Miracle-dz/UniFAR-FTUs huggingface dataset:
G-computation and inverse probability weighting for time-to-event outcomes: a comparative study

Inverse probability weighting is increasingly used in causal inference, but the g-computation constitutes a promising alternative. We aimed to compare the performances of these methods with time-to-event outcomes. Given the limitations of the interpretability of the hazard ratio for causal inference, our target estimand of interest is the difference in restricted mean survival times. We report the findings of an extensive simulation study, showing that both inverse probability weighting and g-computation were unbiased under a correct model specification, but g-computation was generally more efficient. We also analyse two real-life datasets to illustrate these results. We update the R package RISCA to facilitate the implementation of g-computation.
EFFECT OF DIETARY LUPIN (Lupinus angustifolius) WHOLE- SEED MEAL PROTEIN ON THE GROWTH OF TILAPIA (Oreochromis niloticus) REARED IN TANKS

SUMMARY A factorial feeding experiment (2 × 5) was conducted in indoor circular 1 m 3 fiberglass tanks to evaluate the effect of different levels of un-cooked and cooked lupin whole-seed meal protein, at the expense of fish meal (FM) protein, on the growth of Nile tilapia, Oreochromis niloticus, fingerlings (3 g/fish). Nine isocaloric and isonitogenous diets (25 % protein) were formulated where FM protein was replaced partially by 0, 15, 30, 45 and 60 % of un-cooked or cooked whole lupin seed meal protein. Tanks were stocked with 15 fish/tank in duplicate groups/treatment. Fish were fed daily at a rate of 3 % of fish live body weight for 14 weeks. The results indicated that fish receiving diets containing uncooked lupin protein were better in growth performance, feed and nutrient utilization than those fed cooked lupin protein. Growth performance, feed intake, feed conversion ratio and energy utilization began to deteriorate significantly at 45 % lupin protein. Protein efficiency ratio and protein productive values declined significantly subsequent to 30 % lupin protein level. Fish fed diets containing 0, 15 and 30 % lupin protein gained preferable FCR. Dry matter and hepatosomatic index were not affected by lupin protein levels or lupin treatments whil, ash and gross energy contents were affected by lupin protein levels but not lupin treatments. Meanwhile, crude protein and ether extract were affected by both lupin protein levels and lupin treatments. The economic evaluation was in favor of diets containing 15, 30 and 60 % un-cooked in addition to 30 % cooked lupin protein. However, the results of this study indicate that up to and including 30 % lupin protein could replace FM protein in Nile tilapia diets without any retardation in growth.
Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification

Adversarial robustness has become a fundamental requirement in modern machine learning applications. Yet, there has been surprisingly little statistical understanding so far. In this paper, we provide the first result of the optimal minimax guarantees for the excess risk for adversarially robust classification, under Gaussian mixture model proposed by \cite{schmidt2018adversarially}. The results are stated in terms of the Adversarial Signal-to-Noise Ratio (AdvSNR), which generalizes a similar notion for standard linear classification to the adversarial setting. For the Gaussian mixtures with AdvSNR value of $r$, we establish an excess risk lower bound of order $\Theta(e^{-(\frac{1}{8}+o(1)) r^2} \frac{d}{n})$ and design a computationally efficient estimator that achieves this optimal rate. Our results built upon minimal set of assumptions while cover a wide spectrum of adversarial perturbations including $\ell_p$ balls for any $p \ge 1$.
Optimization at the boundary of the tensor network variety

Tensor network states form a variational ansatz class widely used, both analytically and numerically, in the study of quantum many-body systems. It is known that if the underlying graph contains a cycle, e.g. as in projected entangled pair states (PEPS), then the set of tensor network states of given bond dimension is not closed. Its closure is the tensor network variety. Recent work has shown that states on the boundary of this variety can yield more efficient representations for states of physical interest, but it remained unclear how to systematically find and optimize over such representations. We address this issue by defining a new ansatz class of states that includes states at the boundary of the tensor network variety of given bond dimension. We show how to optimize over this class in order to find ground states of local Hamiltonians by only slightly modifying standard algorithms and code for tensor networks. We apply this new method to a different of models and observe favorable energies and runtimes when compared with standard tensor network methods.
ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graph

We propose a knowledge-enhanced approach, ERNIE-ViL, which incorporates structured knowledge obtained from scene graphs to learn joint representations of vision-language. ERNIE-ViL tries to build the detailed semantic connections (objects, attributes of objects and relationships between objects) across vision and language, which are essential to vision-language cross-modal tasks. Utilizing scene graphs of visual scenes, ERNIE-ViL constructs Scene Graph Prediction tasks, i.e., Object Prediction, Attribute Prediction and Relationship Prediction tasks in the pre-training phase. Specifically, these prediction tasks are implemented by predicting nodes of different types in the scene graph parsed from the sentence. Thus, ERNIE-ViL can learn the joint representations characterizing the alignments of the detailed semantics across vision and language. After pre-training on large scale image-text aligned datasets, we validate the effectiveness of ERNIE-ViL on 5 cross-modal downstream tasks. ERNIE-ViL achieves state-of-the-art performances on all these tasks and ranks the first place on the VCR leaderboard with an absolute improvement of 3.7%.
SCE: Scalable Network Embedding from Sparsest Cut

