Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Importance Weighted Auto-Encoders with Annealed Importance Sampling

Stochastic variational inference with an amortized inference model and the reparameterization trick has become a widely-used algorithm for learning latent variable models. Increasing the flexibility of approximate posterior distributions while maintaining computational tractability is one of the core problems in stochastic variational inference. Two families of approaches proposed to address the problem are flow-based and multisample-based approaches such as importance weighted auto-encoders (IWAE). We introduce a new learning algorithm, the annealed importance weighted auto-encoder (AIWAE), for learning latent variable models. The proposed AIWAE combines multisample-based and flow-based approaches with the annealed importance sampling and its memory cost stays constant when the depth of flows increases. The flow constructed using an annealing process in AIWAE facilitates the exploration of the latent space when the posterior distribution has multiple modes. Through computational experiments, we show that, compared to models trained using the IWAE, AIWAE-trained models are better density models, have more complex posterior distributions and use more latent space representation capacity.
Globular clusters as probes of dark matter cusp-core transformations

Bursty star formation in dwarf galaxies can slowly transform a steep dark matter cusp into a constant density core. We explore the possibility that globular clusters (GCs) retain a dynamical memory of this transformation. To test this, we use the nbody6df code to simulate the dynamical evolution of GCs, including stellar evolution, orbiting in static and time-varying potentials for a Hubble time. We find that GCs orbiting within a cored dark matter halo, or within a halo that has undergone a cusp-core transformation, grow to a size that is substantially larger (Reff > 10 pc) than those in a static cusped dark matter halo. They also produce much less tidal debris. We find that the cleanest signal of an historic cusp-core transformation is the presence of large GCs with tidal debris. However, the effect is small and will be challenging to observe in real galaxies. Finally, we qualitatively compare our simulated GCs with the observed GC populations in the Fornax, NGC 6822, IKN, and Sagittarius dwarf galaxies. We find that the GCs in these dwarf galaxies are systematically larger (〈Reff〉 ≃ 7.8 pc), and have substantially more scatter in their sizes than in situ metal-rich GCs in the Milky Way and young massive star clusters forming in M83 (〈Reff〉 ≃ 2.5 pc). We show that the size, scatter, and survival of GCs in dwarf galaxies are all consistent with them having evolved in a constant density core, or a potential that has undergone a cusp-core transformation, but not in a dark matter cusp.
Job demands, job resources, and their relationship with burnout and engagement: a multi‐sample study

This study focuses on burnout and its positive antipode—engagement. A model is tested in which burnout and engagement have different predictors and different possible consequences. Structural equation modeling was used to simultaneously analyze data from four independent occupational samples (total N = 1698). Results confirm the hypothesized model indicating that: (1) burnout and engagement are negatively related, sharing between 10 per cent and 25 per cent of their variances; (2) burnout is mainly predicted by job demands but also by lack of job resources, whereas engagement is exclusively predicted by available job resources; (3) burnout is related to health problems as well as to turnover intention, whereas engagement is related only to the latter; (4) burnout mediates the relationship between job demands and health problems, whereas engagement mediates the relationship between job resources and turnover intention. The fact that burnout and engagement exhibit different patterns of possible causes and consequences implies that different intervention strategies should be used when burnout is to be reduced or engagement is to be enhanced. Copyright © 2004 John Wiley & Sons, Ltd.
Teachers’ Motivating Methods to Support Thai Ninth Grade Students’ Levels of Motivation and Learning in Mathematics Classrooms

The aims of this qualitative research were to investigate the level of motivation and learning of ninth grade students in mathematics classrooms in Thailand and to reveal how the teachers supported students’ levels of motivation and learning. The participants were 333 students and 12 teachers in 12 mathematics classrooms from four regions of Thailand. The results showed, first, that the students’ levels of learning ranged from low to moderate-high while the levels of motivation were from moderate to high. In addition, most students had intrinsic motivation; however, some students still lacked motivation and were only motivated by external sources. Second, teachers enhanced students’ learning by encouraging them to learn as a whole class the most by using lectures and asking questions. The other top methods were allowing time for self-paced learning and answering, and relying on internal sources of motivation using positive interpersonal and group activities. Most teachers employed both autonomy-supportive and controlling motivating styles in classroom. Even with a variety of motivating methods, the students’ learning did not seem to be supported adequately, especially for students who showed a low level of learning.
Lifetime of a greedy forager with long-range smell

