Miracle-dz/UniFAR-FTUs huggingface dataset:
On Size Generalization in Graph Neural Networks

Graph neural networks (GNNs) can process graphs of different sizes but their capacity to generalize across sizes is still not well understood. Size generalization is key to numerous GNN applications, from solving combinatorial optimization problems to learning in molecular biology. In such problems, obtaining labels and training on large graphs can be prohibitively expensive, but training on smaller graphs is possible. This paper puts forward the size-generalization question and characterizes important aspects of that problem theoretically and empirically. We show that even for very simple tasks, GNNs do not naturally generalize to graphs of larger size. Instead, their generalization performance is closely related to the distribution of patterns of connectivity and features and how that distribution changes from small to large graphs. Specifically, we show that in many cases, there are GNNs that can perfectly solve a task on small graphs but generalize poorly to large graphs and that these GNNs are encountered in practice. We then formalize size generalization as a domain-adaption problem and describe two learning setups where size generalization can be improved. First, as a self-supervised learning problem (SSL) over the target domain of large graphs. Second, as a semi-supervised learning problem when few samples are available in the target domain. We demonstrate the efficacy of these solutions on a diverse set of benchmark graph datasets.
Linguistically-Informed Transformations (LIT): A Method for Automatically Generating Contrast Sets

Although large-scale pretrained language models, such as BERT and RoBERTa, have achieved superhuman performance on in-distribution test sets, their performance suffers on out-of-distribution test sets (e.g., on contrast sets). Building contrast sets often requires human-expert annotation, which is expensive and hard to create on a large scale. In this work, we propose a Linguistically-Informed Transformation (LIT) method to automatically generate contrast sets, which enables practitioners to explore linguistic phenomena of interests as well as compose different phenomena. Experimenting with our method on SNLI and MNLI shows that current pretrained language models, although being claimed to contain sufficient linguistic knowledge, struggle on our automatically generated contrast sets. Furthermore, we improve models’ performance on the contrast sets by applying LIT to augment the training data, without affecting performance on the original data.
Velocity derivatives in a high Reynolds number turbulent boundary layer. Part III: Optimization of an SPIV experiment for derivative moments assessment

An SPIV experiment using two orthogonal planes simultaneously was performed in the LML boundary layer facility to specifically measure all of the derivative moments needed to estimate the dissipation rate of the Turbulence Kinetic Energy. The Reynolds number was $Re_\theta = 7500$ or $Re_\tau = 2300$. A detailed analysis of the errors in derivative measurements was carried out, as well as applying and using consistency checks derived from the continuity equation. The random noise error was quantified, and used to ``de-noise'' the derivative moments. A comparison with a DNS channel flow at comparable Reynolds number demonstrated the capability of the technique. The results were further validated using the recent theory developed by George and Stanislas 2020. The resulting data have been extensively used in parts I and II of the present contribution to study near wall dissipation. An important result of the present work is the provision of reliable rules for an accurate assessment of the dissipation in future PIV experiments.
Comparison of methods for deriving neural progenitor cells from nonhuman primate embryonic stem cells

Abstract Background Feeder-free monolayer culture and the suspension culture of embryoid bodies (EB) followed by adherent culture, rosette selection, and expansion are 2 methods for deriving neural progenitor cells (NPCs). Direct comparison of these 2 methods has not yet been reported. Objectives To compare the influence of NPC derivation methods on the properties of NPCs derived from rhesus monkey (Macaca mulatta) embryonic stem cells (rhESCs). Methods rhESCs were used to derive NPC lines using 2 different methods. EB were produced from a suspension culture of rhESC clumps and rhESCs were cultured in feeder-free monolayers on poly-L-ornithine/laminin coated plates. NPCs were derived by exposure to induction factors. Cell morphology, neural and nonneural lineage markers were evaluated. We measured the expression of nuclear receptor tailless (TLX), which acts as repressor of glial fibrillary acidic protein (GFAP) expression. Results NPCs were successful derived using either method, with homogenous populations based on the expression of nestin (>97%) and Pax6 (>99%) as shown by flow cytometry. No significant difference in NPC specific markers or ability to differentiate into neurons in vitro was found between the methods. However, the expression of GFAP was >400-fold higher in cells produced by the feeder-free method. This distinction was consistent with the lower expression of TLX. Conclusions NPCs derived by feeder-free and EB methods share similar morphology and properties. The elevated expression of GFAP and reduced expression of TLX in NPCs derived using the feeder-free method may explain their greater heterogeneity and tendency to differentiate toward cells of an astrocyte lineage.
Partial UV completion of P(X) from a curved field space

