Miracle-dz/UniFAR-FTUs huggingface dataset:
PRIMA: Planner-Reasoner Inside a Multi-task Reasoning Agent

We consider the problem of multi-task reasoning (MTR), where an agent can solve multiple tasks via (first-order) logic reasoning. This capability is essential for human-like intelligence due to its strong generalizability and simplicity for handling multiple tasks. However, a major challenge in developing effective MTR is the intrinsic conflict between reasoning capability and efficiency. An MTR-capable agent must master a large set of"skills"to tackle diverse tasks, but executing a particular task at the inference stage requires only a small subset of immediately relevant skills. How can we maintain broad reasoning capability and also efficient specific-task performance? To address this problem, we propose a Planner-Reasoner framework capable of state-of-the-art MTR capability and high efficiency. The Reasoner models shareable (first-order) logic deduction rules, from which the Planner selects a subset to compose into efficient reasoning paths. The entire model is trained in an end-to-end manner using deep reinforcement learning, and experimental studies over a variety of domains validate its effectiveness.
Extreme Variation in Star Formation Efficiency across a Compact, Starburst Disk Galaxy

We report on the internal distribution of star formation efficiency in IRAS 08339+6517 (hereafter IRAS08), using ∼200 pc resolution CO(2 − 1) observations from NOEMA. The molecular gas depletion time changes by 2 orders-of-magnitude from disk-like values in the outer parts to less than 108 yr inside the half-light radius. This translates to a star formation efficiency per freefall time that also changes by 2 orders-of-magnitude, reaching 50%–100%, different than local spiral galaxies and the typical assumption of constant, low star formation efficiencies. Our target is a compact, massive disk galaxy that has a star formation rate 10× above the z = 0 main sequence; Toomre Q ≈ 0.5−0.7 and high gas velocity dispersion (σ mol ≈ 25 km s−1). We find that IRAS08 is similar to other rotating, starburst galaxies from the literature in the resolved ΣSFR∝ΣmolN relation. By combining resolved literature studies we find that the distance from the main sequence is a strong indicator of the Kennicutt-Schmidt power-law slope, with slopes of N ≈ 1.6 for starbursts from 100 to 104 M ⊙ pc−2. Our target is consistent with a scenario in which violent disk instabilities drive rapid inflows of gas. It has low values of Toomre-Q, and also at all radii, the inflow timescale of the gas is less than the depletion time, which is consistent with the flat metallicity gradients in IRAS08. We consider these results in light of popular star formation theories; in general observations of IRAS08 find the most tension with theories in which star formation efficiency is a constant. Our results argue for the need of high-spatial-resolution CO observations for a larger number of similar targets.
Trivsel i kroppsøvingsfaget blant elever med lavt og høyt oksygenopptak

© 2016Oyvind Bjerke et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license.
How quantum nonlocality without entanglement witnesses classical communication without causal order

We show a correspondence between quantum nonlocality without entanglement and logically consistent classical processes without causal order. Three parties with access to a noncausal classical process—the AF/BW process—can perfectly discriminate the states in an ensemble—the SHIFT ensemble—exhibiting quantum nonlocality without entanglement, something impossible with local operations and classical communication (LOCC): The LOCC restriction implicitly assumes causal classical communication. This provides an operational interpretation of the AF/BW process as a noncausal information-theoretic resource. From a foundational perspective, this means that the ability to perfectly discriminate quantum states in the SHIFT ensemble via local operations and classical communication would serve as an operational witness of noncausality. We then generalize our result to show how any Boolean classical process that maximally violates causal order can be transformed to an ensemble of states that exhibits quantum nonlocality without entanglement. Since multipartite generalizations of the AF/BW process are known, this yields novel multiqubit ensembles of quantum nonlocality without entanglement, which is of independent interest.
A New Estimate of the Cosmic Star Formation Density from a Radio-selected Sample, and the Contribution of H-dark Galaxies at z ≥ 3

The star formation rate density (SFRD) history of the universe is well constrained up to redshift z ∼ 2. At earlier cosmic epochs, the picture has been largely inferred from UV-selected galaxies (e.g., Lyman-break galaxies; LBGs). However, the inferred star formation rates of LBGs strongly depend on the assumed dust extinction correction, which is not well constrained at high z, while observations in the radio domain are not affected by this issue. In this work we measure the SFRD from a 1.4 GHz selected sample of ∼600 galaxies in the GOODS-N field up to redshift ∼3.5. We take into account the contribution of active galactic nuclei from the infrared-radio correlation. We measure the radio luminosity function, fitted with a modified Schechter function, and derive the SFRD. The cosmic SFRD shows an increase up to z ∼ 2 and then an almost flat plateau up to z ∼ 3.5. Our SFRD is in agreement with those from other far-IR/radio surveys and a factor 2 higher than those from LBG samples. We also estimate that galaxies lacking a counterpart in the HST/WFC3 H-band (H-dark) make up ∼25% of the ϕ-integrated SFRD relative to the full sample at z ∼ 3.2, and up to 58% relative to LBG samples.
The Chemo-Dynamical Groups of Galactic Globular Clusters

