Miracle-dz/UniFAR-FTUs huggingface dataset:
A critical analysis of metrics used for measuring progress in artificial intelligence

Comparing model performances on benchmark datasets is an integral part of measuring and driving progress in artificial intelligence. A model's performance on a benchmark dataset is commonly assessed based on a single or a small set of performance metrics. While this enables quick comparisons, it may also entail the risk of inadequately reflecting model performance if the metric does not sufficiently cover all performance characteristics. Currently, it is unknown to what extent this might impact current benchmarking efforts. To address this question, we analysed the current landscape of performance metrics based on data covering 3867 machine learning model performance results from the web-based open platform 'Papers with Code'. Our results suggest that the large majority of metrics currently used to evaluate classification AI benchmark tasks have properties that may result in an inadequate reflection of a classifiers' performance, especially when used with imbalanced datasets. While alternative metrics that address problematic properties have been proposed, they are currently rarely applied as performance metrics in benchmarking tasks. Finally, we noticed that the reporting of metrics was partly inconsistent and partly unspecific, which may lead to ambiguities when comparing model performances.
StyleFlow: Attribute-conditioned Exploration of StyleGAN-Generated Images using Conditional Continuous Normalizing Flows

High-quality, diverse, and photorealistic images can now be generated by unconditional GANs (e.g., StyleGAN). However, limited options exist to control the generation process using (semantic) attributes while still preserving the quality of the output. Further, due to the entangled nature of the GAN latent space, performing edits along one attribute can easily result in unwanted changes along other attributes. In this article, in the context of conditional exploration of entangled latent spaces, we investigate the two sub-problems of attribute-conditioned sampling and attribute-controlled editing. We present StyleFlow as a simple, effective, and robust solution to both the sub-problems by formulating conditional exploration as an instance of conditional continuous normalizing flows in the GAN latent space conditioned by attribute features. We evaluate our method using the face and the car latent space of StyleGAN, and demonstrate fine-grained disentangled edits along various attributes on both real photographs and StyleGAN generated images. For example, for faces, we vary camera pose, illumination variation, expression, facial hair, gender, and age. Finally, via extensive qualitative and quantitative comparisons, we demonstrate the superiority of StyleFlow over prior and several concurrent works. Project Page and Video: https://rameenabdal.github. io/StyleFlow.
Large-time asymptotics in deep learning

It is by now well-known that practical deep supervised learning may roughly be cast as an optimal control problem for a specific discrete-time, nonlinear dynamical system called an artificial neural network. In this work, we consider the continuous-time formulation of the deep supervised learning problem, and study the latter's behavior when the final time horizon increases, a fact that can be interpreted as increasing the number of layers in the neural network setting. When considering the classical regularized empirical risk minimization problem , we show that, in long time, the optimal states converge to zero training error, namely approach the zero training error regime, whilst the optimal control parameters approach, on an appropriate scale, minimal norm parameters with corresponding states precisely in the zero training error regime. This result provides an alternative theoretical underpinning to the notion that neural networks learn best in the overparametrized regime, when seen from the large layer perspective. We also propose a learning problem consisting of minimizing a cost with a state tracking term, and establish the well-known turnpike property, which indicates that the solutions of the learning problem in long time intervals consist of three pieces, the first and the last of which being transient short-time arcs, and the middle piece being a long-time arc staying exponentially close to the optimal solution of an associated static learning problem. This property in fact stipulates a quantitative estimate for the number of layers required to reach the zero training error regime. Both of the aforementioned asymptotic regimes are addressed in the context of continuous-time and continuous space-time neural networks, the latter taking the form of nonlinear, integro-differential equations, hence covering residual neural networks with both fixed and possibly variable depths.
Topological and symmetry-enriched random quantum critical points

We study how symmetry can enrich strong-randomness quantum critical points and phases, and lead to robust topological edge modes coexisting with critical bulk fluctuations. These are the disordered analogues of gapless topological phases. Using real-space and density matrix renormalization group approaches, we analyze the boundary and bulk critical behavior of such symmetry-enriched random quantum spin chains. We uncover a new class of symmetry-enriched infinite randomness fixed points: while local bulk properties are indistinguishable from conventional random singlet phases, nonlocal observables and boundary critical behavior are controlled by a different renormalization group fixed point.
A Practical Privacy-preserving Method in Federated Deep Learning.

