Miracle-dz/UniFAR-FTUs huggingface dataset:
LeafGAN: An Effective Data Augmentation Method for Practical Plant Disease Diagnosis

Many applications for the automated diagnosis of plant disease have been developed based on the success of deep learning techniques. However, these applications often suffer from overfitting, and the diagnostic performance is drastically decreased when used on test data sets from new environments. In this article, we propose LeafGAN, a novel image-to-image translation system with own attention mechanism. LeafGAN generates a wide variety of diseased images via transformation from healthy images, as a data augmentation tool for improving the performance of plant disease diagnosis. Due to its own attention mechanism, our model can transform only relevant areas from images with a variety of backgrounds, thus enriching the versatility of the training images. Experiments with five-class cucumber disease classification show that data augmentation with vanilla CycleGAN cannot help to improve the generalization, i.e., disease diagnostic performance increased by only 0.7% from the baseline. In contrast, LeafGAN boosted the diagnostic performance by 7.4%. We also visually confirmed that the generated images by our LeafGAN were much better quality and more convincing than those generated by vanilla CycleGAN. The code is available publicly at https://github.com/IyatomiLab/LeafGAN. Note to Practitioners Automated plant disease diagnosis systems play an important role in the agricultural automation field. Building a practical image-based automatic plant diagnosis system requires collecting a wide variety of disease images with reliable label information. However, it is quite labor-intensive. Conventional systems have reported relatively high diagnosis performance, but most of their scores were largely biased due to the “latent similarity” between training and test images, and their true diagnosis capabilities were much lower than claimed. To address this issue, we propose LeafGAN, which generates countless diverse and high-quality training images; it works as an efficient data augmentation for the diagnosis classifier. Such generated images can be used as useful resources for improving the performance of the cucumber disease diagnosis systems.
Fair Bandit Learning with Delayed Impact of Actions

Algorithmic fairness has been studied mostly in a static setting where the implicit assumptions are that the frequencies of historically made decisions do not impact the problem structure in subsequent future. However, for example, the capability to pay back a loan for people in a certain group might depend on historically how frequently that group has been approved loan applications. If banks keep rejecting loan applications to people in a disadvantaged group, it could create a feedback loop and further damage the chance of getting loans for people in that group. This challenge has been noted in several recent works but is under-explored in a more generic sequential learning setting. In this paper, we formulate this delayed and long-term impact of actions within the context of multi-armed bandits (MAB). We generalize the classical bandit setting to encode the dependency of this action "bias" due to the history of the learning. Our goal is to learn to maximize the collected utilities over time while satisfying fairness constraints imposed over arms' utilities, which again depend on the decision they have received. We propose an algorithm that achieves a regret of $\tilde{\mathcal{O}}(KT^{2/3})$ and show a matching regret lower bound of $\Omega(KT^{2/3})$, where $K$ is the number of arms and $T$ denotes the learning horizon. Our results complement the bandit literature by adding techniques to deal with actions with long-term impacts and have implications in designing fair algorithms.
Communication-Efficient Edge AI: Algorithms and Systems

Artificial intelligence (AI) has achieved remarkable breakthroughs in a wide range of fields, ranging from speech processing, image classification to drug discovery. This is driven by the explosive growth of data, advances in machine learning (especially deep learning), and the easy access to powerful computing resources. Particularly, the wide scale deployment of edge devices (e.g., IoT devices) generates an unprecedented scale of data, which provides the opportunity to derive accurate models and develop various intelligent applications at the network edge. However, such enormous data cannot all be sent to the cloud for processing, due to the varying channel quality, traffic congestion and/or privacy concerns, and the enormous energy consumption. By pushing inference and training processes of AI models to edge nodes, edge AI has emerged as a promising alternative. AI at the edge requires close cooperation among edge devices, such as smart phones and smart vehicles, and edge servers at the wireless access points and base stations, which however result in heavy communication overheads. In this paper, we present a comprehensive survey of the recent developments in various techniques for overcoming these communication challenges. Specifically, we first identify key communication challenges in edge AI systems. We then introduce communication-efficient techniques, from both algorithmic and system perspectives for training and inference tasks at the network edge. Potential future research directions are also highlighted.
High-resolution 4-D acquisition of freely swimming human sperm cells without staining