Large-scale network embedding is to learn a latent representation for each node in an unsupervised manner, which captures inherent properties and structural information of the underlying graph. In this field, many popular approaches are influenced by the skip-gram model from natural language processing. Most of them use a contrastive objective to train an encoder which forces the embeddings of similar pairs to be close and embeddings of negative samples to be far. A key of success to such contrastive learning methods is how to draw positive and negative samples. While negative samples that are generated by straightforward random sampling are often satisfying, methods for drawing positive examples remains a hot topic. In this paper, we propose SCE for unsupervised network embedding only using negative samples for training. Our method is based on a new contrastive objective inspired by the well-known sparsest cut problem. To solve the underlying optimization problem, we introduce a Laplacian smoothing trick, which uses graph convolutional operators as low-pass filters for smoothing node representations. The resulting model consists of a GCN-type structure as the encoder and a simple loss function. Notably, our model does not use positive samples but only negative samples for training, which not only makes the implementation and tuning much easier, but also reduces the training time significantly. Finally, extensive experimental studies on real world data sets are conducted. The results clearly demonstrate the advantages of our new model in both accuracy and scalability compared to strong baselines such as GraphSAGE, G2G and DGI.
Moduli spaces of semistable pairs on projective Deligne-Mumford stacks

We generalize the construction of a moduli space of semistable pairs parametrizing isomorphism classes of morphisms from a fixed coherent sheaf to any sheaf with fixed Hilbert polynomial under a notion of stability to the case of projective Deligne-Mumford stacks. We study the deformation and obstruction theories of stable pairs, and then prove the existence of virtual fundamental classes for some cases of dimension two and three. This leads to a definition of Pandharipande-Thomas invariants on three-dimensional smooth projective Deligne-Mumford stacks.
Accurate bulk properties of nuclei from 
A=2
 to 
∞
 from potentials with 
Δ
 isobars

We optimize $\Delta$-full nuclear interactions from chiral effective field theory. The low-energy constants of the contact potentials are constrained by two-body scattering phase shifts, and by properties of bound-state of $A=2$ to $4$ nucleon systems and nuclear matter. The pion-nucleon couplings are taken from a Roy-Steiner analysis. The resulting interactions yield accurate binding energies and radii for a range of nuclei from $A=16$ to $A=132$, and provide accurate equations of state for nuclear matter and realistic symmetry energies. Selected excited states are also in agreement with data.
Isolation of pathogenic Tenacibaculum maritimum-related organisms from diseased turbot and sole cultured in the Northwest of Spain

The present study deals with the isolation and characterization of filamentous bacteria, phenotypically related to Tenacibaculum maritimum, from Senegalese sole, (Solea senegalensis, Kaup) and turbot (Scophthalmus maximus, L.) cultured in the Northwest of Spain. Diseased fish showed the typical signs observed in fish affected by marine flexibacteriosis (eroded mouth, rotten fins, shallow skin lesions and paleness of internal organs). The isolated bacteria showed similar morphological, physiological and biochemical characteristics to the Tenacibaculum maritimum strains used as reference in the study. However, results of PCR and serological analysis indicated that the isolated bacteria were antigenically and genetically different from T. maritimum species. Pathogenicity assays demonstrated that these T. maritimum-like bacteria are virulent for turbot and sole with bacterial doses ranging from 9.0 x10 5 to 9.0 x 10 7 colony forming units (CFU) per fish.
Mirror Symmetry and smoothing Gorenstein toric affine 3-folds

We state two conjectures that together allow one to describe the set of smoothing components of a Gorenstein toric affine 3-fold in terms of a combinatorially defined and easily studied set of Laurent polynomials called 0-mutable polynomials. We explain the origin of the conjectures in mirror symmetry and present some of the evidence.
The impact of sward height, forage quality and competitive conditions on foraging behaviour of free-ranging rabbits (Oryctolagus cuniculus L.)