Although federated learning improves privacy of training data by exchanging model updates rather than raw data, many research results show that sharing the model updates may still involve risks. To alleviate this problem, many privacy-preserving techniques have been introduced to federated learning. However, considering deep learning models in federated learning, the resulting schemes either cannot implement non-linear activation functions well, or cannot remain the same model accuracy as the original training, or suffer from unaffordable costs. In this paper, we customize a \emph{practical privacy-preserving method for federated deep learning}, which is versatile and applicable to most state-of-the-art models, such as ResNet and DenseNet. In particular, this method can support non-linear activation functions well on the encrypted domain, hence supporting semi-trusted clients to efficiently train deep neural network locally over encrypted model iterates (i.e., protecting the privacy of the model for server-side). Meanwhile, it can be combined with the secret sharing technique to further ensure the semi-trusted server cannot obtain local gradients of each client (i.e., protect the privacy of training data for client-side). Detailed security analysis and extensive experiments demonstrate that the proposed method can achieve privacy-preservation without sacrificing model accuracy and introducing too much extra costs.
Symmetry-breaking bifurcation for a free boundary problem modeling small arterial plaque

Atherosclerosis, hardening of the arteries, originates from small plaque in the arteries; it is a major cause of disability and premature death in the United States and worldwide. In this paper, we study the bifurcation for a highly nonlinear and highly coupled PDE model describing the growth of arterial plaque. The model involves LDL and HDL cholesterols, macrophage cells as well as foam cells, with the interface separating the plaque and blood flow region being a free boundary. We establish a finite branch of symmetry-breaking stationary solutions which bifurcate from the radially symmetric solutions. Since plaque in reality is unlikely to be strictly radially symmetric, our result would be useful to explain the asymmetric shapes of plaque.
Continuous-in-Depth Neural Networks

Recent work has attempted to interpret residual networks (ResNets) as one step of a forward Euler discretization of an ordinary differential equation, focusing mainly on syntactic algebraic similarities between the two systems. Discrete dynamical integrators of continuous dynamical systems, however, have a much richer structure. We first show that ResNets fail to be meaningful dynamical integrators in this richer sense. We then demonstrate that neural network models can learn to represent continuous dynamical systems, with this richer structure and properties, by embedding them into higher-order numerical integration schemes, such as the Runge Kutta schemes. Based on these insights, we introduce ContinuousNet as a continuous-in-depth generalization of ResNet architectures. ContinuousNets exhibit an invariance to the particular computational graph manifestation. That is, the continuous-in-depth model can be evaluated with different discrete time step sizes, which changes the number of layers, and different numerical integration schemes, which changes the graph connectivity. We show that this can be used to develop an incremental-in-depth training scheme that improves model quality, while significantly decreasing training time. We also show that, once trained, the number of units in the computational graph can even be decreased, for faster inference with little-to-no accuracy drop.
Morphology, Phylogeny, Evolution, and Dispersal of Pelecinid Wasps (Hymenoptera: Pelecinidae) Over 165 Million Years

ABSTRACT Large female pelecinid wasps, elegant and beautiful with uniquely elongated metasoma, are found only in North, Central, and South America. These distinctive wasps are “living fossils” in a relict family of Pelecinidae. Recent discoveries of well preserved pelecinid fossils from the Middle Jurassic (165 Mya) to the Early Cretaceous (125 Mya) of northeastern China, of which Megapelecinus changi gen. et sp. nov. and Megapelecinus nashi, sp. nov. are described herein, provide strong evidence for morphological changes and evolution of pelecinids, such as body size, antennae and wing venation, over the past ≈165 million years. A phylogenetic analysis using 22 morphological characters, 12 extinct pelecinid taxa and one extant pelecinid taxon at the generic level suggests that Megapelecinus gen. nov. is the most basal pelecinid, whereas Cathaypelecinus Shih, Liu et Ren 2009, Archaeopelecinus Shih, Liu et Ren 2009, and Iscopinus Kozlov, 1974 forming a basal clade. Natural selection is demonstrated in pelecinids by the preference of larger female body size offering potentially better ovipositing capability and more efficient forewing venation with an X pattern providing potentially stronger wing structure and better flight performance for large-sized pelecinids. Furthermore, temporal and spatial analyses indicate that the most parsimonious hypothesis is that pelecinid might have originated from Northeastern China, spread to Central and Eastern Asia, and then dispersed to Americas.
Contraceptive practices and awareness about emergency contraception among women attending a tertiary care hospital of Delhi, India