We study a greedy forager who consumes food throughout a region. If the forager does not eat any food for S time steps it dies. We assume that the forager moves preferentially in the direction of greatest smell of food. Each food item in a given direction contributes towards the total smell of food in that direction, however the smell of any individual food item decays with its distance from the forager. We study both power-law decay and exponential decay of the smell with the distance of the food from the forager. For power-law decay, we vary the exponent α governing this decay, while for exponential decay we vary λ also governing the rate of the decay. For power-law decay we find, both analytically and through simulations, that for a forager living in one dimension, there is a critical value of α, namely , where for the forager will die in finite time, however for \alpha_c$ ? > the forager has a nonzero probability to live infinite time. We calculate analytically the critical value, , separating these two behaviors and find that depends on S as . We find analytically that at the system has an essential singularity. For exponential decay we find analytically that for all λ, the forager has a finite probability to live for infinite time. We also study, using simulations, a forager with long-range decaying smell in two dimensions (2D) and find that for this case the forager always dies within finite time. However, in 2D we observe indications of an optimal α (and λ) for which the forager has the longest lifetime.
DEGREE SEQUENCE OF RANDOM PERMUTATION GRAPHS

In this paper we study the degree sequence of the permutation graph $G_{\pi_n}$ associated with a sequence $\pi_n\in S_n$ of random permutations. Joint limiting distributions of the degrees are established using results from graph and permutation limit theories. In particular, for the uniform random permutation, the joint distribution of the degrees of the vertices labelled $\lceil nr_1 \rceil, \lceil nr_2 \rceil, \ldots, \lceil nr_s \rceil$ converges (after scaling by $n$) to independent random variables $D_1, D_2, \ldots, D_s$, where $D_i\sim \text{Unif}(r_i, 1-r_i)$, for $r_i\in [0,1]$ and $i\in \{1, 2, \ldots, s\}$. Moreover, the degree of the mid-vertex (the vertex labelled $n/2$) has a central limit theorem, and the minimum degree converges to a Rayleigh distribution after appropriate scalings. Finally, the limiting degree distribution of the permutation graph associated with a Mallows random permutation is determined, and interesting phase transitions are observed. Our results extend to other exponential measures on permutations.
Northstar: An Interactive Data Science System

In order to democratize data science, we need to fundamentally rethink the current analytics stack, from the user interface to the "guts. " Most importantly, enabling a broader range of users to unfold the potential of (their) data requires a change in the interface and the "protection" we offer them. On the one hand, visual interfaces for data science have to be intuitive, easy, and interactive to reach users without a strong background in computer science or statistics. On the other hand, we need to protect users from making false discoveries. Furthermore, it requires that technically involved (and often boring) tasks have to be automatically done by the system so that the user can focus on contributing their domain expertise to the problem. In this paper, we present Northstar, the Interactive Data Science System, which we have developed over the last 4 years to explore designs that make advanced analytics and model building more accessible.
Inquiring into the nature of the Abell 2667 brightest cluster galaxy: physical properties from MUSE

Based on HST and MUSE data, we probe the stellar and gas properties (i.e. kinematics, stellar mass, star formation rate) of the radio-loud brightest cluster galaxy (BCG) located at the centre of the X-ray-luminous cool-core cluster Abell 2667 (z = 0.2343). The bi-dimensional modelling of the BCG surface brightness profile reveals the presence of a complex system of substructures extending all around the galaxy. Clumps of different size and shape plunged into a more diffuse component constitute these substructures, whose intense ‘blue’ optical colour hints at the presence of a young stellar population. Our results depict the BCG as a massive (M⋆ ≃ 1.38 × 1011 M⊙) dispersion-supported spheroid (Δv⋆ ≤ 150 km s−1, σ0 ∼ 216 km s−1) hosting an active supermassive black hole (MSMBH ≃ 3.8 × 109 M⊙) whose optical features are typical of low-ionization nuclear emission line regions. Although the velocity pattern of the stars in the BCG is irregular, the stellar kinematics in the regions of the clumps show a positive velocity of ∼100 km s−1, similarly to the gas component. An analysis of the mechanism giving rise to the observed lines in the clumps through empirical diagnostic diagrams points out that the emission is composite, suggesting contribution from both star formation and an active galactic nucleus. We conclude our analysis describing how scenarios of both chaotic cold accretion and merging with a gas-rich disc galaxy can efficaciously explain the phenomena the BCG is undergoing.
Communication-Efficient Accurate Statistical Estimation

Abstract When the data are stored in a distributed manner, direct applications of traditional statistical inference procedures are often prohibitive due to communication costs and privacy concerns. This article develops and investigates two communication-efficient accurate statistical estimators (CEASE), implemented through iterative algorithms for distributed optimization. In each iteration, node machines carry out computation in parallel and communicate with the central processor, which then broadcasts aggregated information to node machines for new updates. The algorithms adapt to the similarity among loss functions on node machines, and converge rapidly when each node machine has large enough sample size. Moreover, they do not require good initialization and enjoy linear converge guarantees under general conditions. The contraction rate of optimization errors is presented explicitly, with dependence on the local sample size unveiled. In addition, the improved statistical accuracy per iteration is derived. By regarding the proposed method as a multistep statistical estimator, we show that statistical efficiency can be achieved in finite steps in typical statistical applications. In addition, we give the conditions under which the one-step CEASE estimator is statistically efficient. Extensive numerical experiments on both synthetic and real data validate the theoretical results and demonstrate the superior performance of our algorithms.
A parameterization of gravity waves emitted by fronts and jets