The k-essence theory is a prototypical class of scalar-field models that already gives rich phenomenology and has been a target of extensive studies in cosmology. General forms of shift-symmetric k-essence are known to suffer from formation of caustics in a planar-symmetric configuration, with the only exceptions of canonical and DBI-/cuscuton-type kinetic terms. With this in mind, we seek for multi-field caustic-free completions of a general class of shift-symmetric k-essence models in this paper. The field space in UV theories is naturally curved, and we introduce the scale of the curvature as the parameter that controls the mass of the heavy field(s) that would be integrated out in the process of EFT reduction. By numerical methods, we demonstrate that the introduction of a heavy field indeed resolves the caustic problem by invoking its motion near the would-be caustic formation. We further study the cosmological application of the model. By expanding the equations with respect to the curvature scale of the field space, we prove that the EFT reduction is successfully done by taking the limit of infinite curvature, both for the background and perturbation, with gravity included. The next leading-order computation is consistently conducted and shows that the EFT reduction breaks down in the limit of vanishing sound speed of the perturbation.
Effects of penicillamine, hypotaurine, and epinephrine on motility, hyperactivity, acrosome reaction of fresh ram sperm

Objective: To determine the effects of different concentrations of penicillamine, hypotaurine, and epinephrine (PHE) as well as incubation time on motility, hyperactivity and acrosome reaction (AR) of ram sperm in vitro. Methods: Freshly ejaculated spermatozoa from three ram were collected, pooled and subjected to swim up technique in modified sperm Tyrode's albumin lactate pyruvate medium supplemented with different concentrations of PHE (10, 20, 30, 40, 50, 75 and 100 mM/mL). Then best concentrations were compared and examined for motility, hyperactivity and AR. Results: A high concentrations of PHE (30, 40, 50, 75 and 100mM/mL) showed a significant increase in motility when compared to control immediately after dilution and exist for the first and second hour of incubation period. However, when longer incubation time were used, a significant (P<0.05) decrease in motility was achieved. Similar finding was observed in hyperactivity. Stained semen samples showed a maximum percentage of incomplete AR after 1 h incubation corresponding to 50 and 75 mM/mL of PHE; however, spermatozoa treated with 75 mM/mL had a higher tendency to undergo complete AR after further incubation up to 4 h. A dose dependent relationship was detected where the maximum value of total AR was shown in spermatozoa treated with 75 mM/mL PHE for 4 h. Conclusions: To obtain better motility, hyperactivity and AR, treatment of ram spermatozoa with 75 mM/mL PHE for 4 h before being used in insemination was considered the best concentration of PHE to be used in the process of in-vitro fertilization.
A non-Abelian parton state for the $ν=2+3/8$ fractional quantum Hall effect