Background: Emergency contraceptive pills (ECs) are a safe female-controlled method of post-coital contraception; however, the knowledge about them is low among general population.Methods: This cross-sectional study was done among the adult females, aged 18 years and above, who visited the various out-patient departments (OPD) of a tertiary care hospital in July 2017. A total of 640 adult females, who visited the OPDs of medicine, surgery, orthopedics and gynecology were included in this study. Data were entered and analyzed using SPSS ver 16. Chi-square test was used to test association. A p-value of less than 0.05 was considered as significant association.Results: Of the 640 participants, mostly were Hindu, aged between 20-29 years, married and had more than one child. Only, 52% females were aware of the term ‘contraception’ and 17.7% about ‘emergency contraception’. The only method of ECs they were aware of was i-pill. Commonest source of their knowledge was friends, relatives and spouse. Only 70 women knew that it can be used after unprotected intercourse and 25 knew that it can be used after failure of barrier method. Just 41.2% women used any kind of contraceptive and 5.3% women have ever used an EC. Around 28% women reported not using contraception due to lack of knowledge.Conclusions: Despite of their availability since many years, awareness about emergency contraception is low. More efforts are required to generate awareness about regular use of effective contraception and emergency contraception if required.
Discrimination of Ohmic thermal baths by quantum dephasing probes

We address the discrimination of structured baths at different temperatures by dephasing quantum probes. We derive the exact reduced dynamics and evaluate the minimum error probability achievable by three different kinds of quantum probes, namely a qubit, a qutrit and a quantum register made of two qubits. Our results indicate that dephasing quantum probes are useful in discriminating low values of temperature, and that lower probabilities of error are achieved for intermediate values of the interaction time. A qutrit probe outperforms a qubit one in the discrimination task, whereas a register made of two qubits does not offer any advantage compared to two single qubits used sequentially.
A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration

In this paper, we develop novel perturbation bounds for the high-order orthogonal iteration (HOOI) [DLDMV00b]. Under mild regularity conditions, we establish blockwise tensor perturbation bounds for HOOI with guarantees for both tensor reconstruction in Hilbert-Schmidt norm $\|\widehat{\bcT} - \bcT \|_{\tHS}$ and mode-$k$ singular subspace estimation in Schatten-$q$ norm $\| \sin \Theta (\widehat{\U}_k, \U_k) \|_q$ for any $q \geq 1$. We show the upper bounds of mode-$k$ singular subspace estimation are unilateral and converge linearly to a quantity characterized by blockwise errors of the perturbation and signal strength. For the tensor reconstruction error bound, we express the bound through a simple quantity $\xi$, which depends only on perturbation and the multilinear rank of the underlying signal. Rate matching deterministic lower bound for tensor reconstruction, which demonstrates the optimality of HOOI, is also provided. Furthermore, we prove that one-step HOOI (i.e., HOOI with only a single iteration) is also optimal in terms of tensor reconstruction and can be used to lower the computational cost. The perturbation results are also extended to the case that only partial modes of $\bcT$ have low-rank structure. We support our theoretical results by extensive numerical studies. Finally, we apply the novel perturbation bounds of HOOI on two applications, tensor denoising and tensor co-clustering, from machine learning and statistics, which demonstrates the superiority of the new perturbation results.
Whistler Waves and Electron Properties in the Inner Heliosphere: Helios Observations

We present the analysis of narrowband whistler wave signatures observed in the inner heliosphere (0.3–1 au). These signatures are bumps in the spectral density in the 10–200 Hz frequency range of the AC magnetic field as measured by the search coil magnetometer on board the Helios 1 spacecraft. We show that the majority of whistler signatures are observed in the slow solar wind (<500 km s−1) and their occurrence increases with the radial distance (R), from ∼3% at 0.3 au to ∼10% at 0.9 au. In the fast solar wind (>600 km s−1), whistler activity is significantly lower; whistler signatures start to appear for R > 0.6 au and their number increases from ∼0.03% at 0.65 au to ∼1% at 0.9 au. We have studied the variation of the electron core and halo anisotropy (Te⊥/Te∥), as well as the electron normalized heat flux as a function of R and of the solar wind speed. We find that, in the slow wind electron core and halo anisotropy is higher than in fast one, and also that these anisotropies increase radially in both types of winds, which is in line with the occurrence of whistler signatures. We hypothesize the existence of a feedback mechanism to explain the observed radial variations of the occurrence of whistlers in relation with the halo anisotropy.
Probing the Milky Way’s Dark Matter Halo for the 3.5 keV Line