The habitat choice of the small hindgut fermenter, the European rabbit (Oryctolagus cuniculus L.), was studied in relation to sward height, forage quality, population size fluctuations and spatial distribution of burrows in a temperate grassland. In a multi-phase differential clipping experiment with alternating short and tall vegetation strips, rabbits tended to graze near the closest burrows in situations of equal vegetation heights, while a clear preference for short swards was found during summer (July). In this period, general crude protein content was significantly lower than in spring (April) and autumn (September), apparently leading to a potential forage quality deficit. The summer behavioural pattern with short sward preference coincided with the relatively higher crude protein content of short swards as compared to tall swards in this period, and with higher intraspecific competition, due to significantly larger numbers of animals present in summer. In autumn, rabbit densities decreased, while crude protein content of both short and tall vegetation increased to a higher, though not significantly different level, comparable with spring crude protein content. In those conditions, significant preference for low vegetation height could no longer be detected. Data suggest that selection for nutritive quality appears when intraspecific competition is high and nutritive quality remains under a certain threshold value. When, in autumn, competition decreases and nutritive quality increases again, short sward preference disappears. We conclude that short sward preference is primarily caused by the better forage quality of re-growth in periods of forage quality limitation, while this preference disappears when forage quality limitation no longer occurs.
Mechanisms of dynamic near-wake modulation of a utility-scale wind turbine

Abstract The current study uses large eddy simulations to investigate the transient response of a utility-scale wind turbine wake to dynamic changes in atmospheric and operational conditions, as observed in previous field-scale measurements. Most wind turbine wake investigations assume quasi-steady conditions, but real wind turbines operate in a highly stochastic atmosphere, and their operation (e.g. blade pitch, yaw angle) changes constantly in response. Furthermore, dynamic control strategies have been recently proposed to optimize wind farm power generation and longevity. Therefore, improved understanding of dynamic wake behaviours is essential. First, changes in blade pitch are investigated and the wake expansion response is found to display hysteresis as a result of flow inertia. The time scales of the wake response to different pitch rates are quantified. Next, changes in wind direction with different time scales are explored. Under short time scales, the wake deflection is in the opposite direction of that observed under quasi-steady conditions. Finally, yaw changes are implemented at different rates, and the maximum inverse wake deflection and time scale are quantified, showing a clear dependence on yaw rate. To gain further physical understanding of the mechanism behind the inverse wake deflection, the streamwise vorticity in different parts of the wake is quantified. The results of this study provide guidance for the design of advanced wake flow control algorithms. The lag in wake response observed for both blade pitch and yaw changes shows that proposed dynamic control strategies must implement turbine operational changes with a time scale of the order of the rotor time scale or slower.
Model-based Asynchronous Hyperparameter and Neural Architecture Search

We introduce a model-based asynchronous multi-fidelity method for hyperparameter and neural architecture search that combines the strengths of asynchronous Hyperband and Gaussian process-based Bayesian optimization. At the heart of our method is a probabilistic model that can simultaneously reason across hyperparameters and resource levels, and supports decision-making in the presence of pending evaluations. We demonstrate the effectiveness of our method on a wide range of challenging benchmarks, for tabular data, image classification and language modelling, and report substantial speed-ups over current state-of-the-art methods. Our new methods, along with asynchronous baselines, are implemented in a distributed framework which will be open sourced along with this publication.
Improving robustness against common corruptions by covariate shift adaptation

Today's state-of-the-art machine vision models are vulnerable to image corruptions like blurring or compression artefacts, limiting their performance in many real-world applications. We here argue that popular benchmarks to measure model robustness against common corruptions (like ImageNet-C) underestimate model robustness in many (but not all) application scenarios. The key insight is that in many scenarios, multiple unlabeled examples of the corruptions are available and can be used for unsupervised online adaptation. Replacing the activation statistics estimated by batch normalization on the training set with the statistics of the corrupted images consistently improves the robustness across 25 different popular computer vision models. Using the corrected statistics, ResNet-50 reaches 62.2% mCE on ImageNet-C compared to 76.7% without adaptation. With the more robust AugMix model, we improve the state of the art from 56.5% mCE to 51.0% mCE. Even adapting to a single sample improves robustness for the ResNet-50 and AugMix models, and 32 samples are sufficient to improve the current state of the art for a ResNet-50 architecture. We argue that results with adapted statistics should be included whenever reporting scores in corruption benchmarks and other out-of-distribution generalization settings.
Investigating the Effects of Mobility Metrics in Mobile Ad Hoc Networks