We present a new high-resolution stain-free imaging method for optical CT of individual sperm cells during free swim. We present a new acquisition method that enables high-resolution, fine-detail full reconstruction of the three-dimensional movement and structure of individual human sperm cells swimming freely. We achieve both retrieval of the three-dimensional refractive-index profile of the sperm head, revealing its fine internal organelles and time-varying orientation, and the detailed four-dimensional localization of the thin, highly-dynamic flagellum of the sperm cell. Live human sperm cells were acquired during free swim using a high-speed off-axis holographic system that does not require any moving elements or cell staining. The reconstruction is based solely on the natural movement of the sperm cell and a novel set of algorithms, enabling the detailed four-dimensional recovery. Using this refractive-index imaging approach, we believe that we have detected an area in the cell that is attributed to the centriole. This method has great potential for both biological assays and clinical use of intact sperm cells.
Near-optimal Regret Bounds for Stochastic Shortest Path

Stochastic shortest path (SSP) is a well-known problem in planning and control, in which an agent has to reach a goal state in minimum total expected cost. In the learning formulation of the problem, the agent is unaware of the environment dynamics (i.e., the transition function) and has to repeatedly play for a given number of episodes while reasoning about the problem's optimal solution. Unlike other well-studied models in reinforcement learning (RL), the length of an episode is not predetermined (or bounded) and is influenced by the agent's actions. Recently, Tarbouriech et al. (2019) studied this problem in the context of regret minimization and provided an algorithm whose regret bound is inversely proportional to the square root of the minimum instantaneous cost. In this work we remove this dependence on the minimum cost---we give an algorithm that guarantees a regret bound of $\widetilde{O}(B_\star |S| \sqrt{|A| K})$, where $B_\star$ is an upper bound on the expected cost of the optimal policy, $S$ is the set of states, $A$ is the set of actions and $K$ is the number of episodes. We additionally show that any learning algorithm must have at least $\Omega(B_\star \sqrt{|S| |A| K})$ regret in the worst case.
Three-dimensional core-collapse supernova simulations of massive and rotating progenitors

We present 3D simulations of the core-collapse of massive rotating and non-rotating progenitors performed with the general relativistic neutrino hydrodynamics code coconut-fmt. The progenitor models include Wolf-Rayet stars with initial helium star masses of $39\, \mathrm{ M}_{\odot }$ and $20\, \mathrm{ M}_{\odot }$, and an $18\, \mathrm{ M}_{\odot }$ red supergiant. The $39\, \mathrm{ M}_{\odot }$ model is a rapid rotator, whereas the two other progenitors are non-rotating. Both Wolf-Rayet models produce healthy neutrino-driven explosions, whereas the red supergiant model fails to explode. By the end of the simulations, the explosion energies have already reached $1.1\times 10^{51}\, $ and $0.6\times 10^{51}\, \mathrm{erg}$ for the $39\, \mathrm{ M}_{\odot }$ and $20\, \mathrm{ M}_{\odot }$ model, respectively. They produce neutron stars of relatively high mass, but with modest kicks. Due to the alignment of the bipolar explosion geometry with the rotation axis, there is a relatively small misalignment of 30° between the spin and the kick in the rapidly rotating $39\, \mathrm{ M}_{\odot }$ model. For this model, we find that rotation significantly changes the dependence of the characteristic gravitational-wave frequency of the f-mode on the proto-neutron star parameters compared to the non-rotating case. Its gravitational-wave amplitudes would make it detectable out to almost 2 Mpc by the Einstein Telescope. The other two progenitors have considerably smaller detection distances, despite significant low-frequency emission in the most sensitive frequency band of current gravitational-wave detectors.
Performance / Complexity Trade-offs of the Sphere Decoder Algorithm for Massive MIMO Systems