Based on the theoretical and experimental facts that gravity waves (GWs) can be spontaneously emitted during the evolution of a near‐balanced flow, a stochastic parameterization of GWs linked to fronts and jets is proposed. Although the spontaneous adjustment theory used predicts “exponentially” small GW fields, it is shown that it is sufficient to produce realistic GW drag at mesospheric levels. Off‐line tests using reanalyzed meteorological fields are conducted and show that the GWs emitted present a strong annual cycle following that of the sources. Also, the GW momentum fluxes in the lower stratosphere are qualitatively realistic in terms of intermittency. Online tests in a middle atmosphere general circulation model show that the scheme can potentially perform as well as highly tuned existing GW schemes.
Incremental Learning from Scratch for Task-Oriented Dialogue Systems

Clarifying user needs is essential for existing task-oriented dialogue systems. However, in real-world applications, developers can never guarantee that all possible user demands are taken into account in the design phase. Consequently, existing systems will break down when encountering unconsidered user needs. To address this problem, we propose a novel incremental learning framework to design task-oriented dialogue systems, or for short Incremental Dialogue System (IDS), without pre-defining the exhaustive list of user needs. Specifically, we introduce an uncertainty estimation module to evaluate the confidence of giving correct responses. If there is high confidence, IDS will provide responses to users. Otherwise, humans will be involved in the dialogue process, and IDS can learn from human intervention through an online learning module. To evaluate our method, we propose a new dataset which simulates unanticipated user needs in the deployment stage. Experiments show that IDS is robust to unconsidered user actions, and can update itself online by smartly selecting only the most effective training data, and hence attains better performance with less annotation cost.
Recognizing Manipulation Actions from State-Transformations

Manipulation actions transform objects from an initial state into a final state. In this paper, we report on the use of object state transitions as a mean for recognizing manipulation actions. Our method is inspired by the intuition that object states are visually more apparent than actions from a still frame and thus provide information that is complementary to spatio-temporal action recognition. We start by defining a state transition matrix that maps action labels into a pre-state and a post-state. From each keyframe, we learn appearance models of objects and their states. Manipulation actions can then be recognized from the state transition matrix. We report results on the EPIC kitchen action recognition challenge.
Parameter regimes for surpassing the PLOB bound with error-corrected qudit repeaters

A potential quantum internet would open up the possibility of realizing numerous new applications, including provably secure communication. Since losses of photons limit long-distance, direct quantum communication and wide-spread quantum networks, quantum repeaters are needed. The so-called PLOB-repeaterless bound [Pirandola et al., Nat. Commun. 8, 15043 (2017)] is a fundamental limit on the quantum capacity of direct quantum communication. Here, we analytically derive the quantum-repeater gain for error-corrected, one-way quantum repeaters based on higher-dimensional qudits for two different physical encodings: Fock and multimode qudits. We identify parameter regimes in which such quantum repeaters can surpass the PLOB-repeaterless bound and systematically analyze how typical parameters manifest themselves in the quantum-repeater gain. This benchmarking provides a guideline for the implementation of error-corrected qudit repeaters.
Online Learning and Planning in Partially Observable Domains without Prior Knowledge

How an agent can act optimally in stochastic, partially observable domains is a challenge problem, the standard approach to address this issue is to learn the domain model firstly and then based on the learned model to find the (near) optimal policy. However, offline learning the model often needs to store the entire training data and cannot utilize the data generated in the planning phase. Furthermore, current research usually assumes the learned model is accurate or presupposes knowledge of the nature of the unobservable part of the world. In this paper, for systems with discrete settings, with the benefits of Predictive State Representations~(PSRs), a model-based planning approach is proposed where the learning and planning phases can both be executed online and no prior knowledge of the underlying system is required. Experimental results show compared to the state-of-the-art approaches, our algorithm achieved a high level of performance with no prior knowledge provided, along with theoretical advantages of PSRs. Source code is available at this https URL.
Gravitational scalar field with a RAQUAL-like cubic kinetic term and second order self-interaction