We introduce a multi-component chemo-dynamical method for splitting the Galactic population of Globular Clusters (GCs) into three distinct constituents: bulge, disc, and stellar halo. The latter is further decomposed into the individual large accretion events that built up the Galactic stellar halo: the Gaia -Enceladus-Sausage, Kraken and Sequoia structures, and the Sagittarius and Helmi streams. Our modelling is extensively tested using mock GC samples constructed from the auriga suite of hydrodynamical simulations of Milky Way (MW)-like galaxies. We ﬁnd that, on average, a proportion of the accreted GCs cannot be associated with their true infall group and are left ungrouped, biasing our recovered population numbers to ∼ 80% of their true value. Furthermore, the identiﬁed groups have a completeness and a purity of only ∼ 65%. This reﬂects the diﬃculty of the problem, a result of the large degree of overlap in energy-action space of the debris from past accretion events. We apply the method to the Galactic data to infer, in a statistically robust and easily quantiﬁable way, the GCs associated with each MW accretion event. The resulting groups’ population numbers of GCs, corrected for biases, are then used to infer the halo and stellar masses of the now defunct satellites that built up the halo of the MW.
Timely Gossiping with File Slicing and Network Coding

We consider a system consisting of a large network of n users and a library of files, wherein inter-user communication is established based upon gossip mechanisms. Each file is initially present at exactly one node, which is designated as the file source. The source gets updated with newer versions of the file according to an arbitrary distribution in real time, and the other users in the network wish to acquire the latest possible version of the file. We present a class of gossip protocols that achieve O(1) age at a typical node in a single-file system and O(n) age at a typical node for a given file in an n-file system. We show that file slicing and network coding based protocols fall under the presented class of protocols. Numerical evaluation results are presented to confirm the aforementioned bounds.
Backcasting COVID-19: a physics-informed estimate for early case incidence

It is widely accepted that the number of reported cases during the first stages of the COVID-19 pandemic severely underestimates the number of actual cases. We leverage delay embedding theorems of Whitney and Takens and use Gaussian process regression to estimate the number of cases during the first 2020 wave based on the second wave of the epidemic in several European countries, South Korea and Brazil. We assume that the second wave was more accurately monitored, even though we acknowledge that behavioural changes occurred during the pandemic and region- (or country-) specific monitoring protocols evolved. We then construct a manifold diffeomorphic to that of the implied original dynamical system, using fatalities or hospitalizations only. Finally, we restrict the diffeomorphism to the reported cases coordinate of the dynamical system. Our main finding is that in the European countries studied, the actual cases are under-reported by as much as 50%. On the other hand, in South Korea—which had a proactive mitigation approach—a far smaller discrepancy between the actual and reported cases is predicted, with an approximately 18% predicted underestimation. We believe that our backcasting framework is applicable to other epidemic outbreaks where (due to limited or poor quality data) there is uncertainty around the actual cases.
Tutorial on amortized optimization for learning to optimize over continuous domains

Optimization is a ubiquitous modeling tool that is often deployed in settings that repeatedly solve similar instances of the same problem. Amortized optimization methods use learning to predict the solutions to problems in these settings. This leverages the shared structure between similar problem instances. In this tutorial, we will discuss the key design choices behind amortized optimization, roughly categorizing 1) models into fully-amortized and semi-amortized approaches, and 2) learning methods into regression-based and objectivebased. We then view existing applications through these foundations to draw connections between them, including for manifold optimization, variational inference, sparse coding, meta-learning, control, reinforcement learning, convex optimization, and deep equilibrium networks. This framing enables us easily see, for example, that the amortized inference in variational autoencoders is conceptually identical to value gradients in control and reinforcement learning as they both use fully-amortized models with a objective-based loss. The source code for this tutorial is available at: https://github.com/facebookresearch/amortized-optimization-tutorial ar X iv :2 20 2. 00 66 5v 1 [ cs . L G ] 1 F eb 2 02 2
Dominant Set-based Active Learning for Text Classification and its Application to Online Social Media