<jats:p>Fascinating structures have arisen from the study of the fractional quantum Hall effect (FQHE) at the even denominator fraction of <jats:inline-formula><jats:alternatives><jats:tex-math>5/2</jats:tex-math><mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" display="inline"><mml:mrow><mml:mn>5</mml:mn><mml:mi>/</mml:mi><mml:mn>2</mml:mn></mml:mrow></mml:math></jats:alternatives></jats:inline-formula>. We consider the FQHE at another even denominator fraction, namely <jats:inline-formula><jats:alternatives><jats:tex-math>\nu=2+3/8</jats:tex-math><mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>ν</mml:mi><mml:mo>=</mml:mo><mml:mn>2</mml:mn><mml:mo>+</mml:mo><mml:mn>3</mml:mn><mml:mi>/</mml:mi><mml:mn>8</mml:mn></mml:mrow></mml:math></jats:alternatives></jats:inline-formula>, where a well-developed and quantized Hall plateau has been observed in experiments. We examine the non-Abelian state described by the ``<jats:inline-formula><jats:alternatives><jats:tex-math>\bar{3}\bar{2}^{2}1^{4}</jats:tex-math><mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" display="inline"><mml:mrow><mml:mover><mml:mn>3</mml:mn><mml:mo accent="true">‾</mml:mo></mml:mover><mml:msup><mml:mover><mml:mn>2</mml:mn><mml:mo accent="true">‾</mml:mo></mml:mover><mml:mn>2</mml:mn></mml:msup><mml:msup><mml:mn>1</mml:mn><mml:mn>4</mml:mn></mml:msup></mml:mrow></mml:math></jats:alternatives></jats:inline-formula>" parton wave function and numerically demonstrate it to be a feasible candidate for the ground state at <jats:inline-formula><jats:alternatives><jats:tex-math>\nu=2+3/8</jats:tex-math><mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>ν</mml:mi><mml:mo>=</mml:mo><mml:mn>2</mml:mn><mml:mo>+</mml:mo><mml:mn>3</mml:mn><mml:mi>/</mml:mi><mml:mn>8</mml:mn></mml:mrow></mml:math></jats:alternatives></jats:inline-formula>. We make predictions for experimentally measurable properties of the <jats:inline-formula><jats:alternatives><jats:tex-math>\bar{3}\bar{2}^{2}1^{4}</jats:tex-math><mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" display="inline"><mml:mrow><mml:mover><mml:mn>3</mml:mn><mml:mo accent="true">‾</mml:mo></mml:mover><mml:msup><mml:mover><mml:mn>2</mml:mn><mml:mo accent="true">‾</mml:mo></mml:mover><mml:mn>2</mml:mn></mml:msup><mml:msup><mml:mn>1</mml:mn><mml:mn>4</mml:mn></mml:msup></mml:mrow></mml:math></jats:alternatives></jats:inline-formula> state that can reveal its underlying topological structure. </jats:p>
Effect of cassava leaves preparation methods on sensory quality, retention of proximate components, selected vitamins and Cyanogenic Glycosides in Coastal region cassava varieties

A survey was conducted at Mkuranga and Rufiji districts in the Coastal Region of Tanzania. A total of 10 samples comprised of two from each of 5 varieties were subjected to three preparation methods (pounded cassava leaves as preparation method 1, boiled pounded leaves for 30 minutes as preparation method 2, and pounded leaves in hot water then boiled for 30 minutes as preparation method 3). These samples were analyzed for proximate components, β-carotene, vitamin C and cyanide content. The data was subjected to analysis of variance (ANOVA) to compare the effect of preparation methods, location and variety on the above mentioned components. Means were separated by DMRT and tested for significant by differences attributed to preparation methods and variety. The findings indicate that cassava leaves from Kiroba variety is mostly cultivated by farmers in the study area which was significantly attributed to its being tasty and high yield, following significance test by DMRT at p<0.05. Locational mean difference were subjected to t test (p< 0.05). Mpira variety was found to contain higher moisture content (82.40%) than other varieties (p<0.05). Kizimbani variety was found to contain higher protein (31.78% DM) than other varieties. Nyakamgile contained significantly higher ash content (10.48% DM) compared to other varieties. Crude fiber was significantly higher in Cheupe (25.13% DM) than other varieties all mean differences were significant at p<0.05 level. Crude fat was higher in Kizimbani variety (3.91% DM) while Mpira variety contained significantly higher carbohydrate (36.10% DM) content (p<0.05). The cyanide content on cassava leaves was found to be higher in Kiroba (185.51mgHCN/kg) DM and lower in Nyamkagile variety (54.17mgHCN/kg) DM, cyanide content for the preparation method 1 was 376.31mgHCN/kg DM while
Spatial Organization of Rho GTPase signaling by RhoGEF/RhoGAP proteins