Massive MIMO systems are seen by many researchers as a paramount technology toward next generation networks. This technology consists of hundreds of antennas that are capable of sending and receiving simultaneously a huge amount of data. One of the main challenges when using this technology is the necessity of an efficient decoding framework. The latter must guarantee both a low complexity and a good signal detection accuracy. The Sphere Decoder (SD) algorithm represents one of the promising decoding algorithms in terms of detection accuracy. However, it is inefficient for dealing with large MIMO systems due to its prohibitive complexity. To overcome this drawback, we propose to revisit the sequential SD algorithm and implement several variants that aim at finding appropriate trade-offs between complexity and performance. Then, we propose an efficient high-level parallel SD scheme based on the master/worker paradigm, which permits multiple SD instances to simultaneously explore the search space, while mitigating the overheads from load imbalance. The results of our parallel SD implementation outperform the state-of-the-art by more than 5x using similar MIMO configuration systems, and show a super-linear speedup on multicore platforms. Moreover, this paper presents a new hybrid implementation that combines the strengths of SD and K-best algorithms, i.e., maintaining the detection accuracy of SD, while reducing the complexity using the K-best way of pruning search space. The hybrid approach extends our parallel SD implementation: the master contains the SD search tree, and the workers use the K-best algorithm to accelerate its exploration. The resulting hybrid approach enhances the diversification gain, and therefore, lowers the overall complexity. Our synergistic hybrid approach permits to deal with large MIMO configurations up to 100x100, without sacrificing the accuracy and complexity.
SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation

Estimating 3D orientation and translation of objects is essential for infrastructure-less autonomous navigation and driving. In case of monocular vision, successful methods have been mainly based on two ingredients: (i) a network generating 2D region proposals, (ii) a R-CNN structure predicting 3D object pose by utilizing the acquired regions of interest. We argue that the 2D detection network is redundant and introduces non-negligible noise for 3D detection. Hence, we propose a novel 3D object detection method, named SMOKE, in this paper that predicts a 3D bounding box for each detected object by combining a single keypoint estimate with regressed 3D variables. As a second contribution, we propose a multi-step disentangling approach for constructing the 3D bounding box, which significantly improves both training convergence and detection accuracy. In contrast to previous 3D detection techniques, our method does not require complicated pre/post-processing, extra data, and a refinement stage. Despite of its structural simplicity, our proposed SMOKE network outperforms all existing monocular 3D detection methods on the KITTI dataset, giving the best state-of-the-art result on both 3D object detection and Bird’s eye view evaluation. The code is available at https://github.com/lzccccc/SMOKE.
The effect of internal gravity waves on cloud evolution in sub-stellar atmospheres

Context. Sub-stellar objects exhibit photometric variability, which is believed to be caused by a number of processes, such as magnetically-driven spots or inhomogeneous cloud coverage. Recent sub-stellar models have shown that turbulent flows and waves, including internal gravity waves, may play an important role in cloud evolution. Aims. The aim of this paper is to investigate the effect of internal gravity waves on dust nucleation and dust growth, and whether observations of the resulting cloud structures could be used to recover atmospheric density information. Methods. For a simplified atmosphere in two dimensions, we numerically solved the governing fluid equations to simulate the effect on dust nucleation and mantle growth as a result of the passage of an internal gravity wave. Furthermore, we derived an expression that relates the properties of the wave-induced cloud structures to observable parameters in order to deduce the atmospheric density. Results. Numerical simulations show that the density, pressure, and temperature variations caused by gravity waves lead to an increase of the dust nucleation rate by up to a factor 20, and an increase of the dust mantle growth rate by up to a factor 1.6, compared to their equilibrium values. Through an exploration of the wider sub-stellar parameter space, we show that in absolute terms, the increase in dust nucleation due to internal gravity waves is stronger in cooler (T dwarfs) and TiO2-rich sub-stellar atmospheres. The relative increase, however, is greater in warm (L dwarf) and TiO2-poor atmospheres due to conditions that are less suited for efficient nucleation at equilibrium. These variations lead to banded areas in which dust formation is much more pronounced, similar to the cloud structures observed on Earth. Conclusions. We show that internal gravity waves propagating in the atmosphere of sub-stellar objects can produce banded clouds structures similar to that observed on Earth. We propose a method with which potential observations of banded clouds could be used to estimate the atmospheric density of sub-stellar objects.
Abortion related stigma: a case study of abortion stigma in regions with high and low incidences of unsafe abortion

