Miracle-dz/UniFAR-FTUs huggingface dataset:
Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis

Plant diseases serve as one of main threats to food security and crop production. It is thus valuable to exploit recent advances of artificial intelligence to assist plant disease diagnosis. One popular approach is to transform this problem as a leaf image classification task, which can be then addressed by the powerful convolutional neural networks (CNNs). However, the performance of CNN-based classification approach depends on a large amount of high-quality manually labeled training data, which are inevitably introduced noise on labels in practice, leading to model overfitting and performance degradation. To overcome this problem, we propose a novel framework that incorporates rectified meta-learning module into common CNN paradigm to train a noise-robust deep network without using extra supervision information. The proposed method enjoys the following merits: i) A rectified meta-learning is designed to pay more attention to unbiased samples, leading to accelerated convergence and improved classification accuracy. ii) Our method is free on assumption of label noise distribution, which works well on various kinds of noise. iii) Our method serves as a plug-and-play module, which can be embedded into any deep models optimized by gradient descent based method. Extensive experiments are conducted to demonstrate the superior performance of our algorithm over the state-of-the-arts.
Dynamics of DiskMass Survey galaxies in refracted gravity

We aim to verify whether refracted gravity (RG) is capable of describing the dynamics of disk galaxies without resorting to the presence of dark matter. RG is a classical theory of gravity in which the standard Poisson equation is modified with the introduction of the gravitational permittivity, which is a universal monotonic function of the local mass density. We used the rotation curves and the radial profiles of the stellar velocity dispersion perpendicular to the galactic disks of 30 disk galaxies from the DiskMass Survey (DMS) to determine the gravitational permittivity. RG describes the rotation curves and the vertical velocity dispersions by requiring galaxy mass-to-light ratios that are in agreement with stellar population synthesis models, and disk thicknesses that are in agreement with observations, once observational biases are taken into account. Our results rely on setting the three free parameters of the gravitational permittivity for each individual galaxy. However, we show that the differences of these parameters from galaxy to galaxy can, in principle, be ascribed to statistical fluctuations. We adopted an approximate procedure to estimate a single set of parameters that may properly describe the kinematics of the entire sample and suggest that the gravitational permittivity is indeed a universal function. Finally, we showed that the RG models of the individual rotation curves can only partly describe the radial acceleration relation (RAR) between the observed centripetal acceleration derived from the rotation curve and the Newtonian gravitational acceleration originating from the baryonic mass distribution. Evidently, the RG models underestimate the observed accelerations by 0.1 to 0.3 dex at low Newtonian accelerations. An additional problem that ought to be considered is the strong correlation, at much more than 5σ, between the residuals of the RAR models and three radially-dependent properties of the galaxies, whereas the DMS data show a considerably less significant correlation, at more than 4σ, for only two of these quantities. These correlations might be the source of the non-null intrinsic scatter of the RG models: this non-null scatter is at odds with the observed intrinsic scatter of other galaxy samples different from DMS, which is consistent with zero. Further investigations are required to assess whether these discrepancies in the RAR originate from the DMS sample, which might not be ideal for deriving the RAR, or whether they are genuine failures of RG.
UvA-DARE (Digital Academic Repository) Latent Embedding Feedback and Discriminative Features for Zero-Shot Classification Latent Embedding Feedback and Discriminative Features for Zero-Shot Classiﬁcation

Zero-shot learning strives to classify unseen categories for which no data is available during training. In the generalized variant, the test samples can further belong to seen or unseen categories. The state-of-the-art relies on Generative Adversarial Networks that synthesize unseen class features by leveraging class-speciﬁc semantic embeddings. During training, they generate semantically consistent features, but discard this constraint during feature synthesis and classiﬁcation. We propose to enforce semantic consistency at all stages of (generalized) zero-shot learning: training, feature synthesis and classiﬁcation. We ﬁrst introduce a feedback loop, from a semantic embedding decoder, that iteratively reﬁnes the generated features during both the training and feature synthesis stages. The synthesized features together with their corresponding latent embeddings from the decoder are then transformed into discriminative features and utilized during classiﬁcation to reduce ambiguities among categories. Experiments on (generalized) zero-shot object and action classiﬁcation reveal the beneﬁt of semantic consistency and itera-tive feedback, outperforming existing methods on six zero-shot learning benchmarks. Source code at https://github.com/akshitac8/tfvaegan .
Exceptional Points in Parity-Time-Symmetric Subwavelength Metamaterials

