Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Sentence Representations with Consensus Maximisation

Consensus maximisation learning can provide self-supervision when different views are available of the same data. The distributional hypothesis provides another form of useful self-supervision from adjacent sentences which are plentiful in large unlabelled corpora. Motivated by the observation that different learning architectures tend to emphasise different aspects of sentence meaning, we present a new self-supervised learning framework for learning sentence representations which minimises the disagreement between two views of the same sentence where one view encodes the sentence with a recurrent neural network (RNN), and the other view encodes the same sentence with a simple linear model. After learning, the individual views (networks) result in higher quality sentence representations than their single-view learnt counterparts (learnt using only the distributional hypothesis) as judged by performance on standard downstream tasks. An ensemble of both views provides even better generalisation on both supervised and unsupervised downstream tasks. Also, importantly the ensemble of views trained with consensus maximisation between the two different architectures performs better on downstream tasks than an analogous ensemble made from the single-view trained counterparts.
Topological manifold bundles and the $A$-theory assembly map

We give a new proof of an index theorem for fiber bundles of compact topological manifolds due to Dwyer, Weiss, and Williams, which asserts that the parametrized $A$-theory characteristic of such a fiber bundle factors canonically through the assembly map of $A$-theory. Furthermore our main result shows a refinement of this statement by providing such a factorization for an extended $A$-theory characteristic, defined on the parametrized topological cobordism category. The proof uses a convenient framework for bivariant theories and recent results of Gomez-Lopez and Kupers on the homotopy type of the topological cobordism category. We conjecture that this lift of the extended $A$-theory characteristic becomes highly connected as the manifold dimension increases.
Learning Graph Neural Networks with Noisy Labels

We study the robustness to symmetric label noise of GNNs training procedures. By combining the nonlinear neural message-passing models (e.g. Graph Isomorphism Networks, GraphSAGE, etc.) with loss correction methods, we present a noise-tolerant approach for the graph classification task. Our experiments show that test accuracy can be improved under the artificial symmetric noisy setting.
Universality of population recovery patterns after disasters

Despite the rising importance of enhancing community resilience to disasters, our understanding on how communities recover from catastrophic events is limited. Here we study the population recovery dynamics of disaster affected regions by observing the movements of over 2.5 million mobile phone users across three countries before, during and after five major disasters. We find that, although the regions affected by the five disasters have significant differences in socio-economic characteristics, we observe a universal recovery pattern where displaced populations return in an exponential manner after all disasters. Moreover, the heterogeneity in initial and long-term displacement rates across communities across the three countries were explained by a set of key universal factors including the community's median income level, population size, housing damage rate, and the connectedness to other cities. These universal properties of recovery dynamics extracted from large scale evidence could impact efforts on urban resilience and sustainability across various disciplines.
Spectral Properties of NGC 4151 and the Estimation of Black Hole Mass Using TCAF Solution

We present X-ray spectral analysis of Seyfert 1.5 Active Galactic Nuclei (AGNs) NGC 4151 using NuSTAR observations during 2012. This is the first attempt to fit AGN data using the physical Two Component Advective flow (TCAF) solution. We disentangle the continuum emission properties of the source in the energy range 3.0–70.0 keV using the spectrum obtained from the TCAF model. This model was used as an additive local model directly in XSPEC. Additionally, we used a power-law component, to take care of possible X-ray contribution from the jet, which is not incorporated in the present version of TCAF. Our primary aim is to obtain the flow properties and the mass of the central supermassive black hole from the available archival data. Our best estimate of the average mass obtained from spectral fits of three observations is MBH = 3.03+0.26−0.26 × 107 M⊙. This is consistent with earlier estimations in the literature such as reverberation mapping, gas kinematics, and stellar dynamics around black holes. We also discuss the accretion dynamics and the flow geometry on the basis of model-fitted physical parameters. Model-fitted disk accretion rate is found to be lower than the low angular momentum halo accretion rate, indicating that the source was in a hard state during the observation.
Hot Electron Dynamics in Plasmonic Thermionic Emitters

Thermionic converters generate electricity from thermal energy in a power cycle based on vacuum emission of electrons. While thermodynamically efficient, practical implementations are limited by the extreme temperatures required for electron emission (> 1500 K). Here, we show how metal nanostructures that support resonant plasmonic absorption enable an alternative strategy. High electronic temperatures required for efficient vacuum emission can be maintained during steady-state optical absorption while the lattice temperature remains within the range of thermal stability, below 600 K. We have also developed an optical thermometry technique based on anti-Stokes Raman spectroscopy that confirms these unique electron dynamics. Thermionic devices constructed from plasmonic absorbers show performance that can out-compete other strategies of concentrated solar power conversion in terms of efficiency and thermal stability.
Autonomous Air Traffic Controller: A Deep Multi-Agent Reinforcement Learning Approach