The consequences of two unorthodox contributions to the Lagrangian of a (real valued) relativistic scalar field are studied: a cubic kinetic term similar to the "a-quadratic" Lagrangian used in the first attempt of a covariant generalization of Milgrom's modified Newtonian dynamics (RAQUAL) \citep{Bekenstein/Milgrom:1984}, and a second order derivative term studied by Novello et al. in the context of a Weyl geometric approach to cosmology \citep{Novello/Oliveira_ea:1993,Oliveira/Salim/Sautu:1997}. Both taken together, analysed in a scale covariant (Weyl geometric) approach, lead to a scalar field model which recovers the MOND kinematics for free fall trajectories in the quasi-static weak field approximations for the Einstein equation and the scalar field equation. In contrast to the original MOND and RAQUAL approaches, the second order term of the Lagrangian induces a non-negligible energy-momentum tensor of the scalar field. It results in gravitational light deflection compatible with the additional acceleration due to the scalar field in Einstein gauge. For clusters the scalar field halos around the galaxies superimpose the overall scalar halo of the hot gas. It remains to be checked whether this closes the mass gap for clusters without assuming additional dark matter.
Survival of Shiga toxin-producing Escherichia coli O157:H7 in Minas frescal cheese

Shiga toxin-producing Escherichia coli (STEC) O157:H7 strains (isolated by cattle’s faeces and a reference strain, EDL933), were inoculated into pasteurized milk (102 and 103 cells.mL–1) to prepare the Minas frescal cheese. As control was used uninfected milk. Physicochemical and microbiological analyses were performed to milk and elaborated cheese. The O157:H7 strains were quantified in the stages of cheese processing and during 0, 2, 4, 5, 7, 10 and 15 storage days at 8 °C onto Sorbitol MacConkey Agar supplemented with potassium tellurite and cefixime (CT-SMAC). O157:H7 was not present in the pasteurised milk prior to the artificial inoculation. At the end of the processing the cheese had 10 to 100 times more STEC O157:H7 than the initial inoculum. During the storage, the Minas frescal cheese exhibited the largest population increase on the 4th and 5th day when inoculated with 102 and 103 cells.mL–1, respectively. Additionally, viable cells were found up to the 10th and 15th day, according to the amount of initial inoculum. This number of cells is able to cause infection in humans, and therefore, Minas frescal cheese, even when stored under refrigeration, is a potential vehicle of disease caused by STEC O157:H7.
Elephant random walks with delays.

In the simple random walk the steps are independent, viz., the walker has no memory. In contrast, in the Elephant Random walk (ERW), which was introduced by Sch\"utz and Trimper in 2004, the walker remembers the whole past, and the next step always depends on the whole path so far. One extension, as suggested in a recent paper by Bercu et al. (arXiv:1902.11220v1), is to allow for delays, that is, to put mass at zero. Our aim is to extend known result for the ordinary ERW to a Random Walk With Delays (ERWD).
Model Testing for Generalized Scalar-on-Function Linear Models

Scalar-on-function linear models are commonly used to regress functional predictors on a scalar response. However, functional models are more difficult to estimate and interpret than traditional linear models, and may be unnecessarily complex for a data application. Hypothesis testing can be used to guide model selection by determining if a functional predictor is necessary. Using a mixed effects representation with penalized splines and variance component tests, we propose a framework for testing functional linear models with responses from exponential family distributions. The proposed method can accommodate dense and sparse functional data, and be used to test functional predictors for no effect and form of the effect. We show via simulation study that the proposed method achieves the nominal level and has high power, and we demonstrate its utility with two data applications.
On the XUV Luminosity Evolution of TRAPPIST-1

We model the long-term X-ray and ultraviolet (XUV) luminosity of TRAPPIST-1 to constrain the evolving high-energy radiation environment experienced by its planetary system. Using a Markov Chain Monte Carlo (MCMC) method, we derive probabilistic constraints for TRAPPIST-1's stellar and XUV evolution that account for observational uncertainties, degeneracies between model parameters, and empirical data of low-mass stars. We constrain TRAPPIST-1's mass to m⋆ = 0.089 ± 0.001 M⊙ and find that its early XUV luminosity likely saturated at . From the posterior distribution, we infer that there is a ∼40% chance that TRAPPIST-1 is still in the saturated phase today, suggesting that TRAPPIST-1 has maintained high activity and LXUV/Lbol ≈ 10−3 for several gigayears. TRAPPIST-1's planetary system therefore likely experienced a persistent and extreme XUV flux environment, potentially driving significant atmospheric erosion and volatile loss. The inner planets likely received XUV fluxes ∼103–104 times that of the modern Earth during TRAPPIST-1's ∼1 Gyr long pre-main-sequence phase. Deriving these constraints via MCMC is computationally nontrivial, so scaling our methods to constrain the XUV evolution of a larger number of M dwarfs that harbor terrestrial exoplanets would incur significant computational expenses. We demonstrate that approxposterior, an open source Python machine learning package for approximate Bayesian inference using Gaussian processes, accurately and efficiently replicates our analysis using 980 times less computational time and 1330 times fewer simulations than MCMC sampling using emcee. We find that approxposterior derives constraints with mean errors on the best-fit values and 1σ uncertainties of 0.61% and 5.5%, respectively, relative to emcee.