When sources of energy gain and loss are introduced to a wave-scattering system, the underlying mathematical formulation will be non-Hermitian. This paves the way for the existence of exceptional points, where eigenmodes are linearly dependent. The primary goal of this work is to study the existence of exceptional points in high-contrast subwavelength metamaterials. We begin by studying a parity-time-symmetric pair of subwavelength resonators and prove that this system supports asymptotic exceptional points. We then investigate further properties of parity-time-symmetric subwavelength metamaterials. First, we study cavities of many small resonators and use homogenization theory to show that non-Hermitian behaviour can be replicated at the macroscale. Thereafter, we model a metascreen of subwavelength resonators which we prove exhibits asymptotic unidirectional reflectionless transmission at certain frequencies, and demonstrate extraordinary transmission close to these frequencies.
Gravitational waves from first-order cosmological phase transitions: lifetime of the sound wave source

We survey systematically the general parametrisations of particle-physics models for a first-order phase transition in the early universe, including models with polynomial potentials both with and without barriers at zero temperature, and Coleman-Weinberg-like models with potentials that are classically scale-invariant. We distinguish three possibilities for the transition—detonations, deflagrations and hybrids—and consider sound waves and turbulent mechanisms for generating gravitational waves during the transitions in these models, checking in each case the requirement for successful percolation. We argue that in models without a zero-temperature barrier and in scale-invariant models the period during which sound waves generate gravitational waves lasts only for a fraction of a Hubble time after a generic first-order cosmological phase transition, whereas it may last longer in some models with a zero-temperature barrier that feature severe supercooling. We illustrate the implications of these results for future gravitational-wave experiments.
Directions for Explainable Knowledge-Enabled Systems

Interest in the field of Explainable Artificial Intelligence has been growing for decades and has accelerated recently. As Artificial Intelligence models have become more complex, and often more opaque, with the incorporation of complex machine learning techniques, explainability has become more critical. Recently, researchers have been investigating and tackling explainability with a user-centric focus, looking for explanations to consider trustworthiness, comprehensibility, explicit provenance, and context-awareness. In this chapter, we leverage our survey of explanation literature in Artificial Intelligence and closely related fields and use these past efforts to generate a set of explanation types that we feel reflect the expanded needs of explanation for today's artificial intelligence applications. We define each type and provide an example question that would motivate the need for this style of explanation. We believe this set of explanation types will help future system designers in their generation and prioritization of requirements and further help generate explanations that are better aligned to users' and situational needs.
Deep Attention Fusion Feature for Speech Separation with End-to-End Post-filter Method

In this paper, we propose an end-to-end post-filter method with deep attention fusion features for monaural speaker-independent speech separation. At first, a time-frequency domain speech separation method is applied as the pre-separation stage. The aim of pre-separation stage is to separate the mixture preliminarily. Although this stage can separate the mixture, it still contains the residual interference. In order to enhance the pre-separated speech and improve the separation performance further, the end-to-end post-filter (E2EPF) with deep attention fusion features is proposed. The E2EPF can make full use of the prior knowledge of the pre-separated speech, which contributes to speech separation. It is a fully convolutional speech separation network and uses the waveform as the input features. Firstly, the 1-D convolutional layer is utilized to extract the deep representation features for the mixture and pre-separated signals in the time domain. Secondly, to pay more attention to the outputs of the pre-separation stage, an attention module is applied to acquire deep attention fusion features, which are extracted by computing the similarity between the mixture and the pre-separated speech. These deep attention fusion features are conducive to reduce the interference and enhance the pre-separated speech. Finally, these features are sent to the post-filter to estimate each target signals. Experimental results on the WSJ0-2mix dataset show that the proposed method outperforms the state-of-the-art speech separation method. Compared with the pre-separation method, our proposed method can acquire 64.1%, 60.2%, 25.6% and 7.5% relative improvements in scale-invariant source-to-noise ratio (SI-SNR), the signal-to-distortion ratio (SDR), the perceptual evaluation of speech quality (PESQ) and the short-time objective intelligibility (STOI) measures, respectively.
Efficient two-sided markets with limited information

