gsep can be defined and analyzed on the same lines as that of matrices. Fourth, we present a general framework for the sensitivity analysis of eigenvalues of matrix pencils and matrix polynomials. We lay bare the big picture that lies behind the notion of sensitivity of eigenvalues of matrix pencils/polynomials. We show that
A non-local traffic flow model for 1-to-1 junctions with buffer

In this paper, we introduce a non-local PDE-ODE traffic model devoted to the description of a 1-to-1 junction with buffer. We present a numerical method to approximate solutions and show a maximum principle which is uniform in the non-local interaction range. Further, we exploit the limit models as the support of the kernel tends to zero and to infinity. We compare them with other already existing models for traffic and production flow and present numerical examples.