Rho GTPases control cell morphogenesis and thus fundamental processes in all eukaryotes. They are regulated by 145 RhoGEF and RhoGAP multi-domain proteins in humans. How the Rho signaling system is organized to generate localized responses in cells and prevent their spreading is not understood. Here, we systematically characterized the substrate specificities, localization and interactome of the RhoGEFs/RhoGAPs and revealed their critical role in contextualizing and spatially delimiting Rho signaling. They localize to multiple compartments providing positional information, are extensively interconnected to jointly coordinate their signaling networks and are widely autoinhibited to remain sensitive to local activation. RhoGAPs exhibit lower substrate specificity than RhoGEFs and may contribute to preserving Rho activity gradients. Our approach led us to uncover a multi-RhoGEF complex downstream of G-protein-coupled receptors controlling a Cdc42/RhoA crosstalk. The spatial organization of Rho signaling thus differs from other small GTPases and expands the repertoire of mechanisms governing localized signaling activity.
Training Stronger Baselines for Learning to Optimize

Learning to optimize (L2O) has gained increasing attention since classical optimizers require laborious problem-specific design and hyperparameter tuning. However, there is a gap between the practical demand and the achievable performance of existing L2O models. Specifically, those learned optimizers are applicable to only a limited class of problems, and often exhibit instability. With many efforts devoted to designing more sophisticated L2O models, we argue for another orthogonal, under-explored theme: the training techniques for those L2O models. We show that even the simplest L2O model could have been trained much better. We first present a progressive training scheme to gradually increase the optimizer unroll length, to mitigate a well-known L2O dilemma of truncation bias (shorter unrolling) versus gradient explosion (longer unrolling). We further leverage off-policy imitation learning to guide the L2O learning, by taking reference to the behavior of analytical optimizers. Our improved training techniques are plugged into a variety of state-of-the-art L2O models, and immediately boost their performance, without making any change to their model structures. Especially, by our proposed techniques, an earliest and simplest L2O model can be trained to outperform the latest complicated L2O models on a number of tasks. Our results demonstrate a greater potential of L2O yet to be unleashed, and urge to rethink the recent progress. Our codes are publicly available at: this https URL.
Well-posedness and tamed Euler schemes for McKean-Vlasov equations driven by L\'evy noise

Abstract. We prove the well-posedness of solutions to McKean–Vlasov stochastic differential equations driven by Lévy noise under mild assumptions where, in particular, the Lévy measure is not required to be finite. The drift, diffusion and jump coefficients are allowed to be random, can grow super-linearly in the state variable, and all may depend on the marginal law of the solution process. We provide a propagation of chaos result under more relaxed conditions than those existing in the literature, and consistent with our well-posedness result. We propose a tamed Euler scheme for the associated interacting particle system and prove that the rate of its strong convergence is arbitrarily close to 1/2. As a by-product, we also obtain the corresponding results on well-posedness, propagation of chaos and strong convergence of the tamed Euler scheme for McKean–Vlasov stochastic delay differential equations (SDDE) and McKean–Vlasov stochastic differential equations with Markovian switching (SDEwMS), both driven by Lévy noise. Furthermore, our results on tamed Euler schemes are new even for ordinary SDEs driven by Lévy noise and with super-linearly growing coefficients.
Quantum Fluctuations in the Effective Relational GFT Cosmology

We analyze the size and evolution of quantum fluctuations of cosmologically relevant geometric observables, in the context of the effective relational cosmological dynamics of GFT models of quantum gravity. We consider the fluctuations of the matter clock observables, to test the validity of the relational evolution picture itself. Next, we compute quantum fluctuations of the universe volume and of other operators characterizing its evolution (number operator for the fundamental GFT quanta, effective Hamiltonian and scalar field momentum). In particular, we focus on the late (clock) time regime, where the dynamics is compatible with a flat FRW universe, and on the very early phase near the quantum bounce produced by the fundamental quantum gravity dynamics.
A combined full-reference image quality assessment method based on convolutional activation maps