Background : Abortion accounts for 35% of maternal mortality in Kenya. Kenya has reported an increase in the rate of unsafe abortions from 32 to 48 per 1000 women of reproductive age in 2002 and 2012 respectively. During the same period, women presented in public health facilities with severe complications indicating that women were having unsafe abortions. Objective : To investigate the association between incidences of unsafe abortion and stigma attitudes and beliefs about abortion among community member’s in two counties located in regions with either high or low incidences of unsafe abortion. Design : A cross-sectional comparative study. Settings : General community members in Trans Nzoia and Machakos Counties. Subjects : Men and women of reproductive age in Trans Nzoia and Machakos Counties. Results : Respondents in Trans Nzoia County reported the highest full-scale abortion stigma levels (μ=55.4) compared to those from Machakos County (μ=53.07). The mean differences in SABAS scores for all the four subscales were significant for fear of contagion, exclusion and discrimination and Negative stereotyping (p-value <0.000). Incidence region, educational attainment and marital status were all significantly associated with stigmatising attitudes. Respondents in the 35-49 age group showed more stigmatising attitudes than younger respondents, and married individuals showed more stigmatising attitudes than single respondents, and lower education levels were associated with higher levels of stigma. Conclusions : Mean stigma scores for counties with high incidence of unsafe abortions were higher than those from regions with a low incidences of unsafe abortion. Male community members, those with lower levels of education were more likely to report higher levels of stigma at the community level. The majority of women seeking abortion were viewed negatively by general community members, and this could explain women’s decision to seek an unsafe abortion.
Polarizing Front Ends for Robust Cnns

The vulnerability of deep neural networks to small, adversarially designed perturbations can be attributed to their "excessive linearity. " In this paper, we propose a bottom-up strategy for attenuating adversarial perturbations using a nonlinear front end which polarizes and quantizes the data. We observe that ideal polarization can be utilized to completely eliminate perturbations, develop algorithms to learn approximately polarizing bases for data, and investigate the effectiveness of the proposed strategy on the MNIST and Fashion MNIST datasets.
String graphs have the Erd\H{o}s-Hajnal property

A string graph is the intersection graph of curves in the plane. We prove that there exists an absolute constant $c>0$ such that if $G$ is a string graph on $n$ vertices, then $G$ contains either a clique or an independent set of size at least $n^{c}$.
Collectivity of heavy mesons in proton-nucleus collisions

Using a model based on the color glass condensate framework and the dilute-dense factorization, we systematically study the azimuthal angular correlations between a heavy flavor meson and a light reference particle in proton-nucleus collisions. The obtained second-harmonic coefficients (also known as the elliptic flows) for $J/\ensuremath{\psi}$ and ${D}^{0}$ agree with recent experimental data from the LHC. We also provide predictions for the elliptic flows of $\mathrm{\ensuremath{\Upsilon}}$ and $B$ mesons, which can be measured in the near future at the LHC. This work can shed light on the physical origin of the collectivity phenomenon in the collisions of small systems.
Bayes-Probe: Distribution-Guided Sampling for Prediction Level Sets