Air traffic control is a real-time safety-critical decision making process in highly dynamic and stochastic environments. In today's aviation practice, a human air traffic controller monitors and directs many aircraft flying through its designated airspace sector. With the fast growing air traffic complexity in traditional (commercial airliners) and low-altitude (drones and eVTOL aircraft) airspace, an autonomous air traffic control system is needed to accommodate high density air traffic and ensure safe separation between aircraft. We propose a deep multi-agent reinforcement learning framework that is able to identify and resolve conflicts between aircraft in a high-density, stochastic, and dynamic en-route sector with multiple intersections and merging points. The proposed framework utilizes an actor-critic model, A2C that incorporates the loss function from Proximal Policy Optimization (PPO) to help stabilize the learning process. In addition we use a centralized learning, decentralized execution scheme where one neural network is learned and shared by all agents in the environment. We show that our framework is both scalable and efficient for large number of incoming aircraft to achieve extremely high traffic throughput with safety guarantee. We evaluate our model via extensive simulations in the BlueSky environment. Results show that our framework is able to resolve 99.97% and 100% of all conflicts both at intersections and merging points, respectively, in extreme high-density air traffic scenarios.
Mechanical failure of the latex condom in a cohort of women at high STD risk.

BACKGROUND AND OBJECTIVES Mechanical failure may reduce the efficacy of condoms. Little is known about frequency and determinants of condom failure in groups at high risk of sexually transmitted diseases (STD). GOAL To measure condom breakage and slippage rates and evaluate potential determinants of failure among women attending a public STD clinic. STUDY DESIGN Women attending an STD clinic participated in a 6-month prospective study of barrier contraception for the prevention of STD. They completed sexual diaries that were reviewed at monthly follow-up visits. No data were collected from the male partners. Baseline characteristics of the participants and time-dependent behaviors were evaluated as potential determinants of condom failure. RESULTS Of 21,852 condoms used by 892 women, 500 broke during intercourse (2.3%) and 290 slipped (1.3%). Breakage was more common among young, black, single nulliparae who engaged in high-risk behavior. Slippage was more common among married women with children. Failure rates decreased with condom use, with coital frequency, and with use of spermicides. CONCLUSION User characteristics and experience are determinants of breakage and slippage, which are often regarded only as the effect of product design flaws. Attention to modifiable determinants of failure may improve user counseling and product labeling.
Towards Instance-Level Image-To-Image Translation

Unpaired Image-to-image Translation is a new rising and challenging vision problem that aims to learn a mapping between unaligned image pairs in diverse domains. Recent advances in this field like MUNIT and DRIT mainly focus on disentangling content and style/attribute from a given image first, then directly adopting the global style to guide the model to synthesize new domain images. However, this kind of approaches severely incurs contradiction if the target domain images are content-rich with multiple discrepant objects. In this paper, we present a simple yet effective instance-aware image-to-image translation approach (INIT), which employs the fine-grained local (instance) and global styles to the target image spatially. The proposed INIT exhibits three import advantages: (1) the instance-level objective loss can help learn a more accurate reconstruction and incorporate diverse attributes of objects; (2) the styles used for target domain of local/global areas are from corresponding spatial regions in source domain, which intuitively is a more reasonable mapping; (3) the joint training process can benefit both fine and coarse granularity and incorporates instance information to improve the quality of global translation. We also collect a large-scale benchmark for the new instance-level translation task. We observe that our synthetic images can even benefit real-world vision tasks like generic object detection.
Trends in College Binge Drinking During a Period of Increased Prevention Efforts: Findings from 4 Harvard School of Public Health College Alcohol Study Surveys: 1993–2001

Abstract The 2001 Harvard School of Public Health College Alcohol Study surveyed students at 119 4-year colleges that participated in the 1993, 1997, and 1999 studies. Responses in the 4 survey years were compared to determine trends in heavy alcohol use, alcohol-related problems, and encounters with college and community prevention efforts. In 2001, approximately 2 in 5 (44.4%) college students reported binge drinking, a rate almost identical to rates in the previous 3 surveys. Very little change in overall binge drinking occurred at the individual college level. The percentages of abstainers and frequent binge drinkers increased, a polarization of drinking behavior first noted in 1997. A sharp rise in frequent binge drinking was noted among students attending all-women's colleges. Other significant changes included increases in immoderate drinking and harm among drinkers. More students lived in substance-free housing and encountered college educational efforts and sanctions resulting from their alcohol use.
Photometry of high-redshift blended galaxies using deep learning