Mobile Ad Hoc Networks (MANETs) are formed by a collection of mobile nodes (MNs) that are capable of moving from one location to another location. These networks are widely identified by their unique characteristics such as lack of infrastructure, mobility and multi-hop communication. Unlike traditional (wired) networks, MNs in MANETs do not rely on any infrastructure or central management. Mobility allows MNs to move at different values of speed. Multi-hop communication is used to deliver data across the entire network. Due to mobility and changing network topology, the performance of these networks is significantly affected by the choice of mobility models and routing protocols. Our research work aims to evaluate the effects of mobility metrics on distinguishing between entity and group mobility models in MANETs. In addition, we demonstrate the interactions between mobility metrics and performance metrics. We also investigate how effective are the mobility metrics and which metrics can clearly distinguish between entity and group mobility models? We performed extensive simulations using network simulator, ns-2.35, to capture mobility metrics as well as different performance metrics. Simulation results reveal the efficiency of mobility metrics on distinguishing between entity and group mobility models. We also obtain useful interactions between mobility metrics and performance metrics. The results presented in this paper provide new insights into the variability of mobility metrics in MANETs. Furthermore, our simulation results reveal better understanding of the relationships between mobility metrics (e.g., relative speed, node degree, network partitions etc.) and performance metrics (e.g., packet delivery ratio, end-to-end delay and normalized routing load
Robustness of quantized transport through edge states of finite length: Imaging current density in Floquet topological versus quantum spin and anomalous Hall insulators

The theoretical analysis of topological insulators (TIs) has been traditionally focused on infinite homogeneous crystals with band gap in the bulk and nontrivial topology of their wavefunctions, or infinite wires whose boundaries host surface or edge metallic states. However, experimental devices contain finite-size topological region attached to normal metal (NM) leads, which poses a question about how precise is quantization of longitudinal conductance and how electrons transition from topologically trivial NM leads into the edge states. This is particularly pressing issues for recently conjectured two-dimensional (2D) Floquet TI where electrons flow from time-independent NM leads into time-dependent edge states---the very recent experimental realization of Floquet TI using graphene irradiated by circularly polarized light did not exhibit either quantized longitudinal or Hall conductance. Here we employ charge conserving solution for Floquet-nonequlibrium Green functions (NEGFs) of irradiated graphene nanoribbon to compute longitudinal two-terminal conductance, as well as spatial profiles of local current density as electrons propagate from NM leads into the Floquet TI. In the case of Floquet TI both bulk and edge local current densities contribute equally to total current, which leads to longitudinal conductance below the expected quantized plateau that is slightly reduced by edge vacancies. We propose two experimental schemes to detect coexistence of bulk and edge current densities within Floquet TI: (i) drilling a nanopore in the interior of irradiated region of graphene will induce backscattering of bulk current density, thereby reducing longitudinal conductance by $\sim 28$%; (ii) imaging of magnetic field produced by local current density using diamond NV centers.
Minimax estimation of smooth optimal transport maps

Brenier's theorem is a cornerstone of optimal transport that guarantees the existence of an optimal transport map $T$ between two probability distributions $P$ and $Q$ over $\mathbb{R}^d$ under certain regularity conditions. The main goal of this work is to establish the minimax estimation rates for such a transport map from data sampled from $P$ and $Q$ under additional smoothness assumptions on $T$. To achieve this goal, we develop an estimator based on the minimization of an empirical version of the semi-dual optimal transport problem, restricted to truncated wavelet expansions. This estimator is shown to achieve near minimax optimality using new stability arguments for the semi-dual and a complementary minimax lower bound. Furthermore, we provide numerical experiments on synthetic data supporting our theoretical findings and highlighting the practical benefits of smoothness regularization. These are the first minimax estimation rates for transport maps in general dimension.
Conformal mapping of the Borel plane: Going beyond perturbative QCD

The power corrections in the Operator Product Expansion (OPE) of QCD correlators can be viewed mathematically as an illustration of the transseries concept, which allows to recover a function from its asymptotic divergent expansion. Alternatively, starting from the divergent behavior of the perturbative QCD encoded in the singularities in the Borel plane, a modified expansion can be defined by means of the conformal mapping of this plane. A comparison of the two approaches concerning their ability to recover nonperturbative properties of the true correlator was not explored up to now. In the present paper, we make a first attempt to investigate this problem. We use for illustration the Adler function and observables expressed as integrals of this function along contours in the complex energy plane. We show that the expansions based on the conformal mapping of the Borel plane go beyond finite-order perturbation theory, containing an infinite number of terms when reexpanded in powers of the coupling. Moreover, the expansion functions exhibit nonperturbative features of the true function, while the expansions have a tamed behavior at large orders are are expected even to be convergent. Using these properties, we argue that there are no mathematical reasons for supplementing the expansions based on the conformal mapping of the Borel plane by additional arbitrary power corrections. Therefore, we make the conjecture that they provide an alternative to the standard OPE in approximating the QCD correlator. This conjecture allows to slightly improve the accuracy of the strong coupling extracted from the hadronic $\tau$ decay width. Using the optimal expansions based on conformal mapping and the contour-improved prescription of renormalization-group resummation, we obtain $\alpha_s(m_\tau^2)=0.314 \pm 0.006$, which implies $\alpha_s(m_Z^2)=0.1179 \pm 0.0008$.