Recent advances in natural language processing (NLP) in online social media are evidently owed to large-scale datasets. However, labeling, storing, and processing a large number of textual data points, e.g., tweets, has remained challenging. On top of that, in applications such as hate speech detection, labeling a sufficiently large dataset containing offensive content can be mentally and emotionally taxing for human annotators. Thus, NLP methods that can make the best use of significantly less labeled data points are of great interest. In this paper, we present a novel pool-based active learning method that can be used for the training of large unlabeled corpus with minimum annotation cost. For that, we propose to find the dominant sets of local clusters in the feature space. These sets represent maximally cohesive structures in the data. Then, the samples that do not belong to any of the dominant sets are selected to be used to train the model, as they represent the boundaries of the local clusters and are more challenging to classify. Our proposed method does not have any parameters to be tuned, making it dataset-independent, and it can approximately achieve the same classification accuracy as full training data, with significantly fewer data points. Additionally, our method achieves a higher performance in comparison to the state-of-the-art active learning strategies. Furthermore, our proposed algorithm is able to incorporate conventional active learning scores, such as uncertainty-based scores, into its selection criteria. We show the effectiveness of our method on different datasets and using different neural network architectures.
Holistic Fine-grained GGS Characterization: From Detection to Unbalanced Classification

Recent studies have demonstrated the diagnostic and prognostic values of global glomerulosclerosis (GGS) in IgA nephropathy, aging, and end-stage renal disease. However, the fine-grained quantitative analysis of multiple GGS subtypes (e.g., obsolescent, solidified, and disappearing glomerulosclerosis) is typically a resource extensive manual process. Very few automatic methods, if any, have been developed to bridge this gap for such analytics. In this paper, we present a holistic pipeline to quantify GGS (with both detection and classification) from a whole slide image in a fully automatic manner. In addition, we conduct the fine-grained classification for the sub-types of GGS. Our study releases the open-source quantitative analytical tool for fine-grained GGS characterization while tackling the technical challenges in unbalanced classification and integrating detection and classification.
Rise and fall of plaquette order in the Shastry-Sutherland magnet revealed by pseudofermion functional renormalization group

The Shastry-Sutherland model as a canonical example of frustrated magnetism has been extensively studied. The conventional wisdom has been that the transition from the plaquette valence bond order to the Neel order is direct and potentially realizes a deconfined quantum critical point beyond the Ginzburg-Landau paradigm. This scenario however was challenged recently by improved numerics from density matrix renormalization group which offers evidence for a narrow gapless spin liquid between the two phases. Prompted by this controversy and to shed light on this intricate parameter regime from a fresh perspective, we report high-resolution functional renormalization group analysis of the generalized Shastry-Sutherland model. The flows of over 50 million running couplings provide a detailed picture for the evolution of spin correlations as the frequency/energy scale is dialed from the ultraviolet to the infrared to yield the zero temperature phase diagram. The singlet dimer phase emerges as a fixed point, the Neel order is characterized by divergence in the vertex function, while the transition into and out of the plaquette order is accompanied by pronounced peaks in the plaquette susceptibility. The plaquette order is suppressed before the onset of the Neel order, lending evidence for a finite spin liquid region for $J_1/J_2\in (0.77,0.82)$, where the flow is continuous without any indication of divergence.
Numerical Methods and Simulations of Complex Multiphase Flows

Multiphase flows are an important part of many natural and technological phenomena such as ocean-air coupling (which is important for climate modeling) and the atomization of liquid fuel jets in combustion engines. The unique challenges of multiphase flow often make analytical solutions to the governing equations impossible and experimental investigations very difficult. Thus, high-fidelity numerical simulations can play a pivotal role in understanding these systems. This dissertation describes numerical methods developed for complex multiphase flows and the simulations performed using these methods. First, the issue of multiphase code verification is addressed. Code verification answers the question “Is this code solving the equations correctly? ” The method of manufactured solutions (MMS) is a procedure for generating exact benchmark solutions which can test the most general capabilities of a code. The chief obstacle to applying MMS to multiphase flow lies in the discontinuous nature of the material properties at the interface. An extension of the MMS procedure to multiphase flow is presented, using an adaptive marching tetrahedron style algorithm to compute the source terms near the interface. Guidelines for the use of the MMS to help locate coding mistakes are also detailed. Three multiphase systems are then investigated: (1) the thermocapillary motion of three-dimensional and axisymmetric drops in a confined apparatus, (2) the flow of two immiscible fluids completely filling an enclosed cylinder and driven by the rotation of the bottom endwall, and (3) the atomization of a single drop subjected to a high shear turbulent flow. The systems are simulated numerically by solving the full multiphase NavierStokes equations coupled to the various equations of state and a level set interface tracking scheme based on the refined level set grid method. The codes have been parallelized using MPI in order to take advantage of today’s very large parallel ii computational architectures. In the first system, the code’s ability to handle surface tension and large temperature gradients is established. In the second system, the code’s ability to simulate simple interface geometries with strong shear is demonstrated. In the third system, the ability to handle extremely complex geometries and topology changes with strong shear is shown.
Two-way Fixed Effects and Differences-in-Differences Estimators with Several Treatments