The new generation of deep photometric surveys requires unprecedentedly precise shape and photometry measurements of billions of galaxies to achieve their main science goals. At such depths, one major limiting factor is the blending of galaxies due to line-of-sight projection, with an expected fraction of blended galaxies of up to 50 per cent. This proof-of-concept work explores for the first time the use of deep neural networks to estimate the photometry of blended pairs of galaxies in space-based monochrome images similar to the ones that will be delivered by the Euclidspace telescope under simplified idealized conditions. Using a clean sample of isolated galaxies from the CANDELS survey, we artificially blend them and train two different network models to recover the photometry of the two galaxies. We show that our approach can recover the original photometry of the galaxies before being blended with $\sim 7{{\ \rm per\ cent}}$ mean absolute percentage error on flux estimations without any human intervention and without any assumption on the galaxy shape. This represents an improvement of at least a factor of 4 compared to the classical SExtractor approach. We also show that, forcing the network to simultaneously estimate fractional segmentation maps results in a slightly improved photometry. All data products and codes have been made public to ease the comparison with other approaches on a common data set. See https://github.com/aboucaud/coindeblend.
NAMSG: An Efficient Method For Training Neural Networks

We introduce NAMSG, an adaptive first-order algorithm for training neural networks. The method is efficient in computation and memory, and is straightforward to implement. It computes the gradients at configurable remote observation points, in order to expedite the convergence by adjusting the step size for directions with different curvatures in the stochastic setting. It also scales the updating vector elementwise by a nonincreasing preconditioner to take the advantages of AMSGRAD. We analyze the convergence properties for both convex and nonconvex problems by modeling the training process as a dynamic system, and provide a guideline to select the observation distance without grid search. A data-dependent regret bound is proposed to guarantee the convergence in the convex setting. Experiments demonstrate that NAMSG works well in practice and compares favorably to popular adaptive methods, such as ADAM, NADAM, and AMSGRAD.
PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph

Despite some exciting progress on high-quality image generation from structured(scene graphs) or free-form(sentences) descriptions, most of them only guarantee the image-level semantical consistency, i.e. the generated image matching the semantic meaning of the description. They still lack the investigations on synthesizing the images in a more controllable way, like finely manipulating the visual appearance of every object. Therefore, to generate the images with preferred objects and rich interactions, we propose a semi-parametric method, PasteGAN, for generating the image from the scene graph and the image crops, where spatial arrangements of the objects and their pair-wise relationships are defined by the scene graph and the object appearances are determined by the given object crops. To enhance the interactions of the objects in the output, we design a Crop Refining Network and an Object-Image Fuser to embed the objects as well as their relationships into one map. Multiple losses work collaboratively to guarantee the generated images highly respecting the crops and complying with the scene graphs while maintaining excellent image quality. A crop selector is also proposed to pick the most-compatible crops from our external object tank by encoding the interactions around the objects in the scene graph if the crops are not provided. Evaluated on Visual Genome and COCO-Stuff dataset, our proposed method significantly outperforms the SOTA methods on Inception Score, Diversity Score and Frechet Inception Distance. Extensive experiments also demonstrate our method's ability to generate complex and diverse images with given objects.
Assisted concentration of Gaussian resources

In spite of their outstanding experimental relevance, Gaussian operations in continuous-variable quantum systems are subjected to fundamental limitations, as it is known that general resources cannot be distilled within the Gaussian paradigm. We show that these limitations can be overcome by considering a collaborative setting where one party distills resources with the aid of another party, whose operations are assumed to be Gaussian but are otherwise unrestricted; the two parties can only communicate classically. We show that in single-shot scenarios, unlike in the well-known case of entanglement theory, two-way classical communication does not lead to any improvement over one-way classical communication from the aiding party to the aided party. We then provide a concise general expression for the Gaussian resource of assistance, i.e., the distillable resource when the aiding party holds a purification of the aided party's state. To demonstrate the usefulness of this general expression, we apply it to two important kinds of resources, squeezing and entanglement, and find some simple analytic solutions. In the case of entanglement theory, we are able to find general upper bounds on the regularized Gaussian entanglement of assistance, and to establish additivity for tensor powers of thermal states. This allows us to draw a quantitative and enlightening comparison with the performance of assisted entanglement distillation in the non-Gaussian setting. On the technical side, we develop some variational expressions to handle functions of symplectic eigenvalues that may be of independent interest. Our results suggest further potential for Gaussian operations to play a major role in practical quantum information processing protocols.
Mean velocity and effective diffusion constant for translocation of biopolymer chains across membrane