We present a comprehensive search for the 3.5 keV line using ∼51 Ms of archival Chandra observations peering through the Milky Way’s dark matter halo from across the entirety of the sky, gathered via the Chandra Source Catalog Release 2.0. We consider the data’s radial distribution, organizing observations into four data subsets based on angular distance from the Galactic center. All data are modeled using both background-subtracted and background-modeled approaches to account for the particle instrument background, demonstrating the statistical limitations of the currently available ∼1 Ms of particle background data. A nondetection is reported in the total data set, allowing us to set an upper limit on 3.5 keV line flux and constrain the sterile neutrino dark matter mixing angle. The upper limit on sin2(2θ) is (though systematic uncertainty may increase this by a factor of ∼2), corresponding to an upper limit on 3.5 keV line flux of photons s−1 cm−2. These limits show consistency with recent constraints and several prior detections. Nondetections are reported in all radial data subsets, allowing us to constrain the spatial profile of 3.5 keV line intensity, which does not conclusively differ from Navarro–Frenk–White predictions. Thus, while offering heavy constraints, we do not entirely rule out the sterile neutrino dark matter scenario or the more general decaying dark matter hypothesis for the 3.5 keV line. We have also used the nondetection of any unidentified emission lines across our continuum to further constrain the sterile neutrino parameter space.
Towards a better understanding of language model information retrieval

Language models form a class of successful probabilistic models in information retrieval. However, knowledge of why some methods perform better than others in a particular situation remains limited. In this study we analyze what language model factors influence information retrieval performance. Starting from popular smoothing methods we review what data features have been used. Document length and a measure of document word distribution turned out to be the important factors, in addition to a distinction in estimating the probability of seen and unseen words. We propose a class of parameter-free smoothing methods, of which multiple specific instances are possible. Instead of parameter tuning however, an analysis of data features should be used to decide upon a specific method. Finally, we discuss some initial experiments.
Yield responses of cauliflower (Brassica oleracea L. var. Botrytis) to different water and nitrogen levels in a Mediterranean coastal area

Abstract Global warming along with the increasing population and fresh water shortages necessitates a specific fertilization programme under water-scarce conditions. This study was conducted to investigate the effects of different irrigation and nitrogen levels on yield, growth components and water use characteristics of cauliflower (Brassica oleracea L. var. Botrytis cv. Tetris-F1) cultivated in a field for three consecutive years from 2005 to 2007 in the Eastern Mediterranean region of Turkey. Four irrigation (Kcp) levels with a drip irrigation system based on adjustment coefficients (0, 0.75, 1.0 and 1.25) of pan evaporation were used. Nitrogen (N) treatments were consisted of four different nitrogen rates (0, 75, 150 and 225 kg N ha–1). The following yield and quality parameters were determined: curd weight, curd diameters, number of leaves per crop, above ground biomass (AGB) and curd/AGB ratio. Fertilizer use efficiency (FUE) and leaf mineral contents were also determined to clarify the productivity of N treatments. According to the results; the amount of irrigation water and/or total received water affects the plant water consumption, consequently, crop yield in a field grown cauliflower. The highest yield was obtained in Kcp1.0 irrigation level which represents full irrigation treatment. The excess water applications had negative effect on yield and AGB of cauliflower. Highest yield was obtained at 225 kg N ha–1. The water use efficiency and irrigation water use efficiency values increased with decreasing irrigation rate. However, lower Kcp coefficients resulted in lower total yield. The FUE in irrigation treatments showed linear increases from non irrigation to full irrigation plots. However, excessive irrigation caused a decrease in FUE. It can be recommended that the Kcp1.0 crop-pan coefficient with 225 kg ha–1 nitrogen application can be used to achieve the highest yield for field grown cauliflower in the Eastern Mediterranean coastal region of Turkey.
Structured Convolutions for Efficient Neural Network Design

In this work, we tackle model efficiency by exploiting redundancy in the \textit{implicit structure} of the building blocks of convolutional neural networks. We start our analysis by introducing a general definition of Composite Kernel structures that enable the execution of convolution operations in the form of efficient, scaled, sum-pooling components. As its special case, we propose \textit{Structured Convolutions} and show that these allow decomposition of the convolution operation into a sum-pooling operation followed by a convolution with significantly lower complexity and fewer weights. We show how this decomposition can be applied to 2D and 3D kernels as well as the fully-connected layers. Furthermore, we present a Structural Regularization loss that promotes neural network layers to leverage on this desired structure in a way that, after training, they can be decomposed with negligible performance loss. By applying our method to a wide range of CNN architectures, we demonstrate "structured" versions of the ResNets that are up to 2$\times$ smaller and a new Structured-MobileNetV2 that is more efficient while staying within an accuracy loss of 1% on ImageNet and CIFAR-10 datasets. We also show similar structured versions of EfficientNet on ImageNet and HRNet architecture for semantic segmentation on the Cityscapes dataset. Our method performs equally well or superior in terms of the complexity reduction in comparison to the existing tensor decomposition and channel pruning methods.