Building machine learning models requires a suite of tools for interpretation, understanding, and debugging. Many existing methods have been proposed, but it can still be difficult to probe for examples which communicate model behaviour. We introduce Bayes-Probe, a model inspection method for analyzing neural networks by generating distribution-conforming examples of known prediction confidence. By selecting appropriate distributions and confidence prediction values, Bayes-Probe can be used to synthesize ambivalent predictions, uncover in-distribution adversarial examples, and understand novel-class extrapolation and domain adaptation behaviours. Bayes-Probe is model agnostic, requiring only a data generator and classifier prediction. We use Bayes-Probe to analyze models trained on both procedurally-generated data (CLEVR) and organic data (MNIST and Fashion-MNIST). Code is available at https://github.com/serenabooth/Bayes-Probe.
Preference Modeling with Context-Dependent Salient Features

We consider the problem of estimating a ranking on a set of items from noisy pairwise comparisons given item features. We address the fact that pairwise comparison data often reflects irrational choice, e.g. intransitivity. Our key observation is that two items compared in isolation from other items may be compared based on only a salient subset of features. Formalizing this framework, we propose the salient feature preference model and prove a finite sample complexity result for learning the parameters of our model and the underlying ranking with maximum likelihood estimation. We also provide empirical results that support our theoretical bounds and illustrate how our model explains systematic intransitivity. Finally we demonstrate strong performance of maximum likelihood estimation of our model on both synthetic data and two real data sets: the UT Zappos50K data set and comparison data about the compactness of legislative districts in the US.
Application of a Modified Spheromak Model to Simulations of Coronal Mass Ejection in the Inner Heliosphere

The magnetic fields of interplanetary coronal mass ejections (ICMEs), which originate close to the Sun in the form of a flux rope, determine their geoeffectiveness. Therefore, robust flux rope‐based models of CMEs are required to perform magnetohydrodynamic (MHD) simulations aimed at space weather predictions. We propose a modified spheromak model and demonstrate its applicability to CME simulations. In this model, such properties of a simulated CME as the poloidal and toroidal magnetic fluxes, and the helicity sign can be controlled with a set of input parameters. We propose a robust technique for introducing CMEs with an appropriate speed into a background, MHD solution describing the solar wind in the inner heliosphere. Through a parametric study, we find that the speed of a CME is much more dependent on its poloidal flux than on the toroidal flux. We also show that the CME speed increases with its total energy, giving us control over its initial speed. We further demonstrate the applicability of this model to simulations of CME‐CME collisions. Finally, we use this model to simulate the 12 July 2012 CME and compare the plasma properties at 1 AU with observations. The predicted CME properties agree reasonably with observational data.
Communication Contention Aware Scheduling of Multiple Deep Learning Training Jobs

Distributed Deep Learning (DDL) has rapidly grown its popularity since it helps boost the training performance on high-performance GPU clusters. Efficient job scheduling is indispensable to maximize the overall performance of the cluster when training multiple jobs simultaneously. However, existing schedulers do not consider the communication contention of multiple communication tasks from different distributed training jobs, which could deteriorate the system performance and prolong the job completion time. In this paper, we first establish a new DDL job scheduling framework which organizes DDL jobs as Directed Acyclic Graphs (DAGs) and considers communication contention between nodes. We then propose an efficient algorithm, LWF-$\kappa$, to balance the GPU utilization and consolidate the allocated GPUs for each job. When scheduling those communication tasks, we observe that neither avoiding all the contention nor blindly accepting them is optimal to minimize the job completion time. We thus propose a provable algorithm, AdaDUAL, to efficiently schedule those communication tasks. Based on AdaDUAL, we finally propose Ada-SRSF for the DDL job scheduling problem. Simulations on a 64-GPU cluster connected with 10 Gbps Ethernet show that LWF-$\kappa$ achieves up to $1.59\times$ improvement over the classical first-fit algorithms. More importantly, Ada-SRSF reduces the average job completion time by $20.1\%$ and $36.7\%$, as compared to the SRSF(1) scheme (avoiding all the contention) and the SRSF(2) scheme (blindly accepting all of two-way communication contention) respectively.