Chaperone-assisted translocation through a nanopore embedded in membrane holds a prominent role in the transport of biopolymers. Inspired by classical Brownian ratchet, we develop a theoretical framework characterizing such a translocation process through a master equation approach. In this framework, the polymer chain, provided with reversible binding of chaperones, undergoes forward/backward diffusion, which is rectified by chaperones. We drop the assumption of timescale separation and keep the length of a polymer chain finite, both of which happen to be key points in most of the previous studies. Our framework makes it accessible to derive analytical expressions for mean translocation velocity and an effective diffusion constant in a stationary state, which is the basis of a comprehensive understanding of the dynamics of such a process. Generally, the translocation of polymer chains across a membrane consist of three subprocesses: initiation, termination, and translocation of the main body part of a polymer chain, where the translocation of the main body part depends on both the binding/unbinding kinetics of chaperones and the diffusion of the biopolymer chain. This is the main concern of this study. Our results show that the increase of the forward/backward diffusion rate of a polymer chain and the binding/unbinding ratio of chaperones both raise the mean translocation velocity of a polymer chain, and the mean velocity finally reaches a saturation amount with an extremely rapid diffusion or extremely high binding/unbinding ratio. Roughly speaking, the dependence of effective diffusion constant on these two major processes achieves similar behavior. Besides, longer polymer chains employ higher velocity when the diffusion rate and binding/unbinding ratio are both large and similar results hold for polymer chains that are not too long in terms of the effects on the effective diffusion constant.
Repair mechanisms of bone marrow mesenchymal stem cells in myocardial infarction

• Introduction • Transdifferentiation ‐ MSCs differentiate into cardiomyocytes ‐ MSCs differentiate into vascular cells • Paracrine effects ‐ Endogenous cardiac regeneration induced by paracrine effects ‐ Neovascularization induced by paracrine effects ‐ Anti‐inflammatory effect of MSCs ‐ Anti‐apoptotic effects by MSCs ‐ Cardiac remodelling induced by paracrine effects ‐ Paracrine‐mediated cardiac contractility ‐ Cardiac metabolic modulation by MSCs • Other potential effects ‐ Cardiac nerve sprouting ‐ Anti‐arrhythmic potential • Conclusion
Question Relatedness on Stack Overflow: The Task, Dataset, and Corpus-inspired Models

Domain-specific community question answering is becoming an integral part of professions. Finding related questions and answers in these communities can significantly improve the effectiveness and efficiency of information seeking. Stack Overflow is one of the most popular communities that is being used by millions of programmers. In this paper, we analyze the problem of predicting knowledge unit (question thread) relatedness in Stack Overflow. In particular, we formulate the question relatedness task as a multi-class classification problem with four degrees of relatedness. We present a large-scale dataset with more than 300K pairs. To the best of our knowledge, this dataset is the largest domain-specific dataset for Question-Question relatedness. We present the steps that we took to collect, clean, process, and assure the quality of the dataset. The proposed dataset Stack Overflow is a useful resource to develop novel solutions, specifically data-hungry neural network models, for the prediction of relatedness in technical community question-answering forums. We adopt a neural network architecture and a traditional model for this task that effectively utilize information from different parts of knowledge units to compute the relatedness between them. These models can be used to benchmark novel models, as they perform well in our task and in a closely similar task.
On the Difficulty of Evaluating Baselines: A Study on Recommender Systems

Numerical evaluations with comparisons to baselines play a central role when judging research in recommender systems. In this paper, we show that running baselines properly is difficult. We demonstrate this issue on two extensively studied datasets. First, we show that results for baselines that have been used in numerous publications over the past five years for the Movielens 10M benchmark are suboptimal. With a careful setup of a vanilla matrix factorization baseline, we are not only able to improve upon the reported results for this baseline but even outperform the reported results of any newly proposed method. Secondly, we recap the tremendous effort that was required by the community to obtain high quality results for simple methods on the Netflix Prize. Our results indicate that empirical findings in research papers are questionable unless they were obtained on standardized benchmarks where baselines have been tuned extensively by the research community.