A celebrated impossibility result by Myerson and Satterthwaite (1983) shows that any truthful mechanism for two-sided markets that maximizes social welfare must run a deficit, resulting in a necessity to relax welfare efficiency and the use of approximation mechanisms. Such mechanisms in general make extensive use of the Bayesian priors. In this work, we investigate a question of increasing theoretical and practical importance: how much prior information is required to design mechanisms with near-optimal approximations? Our first contribution is a more general impossibility result stating that no meaningful approximation is possible without any prior information, expanding the famous impossibility result of Myerson and Satterthwaite. Our second contribution is that one single sample (one number per item), arguably a minimum-possible amount of prior information, from each seller distribution is sufficient for a large class of two-sided markets. We prove matching upper and lower bounds on the best approximation that can be obtained with one single sample for subadditive buyers and additive sellers, regardless of computational considerations. Our third contribution is the design of computationally efficient blackbox reductions that turn any one-sided mechanism into a two-sided mechanism with a small loss in the approximation, while using only one single sample from each seller. On the way, our blackbox-type mechanisms deliver several interesting positive results in their own right, often beating even the state of the art that uses full prior information.
Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach

The recent enthusiasm for artificial intelligence (AI) is due principally to advances in deep learning. Deep learning methods are remarkably accurate, but also opaque, which limits their potential use in safety-critical applications. To achieve trust and accountability, designers and operators of machine learning algorithms must be able to explain the inner workings, the results and the causes of failures of algorithms to users, regulators, and citizens. The originality of this paper is to combine technical, legal and economic aspects of explainability to develop a framework for defining the "right" level of explain-ability in a given context. We propose three logical steps: First, define the main contextual factors, such as who the audience of the explanation is, the operational context, the level of harm that the system could cause, and the legal/regulatory framework. This step will help characterize the operational and legal needs for explanation, and the corresponding social benefits. Second, examine the technical tools available, including post hoc approaches (input perturbation, saliency maps.) and hybrid AI approaches. Third, as function of the first two steps, choose the right levels of global and local explanation outputs, taking into the account the costs involved. We identify seven kinds of costs and emphasize that explanations are socially useful only when total social benefits exceed costs.
Neighborhood-based Pooling for Population-level Label Distribution Learning

Supervised machine learning often requires human-annotated data. While annotator disagreement is typically interpreted as evidence of noise, population-level label distribution learning (PLDL) treats the collection of annotations for each data item as a sample of the opinions of a population of human annotators, among whom disagreement may be proper and expected, even with no noise present. From this perspective, a typical training set may contain a large number of very small-sized samples, one for each data item, none of which, by itself, is large enough to be considered representative of the underlying population's beliefs about that item. We propose an algorithmic framework and new statistical tests for PLDL that account for sampling size. We apply them to previously proposed methods for sharing labels across similar data items. We also propose new approaches for label sharing, which we call neighborhood-based pooling.
A Novel AI-enabled Framework to Diagnose Coronavirus COVID-19 using Smartphone Embedded Sensors: Design Study