The goal of full-reference image quality assessment (FR-IQA) is to predict the perceptual quality of an image as perceived by human observers using its pristine (distortion free) reference counterpart. In this study, we explore a novel, combined approach which predicts the perceptual quality of a distorted image by compiling a feature vector from convolutional activation maps. More specifically, a reference-distorted image pair is run through a pretrained convolutional neural network and the activation maps are compared with a traditional image similarity metric. Subsequently, the resulting feature vector is mapped onto perceptual quality scores with the help of a trained support vector regressor. A detailed parameter study is also presented in which the design choices of the proposed method is explained. Furthermore, we study the relationship between the amount of training images and the prediction performance. Specifically, it is demonstrated that the proposed method can be trained with a small amount of data to reach high prediction performance. Our best proposal—called ActMapFeat—is compared to the state-of-the-art on six publicly available benchmark IQA databases, such as KADID-10k, TID2013, TID2008, MDID, CSIQ, and VCL-FER. Specifically, our method is able to significantly outperform the state-of-the-art on these benchmark databases.
Survey on Causal-based Machine Learning Fairness Notions

Addressing the problem of fairness is crucial to safely use machine learning algorithms to support decisions with a critical impact on people's lives such as job hiring, child maltreatment, disease diagnosis, loan granting, etc. Several notions of fairness have been defined and examined in the past decade, such as, statistical parity and equalized odds. The most recent fairness notions, however, are causal-based and reflect the now widely accepted idea that using causality is necessary to appropriately address the problem of fairness. This paper examines an exhaustive list of causal-based fairness notions, in particular their applicability in real-world scenarios. As the majority of causal-based fairness notions are defined in terms of non-observable quantities (e.g. interventions and counterfactuals), their applicability depends heavily on the identifiability of those quantities from observational data. In this paper, we compile the most relevant identifiability criteria for the problem of fairness from the extensive literature on identifiability theory. These criteria are then used to decide about the applicability of causal-based fairness notions in concrete discrimination scenarios.
Fourier Neural Operator for Parametric Partial Differential Equations

The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces. Recently, this has been generalized to neural operators that learn mappings between function spaces. For partial differential equations (PDEs), neural operators directly learn the mapping from any functional parametric dependence to the solution. Thus, they learn an entire family of PDEs, in contrast to classical methods which solve one instance of the equation. In this work, we formulate a new neural operator by parameterizing the integral kernel directly in Fourier space, allowing for an expressive and efficient architecture. We perform experiments on Burgers' equation, Darcy flow, and the Navier-Stokes equation (including the turbulent regime). Our Fourier neural operator shows state-of-the-art performance compared to existing neural network methodologies and it is up to three orders of magnitude faster compared to traditional PDE solvers.
Multiple Pedestrians and Vehicles Tracking in Aerial Imagery: A Comprehensive Study

In this paper, we address various challenges in multi-pedestrian and vehicle tracking in high-resolution aerial imagery by intensive evaluation of a number of traditional and Deep Learning based Single- and Multi-Object Tracking methods. We also describe our proposed Deep Learning based Multi-Object Tracking method AerialMPTNet that fuses appearance, temporal, and graphical information using a Siamese Neural Network, a Long Short-Term Memory, and a Graph Convolutional Neural Network module for a more accurate and stable tracking. Moreover, we investigate the influence of the Squeeze-and-Excitation layers and Online Hard Example Mining on the performance of AerialMPTNet. To the best of our knowledge, we are the first in using these two for a regression-based Multi-Object Tracking. Additionally, we studied and compared the L1 and Huber loss functions. In our experiments, we extensively evaluate AerialMPTNet on three aerial Multi-Object Tracking datasets, namely AerialMPT and KIT AIS pedestrian and vehicle datasets. Qualitative and quantitative results show that AerialMPTNet outperforms all previous methods for the pedestrian datasets and achieves competitive results for the vehicle dataset. In addition, Long Short-Term Memory and Graph Convolutional Neural Network modules enhance the tracking performance. Moreover, using Squeeze-and-Excitation and Online Hard Example Mining significantly helps for some cases while degrades the results for other cases. In addition, according to the results, L1 yields better results with respect to Huber loss for most of the scenarios. The presented results provide a deep insight into challenges and opportunities of the aerial Multi-Object Tracking domain, paving the way for future research.
The anisotropy in the optical constants of quartz crystals for soft X-rays

The refractive index of a y-cut SiO2 crystal surface is reconstructed from polarization-dependent soft X-ray reflectometry measurements in the energy range from 45 to 620 eV. The reconstructed anisotropy in the optical constants is also confirmed by ab initio Bethe–Salpeter equation calculations of the O K edge.