We study two-way-fixed-effects regressions (TWFE) with several treatment variables. Under a parallel trends assumption, we show that the coefficient on each treatment identifies a weighted sum of that treatment's effect, with possibly negative weights, plus a weighted sum of the effects of the other treatments. Thus, those estimators are not robust to heterogeneous effects and may be contaminated by other treatments' effects. We further show that omitting a treatment from the regression can actually reduce the estimator's bias, unlike what would happen under constant treatment effects. We propose an alternative difference-in-differences estimator, robust to heterogeneous effects and immune to the contamination problem. In the application we consider, the TWFE regression identifies a highly non-convex combination of effects, with large contamination weights, and one of its coefficients significantly differs from our heterogeneity-robust estimator.
Male gender as a poor prognostic factor in Medullary Thyroid Carcinoma: behaviour or biological difference?

PURPOSE Due to the low incidence and heterogeneous behaviour of medullary thyroid carcinoma (MTC), its prognostic factors are still not well stablished. While several large studies have investigated the impact of gender in differentiated thyroid cancer (DTC), its role in MTC outcomes remains controversial. We aim to identify MTC prognostic features, specially focusing on the role of gender. METHODS Retrospective analysis of 76 patients diagnosed with MTC between 1984 and 2018 at a Portuguese Comprehensive Cancer Center. RESULTS Patients presented a median age at diagnosis of 49 years and multiple endocrine neoplasia type 2 (MEN2) was identified in 27.6% of them, with those individuals being significantly younger (P<0.001). Most cases were diagnosed as stage IV disease (46.9%), except for the subgroup detected through pre-symptomatic genetic screening (55.6% at stage I). The 5- and 10-year survival rates were 87.6% and 75.6%, respectively. Univariate analysis identified male gender (P=0.010), age ≥45 years (P=0.007), presence of distant metastasis at diagnosis (P<0.01), capsule invasion (P=0.004), extrathyroidal invasion (P=0.003) and absence of biochemical cure after surgery (P=0.042) as having a negative impact on prognosis. On multivariate analysis, male gender (P=0.046) remained an independent predictor of mortality, as well as an older age (P<0.001) and the presence of distant metastases (P=0.012). CONCLUSIONS Male gender independently predicted worse survival in MTC patients even after adjusting for age and disease stage. The few older studies on the topic pointed to a behavioural explanation regarding medical care seeking patterns by men, but our study and newer genetic and basic-science oriented publications raise the possibility of a true biological difference between genders in the tumourigenesis of MTC that should me further investigated.
Exploring microbiome engineering as a strategy for improved thermal tolerance in Exaiptasia diaphana

Fourteen percent of all living coral, equivalent to more than all the coral on the Great Barrier Reef, has died in the past decade as a result of climate change‐driven bleaching. Inspired by the ‘oxidative stress theory of coral bleaching’, we investigated whether a bacterial consortium designed to scavenge free radicals could integrate into the host microbiome and improve thermal tolerance of the coral model, Exaiptasia diaphana.
The Longitudinal Association of Late-Life Visual and Hearing Difficulty and Cognitive Function: The Role of Social Isolation

Objectives Sensory impairments are prevalent among older adults and have been associated with cognitive challenges in later life, yet mechanisms are less well understood. We examined the mediating role of social isolation in the longitudinal relationship between self-reported sensory difficulty and impaired cognitive functioning among older adults. Methods Data were taken from the NHATS Study, an annual survey of Medicare beneficiaries’ age ≥ 65. Participants (N = 6,338) provided data at Rounds 5, 6, and 7 (2015, 2016, 2017). Structural equation models were estimated to test longitudinal direct and indirect associations. Results All sensory difficulties were negatively associated with all cognitive functioning measures cross-sectionally through social isolation. Longitudinally, vision difficulty and dual sensory difficulty were indirectly associated with cognitive functioning across time. Hearing difficulty had no longitudinal indirect associations with cognitive functioning through social isolation. Discussion Social isolation is an important pathway through which late-life vision difficulty is associated with decreased cognitive function.