Coronaviruses are a famous family of viruses that cause illness in both humans and animals. The new type of coronavirus COVID-19 was firstly discovered in Wuhan, China. However, recently, the virus has widely spread in most of the world and causing a pandemic according to the World Health Organization (WHO). Further, nowadays, all the world countries are striving to control the COVID-19. There are many mechanisms to detect coronavirus including clinical analysis of chest CT scan images and blood test results. The confirmed COVID-19 patient manifests as fever, tiredness, and dry cough. Particularly, several techniques can be used to detect the initial results of the virus such as medical detection Kits. However, such devices are incurring huge cost, taking time to install them and use. Therefore, in this paper, a new framework is proposed to detect COVID-19 using built-in smartphone sensors. The proposal provides a low-cost solution, since most of radiologists have already held smartphones for different daily-purposes. Not only that but also ordinary people can use the framework on their smartphones for the virus detection purposes. Today’s smartphones are powerful with existing computation-rich processors, memory space, and large number of sensors including cameras, microphone, temperature sensor, inertial sensors, proximity, colour-sensor, humidity-sensor, and wireless chipsets/sensors. The designed Artificial Intelligence (AI) enabled framework reads the smartphone sensors’ signal measurements to predict the grade of severity of the pneumonia as well as predicting the result of the disease.
Nonparametric Deconvolution Models

We describe nonparametric deconvolution models (NDMs), a family of Bayesian nonparametric models for collections of data in which each observation is the average over the features from heterogeneous particles. For example, these types of data are found in elections, where we observe precinct-level vote tallies (observations) of individual citizens' votes (particles) across each of the candidates or ballot measures (features), where each voter is part of a specific voter cohort or demographic (factor). Like the hierarchical Dirichlet process, NDMs rely on two tiers of Dirichlet processes to explain the data with an unknown number of latent factors; each observation is modeled as a weighted average of these latent factors. Unlike existing models, NDMs recover how factor distributions vary locally for each observation. This uniquely allows NDMs both to deconvolve each observation into its constituent factors, and also to describe how the factor distributions specific to each observation vary across observations and deviate from the corresponding global factors. We present variational inference techniques for this family of models and study its performance on simulated data and voting data from California. We show that including local factors improves estimates of global factors and provides a novel scaffold for exploring data.
Marangoni instability in a viscoelastic binary film with cross-diffusive effect

Abstract Viscoelastic liquids are usually blends of a polymeric solute and a Newtonian solvent. In the presence of a temperature gradient, stratification of these solutes can take place via the Soret effect. Here, we investigate the classical Marangoni instability problem for a thin viscoelastic film considering this binary aspect of the liquid. The film, bounded above by a deformable free surface, is subjected to heating from below by a solid substrate. Linear stability analysis performed numerically for perturbations of finite wavelength (short-wave perturbations) reveals that both monotonic and oscillatory instabilities can emerge in this system. In the presence of Soret diffusion, the interaction between thermocapillary and solutocapillary forces is found to give rise to two different oscillatory instabilities, of which one mode was overlooked previously, even for the Newtonian binary mixtures. As a principal result of this work, we provide a complete picture of the susceptibility to different instability modes based on model parameter values. Finally, an approximate model is developed under the framework of long-wave analysis, which can qualitatively depict the stability behaviour of the system without numerically solving the problem.
Dalitz analysis of 
D0→K−π+η
 decays at Belle

