Miracle-dz/UniFAR-FTUs huggingface dataset:
Steering Distortions to Preserve Classes and Neighbors in Supervised Dimensionality Reduction

This supplementary material demonstrates the link between NeRV and Class-NeRV in its unsupervised case (Section 1), illustrates individual effect of each sub-term of ClassNeRV stress with an ablation study (Section 2) and presents the full confusion matrix of the 10-NN classiﬁer on the Isolet dataset (Section 3). It also provides the parameters of the DR techniques (Section 4), additional experiments to support our claim while accounting for randomness of stochastic methods (Section 5), quantitative comparison with unsupervised DR methods for the digits dataset (Section 6) and additional supervised indicators (Section 7)
Model Selection for Production System via Automated Online Experiments

A challenge that machine learning practitioners in the industry face is the task of selecting the best model to deploy in production. As a model is often an intermediate component of a production system, online controlled experiments such as A/B tests yield the most reliable estimation of the effectiveness of the whole system, but can only compare two or a few models due to budget constraints. We propose an automated online experimentation mechanism that can efficiently perform model selection from a large pool of models with a small number of online experiments. We derive the probability distribution of the metric of interest that contains the model uncertainty from our Bayesian surrogate model trained using historical logs. Our method efficiently identifies the best model by sequentially selecting and deploying a list of models from the candidate set that balance exploration-exploitation. Using simulations based on real data, we demonstrate the effectiveness of our method on two different tasks.
MultiON: Benchmarking Semantic Map Memory using Multi-Object Navigation

Navigation tasks in photorealistic 3D environments are challenging because they require perception and effective planning under partial observability. Recent work shows that map-like memory is useful for long-horizon navigation tasks. However, a focused investigation of the impact of maps on navigation tasks of varying complexity has not yet been performed. We propose the multiON task, which requires navigation to an episode-specific sequence of objects in a realistic environment. MultiON generalizes the ObjectGoal navigation task and explicitly tests the ability of navigation agents to locate previously observed goal objects. We perform a set of multiON experiments to examine how a variety of agent models perform across a spectrum of navigation task complexities. Our experiments show that: i) navigation performance degrades dramatically with escalating task complexity; ii) a simple semantic map agent performs surprisingly well relative to more complex neural image feature map agents; and iii) even oracle map agents achieve relatively low performance, indicating the potential for future work in training embodied navigation agents using maps. Video summary: this https URL
Resequencing and Analysis of Whole Genome of Local Tribolium castaneum Using Next-generation Sequencing

The flour beetles Tribolium castaneum (Herbst) was noted as an important model organism for studies of insect physiology, for pest control and other topic[1]. As a product pest, T. castaneum is widely distributed all over the world. We reported and analyzed the genomic characteristics of local T. castaneum using next-generation sequencing. It is hoped to carry out pest control through its variation characteristics of SNP, Indel, SV and CNV. Through the variation detection, it was found that there were a lot of variations in the energy metabolism pathway, which was closely related to the antioxidant and cold resistance. It can be used as a genomic variation level reference for the subsequent prevention and control of local T. castaneum.
Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation

Zero-shot semantic segmentation (ZSS) aims to classify pixels of novel classes without training examples available. Recently, most ZSS methods focus on learning the visual-semantic correspondence to transfer knowledge from seen classes to unseen classes at the pixel level. Yet, few works study the adverse effects caused by the noisy and outlying training samples of the seen classes. In this paper, we identify this challenge and address it with a novel framework that learns to discriminate noisy samples based on Bayesian uncertainty estimation. Speciﬁcally, we model the network outputs with Gaussian and Laplacian distributions, with the variances accounting for the observation noise and uncertainty of input samples. Learning objectives are then derived with the estimated variances playing as adaptive attenuation for individual samples in training. Consequently, our model learns more attentively from representative samples of seen classes while suffering less from noisy and outlying ones, thus providing better reliability and generalization toward unseen categories. We demonstrate the effectiveness of our framework through comprehensive experiments on multiple challenging benchmarks, and show that our method achieves signiﬁcant accuracy improvement over previous approaches for large-scale open-set segmentation.
Offline Imitation Learning with a Misspecified Simulator

In real-world decision-making tasks, learning an optimal policy without a trial-and-error process is an appealing challenge. When expert demonstrations are available, imitation learning that mimics expert actions can learn a good policy ef-ﬁciently. Learning in simulators is another commonly adopted approach to avoid real-world trials-and-errors. However, neither sufﬁcient expert demonstrations nor high-ﬁdelity simulators are easy to obtain. In this work, we investigate policy learning in the condition of a few expert demonstrations and a simulator with misspeciﬁed dynamics. Under a mild assumption that local states shall still be partially aligned under a dynamics mismatch, we propose imitation learning with horizon-adaptive inverse dynamics (HIDIL) that matches the simulator states with expert states in a H -step horizon and accurately recovers actions based on inverse dynamics policies. In the real environment, HIDIL can effectively derive adapted actions from the matched states. Experiments are conducted in four MuJoCo loco-motion environments with modiﬁed friction, gravity, and density conﬁgurations. Experiment results show that HIDIL achieves signiﬁcant improvement in terms of performance and stability in all of the real environments, compared with imitation learning methods and transferring methods in reinforcement learning.
On the Power of Louvain in the Stochastic Block Model