We present the results of the first Dalitz plot analysis of the decay $D^{0}\to K^{-}\pi^{+}\eta$. The analysis is performed on a data set corresponding to an integrated luminosity of 953 $\rm{fb}^{-1}$ collected by the Belle detector at the asymmetric-energy $e^{+}e^{-}$ KEKB collider. The Dalitz plot is well described by a combination of the six resonant decay channels $\bar{K}^{*}(892)^0\eta$, $K^{-}a_0(980)^+$, $K^{-}a_2(1320)^+$, $\bar{K}^{*}(1410)^0\eta$, $K^{*}(1680)^-\pi^{+}$ and $K_2^{*}(1980)^-\pi^{+}$, together with $K\pi$ and $K\eta$ S-wave components. The decays $K^{*}(1680)^{-}\to K^{-}\eta$ and $K_{2}^{*}(1980)^{-}\to K^{-}\eta$ are observed for the first time. We measure ratio of the branching fractions, $\frac{\mathcal{B}(D^{0}\to K^{-}\pi^{+}\eta)}{\mathcal{B}(D^{0}\to K^{-}\pi^{+})}=0.500\pm0.002{\rm(stat)}\pm0.020{\rm(syst)}\pm0.003{\rm (\mathcal{B}_{PDG})}$. Using the Dalitz fit result, the ratio $\frac{\mathcal{B}(K^{*}(1680)\to K\eta)}{\mathcal{B}(K^{*}(1680)\to K\pi)}$ is measured to be $0.11\pm0.02{\rm(stat)}^{+0.06}_{-0.04}{\rm(syst)}\pm0.04{\rm(\mathcal{B}_{\text{PDG}})}$; this is much lower than the theoretical expectations ($\approx1$) made under the assumption that $K^{*}(1680)$ is a pure $1^{3}D_1$ state. The product branching fraction $\mathcal{B}(D^0\to [K_2^{*}(1980)^-\to K^{-}\eta]\pi^{+})=(2.2^{+1.7}_{-1.9})\times10^{-4}$ is determined. In addition, the $\pi\eta^{\prime}$ contribution to the $a_0(980)^{\pm}$ resonance shape is confirmed with 10.1$\sigma$ statistical significance using the three-channel Flatte model. We also measure $\mathcal{B}(D^0\to\bar{K}^{*}(892)^0\eta)=(1.41^{+0.13}_{-0.12})\%$. This is consistent with, and more precise than, the current world average $(1.02\pm0.30)\%$, deviates with a significance of more than $3\sigma$ from the theoretical predictions of (0.51-0.92)%.
Neutron Star Radius-to-mass Ratio from Partial Accretion Disk Occultation as Measured through Fe Kα Line Profiles

We present a new method to measure the radius-to-mass ratio (R/M) of weakly magnetic, disk-accreting neutron stars by exploiting the occultation of parts of the inner disk by the star itself. This occultation imprints characteristic features on the X-ray line profile that are unique and are expected to be present in low-mass X-ray binary systems seen under inclinations higher than ∼65°. We analyze a Nuclear Spectroscopic Telescope Array observation of a good candidate system, 4U 1636-53, and find that X-ray spectra from current instrumentation are unlikely to single out the occultation features owing to insufficient signal-to-noise. Based on an extensive set of simulations we show that large-area X-ray detectors of the future generation could measure R/M to ∼ % precision over a range of inclinations. Such is the precision in radius determination required to derive tight constraints on the equation of state of ultradense matter and it represents the goal that other methods also aim to achieve in the future.
Overview of the TREC 2020 Deep Learning Track

This is the second year of the TREC Deep Learning Track, with the goal of studying ad hoc ranking in the large training data regime. We again have a document retrieval task and a passage retrieval task, each with hundreds of thousands of human-labeled training queries. We evaluate using single-shot TREC-style evaluation, to give us a picture of which ranking methods work best when large data is available, with much more comprehensive relevance labeling on the small number of test queries. This year we have further evidence that rankers with BERT-style pretraining outperform other rankers in the large data regime.
Sparse Resource Allocation for Control of Spreading Processes via Convex Optimization

In this letter we propose a method for sparse allocation of resources to control spreading processes – such as epidemics and wildfires – using convex optimization, in particular exponential cone programming. Sparsity of allocation has advantages in situations where resources cannot easily be distributed over a large area. In addition, we introduce a model of risk to optimize the product of the likelihood and the future impact of an outbreak. We demonstrate with a simplified wildfire example that our method can provide more targeted resource allocation compared to previous approaches based on geometric programming.