A classic problem in machine learning and data analysis is to partition the vertices of a network in such a way that vertices in the same set are densely connected and vertices in different sets are loosely connected. In practice, the most popular approaches rely on local search algorithms; not only for the ease of implementation and the efﬁciency, but also because of the accuracy of these methods on many real world graphs. For example, the Louvain algorithm – a local search based algorithm – has quickly become the method of choice for clustering in social networks. However, explaining the success of these methods remains an open problem: in the worst-case, the runtime can be up to ⌦ ( n 2 ) , much worse than what is typically observed in practice, and no guarantee on the quality of its output can be established. The goal of this paper is to shed light on the inner-workings of Louvain; only if we understand Louvain, can we rely on it and further improve it. To achieve this goal, we study the behavior of Louvain in the famous two-bloc Stochastic Block Model, which has a clear ground-truth and serves as the standard testbed for graph clustering algorithms. We provide valuable tools for the analysis of Louvain, but also for many other combinatorial algorithms. For example, we show that the probability for a node to have more edges towards its own community is 1 / 2 + ⌦ (min( � ( p � q ) / p np, 1)) in the SBM( n, p, q ), where � is the imbalance. Note that this bound is asymptotically tight and useful for the analysis of a wide range of algorithms (Louvain, Kernighan-Lin, Simulated Annealing etc).
MSU-Net: A multi-scale U-Net for retinal vessel segmentation

Retinal vessel segmentation is widely used in the diagnosis of eye diseases, and the effect of segmentation plays a crucial role in whether doctors can correctly diagnose diseases. To further improve the accuracy of the automatic segmentation method, a network structure named Multi-Scale U-Net (MSU-Net) based on deep learning is proposed in this paper. The network combines Atrous Spatial Pyramid Pooling (ASPP) module to extract multi-scale information, making the U-Net more suitable for segmentation of complex and changeable vessel structures. We evaluate the network on two public databases, DRIVE and STARE. The Accuracy (ACC), Sensitivity (SEN), Specificity (SPE) and Dice coefficient on the DRIVE database are 0.9667, 0.8159, 0.9805 and 0.8059, respectively. These indicators are respectively 0.9732, 0.8272, 0.9866 and 0.8400 on the STARE database. Experiments show that the network has excellent segmentation results, and has state-of-the-art performance indicators on the STARE database, which fully proves the outstanding performance of the network.
Asymptotic normality and confidence intervals for derivatives of 2-layers neural network in the random features model

This paper studies two-layers Neural Networks (NN), where the ﬁrst layer contains random weights, and the second layer is trained using Ridge regularization. This model has been the focus of numerous recent works, showing that despite its simplicity, it captures some of the empirically observed behaviors of NN in the over-parametrized regime, such as the double-descent curve where the generalization error decreases as the number of weights increases to + ∞ . This paper establishes asymptotic distribution results for this 2-layers NN model in the regime where the ratios pn and dn have ﬁnite limits, where n is the sample size, p the ambient dimension and d is the width of the ﬁrst layer. We show that a weighted average of the derivatives of the trained NN at the observed data is asymptotically normal, in a setting with Lipschitz activation functions in a linear regression response with Gaussian features under possibly non-linear perturbations. We then leverage this asymptotic normality result to construct conﬁdence intervals (CIs) for single components of the unknown regression vector. The novelty of our results are threefold: (1) Despite the nonlinearity induced by the activation function, we characterize the asymptotic distribution of a weighted average of the gradients of the network after training; (2) It provides the ﬁrst frequentist uncertainty quantiﬁcation guarantees, in the form of valid ( 1 - α )-CIs, based on NN estimates; (3) It shows that the double-descent phenomenon occurs in terms of the length of the CIs, with the length increasing and then decreasing as d n (cid:37) + ∞ for certain ﬁxed values of p n . We also provide a toolbox to predict the length of CIs numerically, which lets
A Numerical Method for Weakly Singular Nonlinear Volterra Integral Equations of the Second Kind

This paper presents a numerical iterative method for the approximate solutions of nonlinear Volterra integral equations of the second kind, with weakly singular kernels. We derive conditions so that a unique solution of such equations exists, as the unique fixed point of an integral operator. Iterative application of that operator to an initial function yields a sequence of functions converging to the true solution. Finally, an appropriate numerical integration scheme (a certain type of product integration) is used to produce the approximations of the solution at given nodes. The resulting procedure is a numerical method that is more practical and accessible than the classical approximation techniques. We prove the convergence of the method and give error estimates. The proposed method is applied to some numerical examples, which are discussed in detail. The numerical approximations thus obtained confirm the theoretical results and the predicted error estimates. In the end, we discuss the method, drawing conclusions about its applicability and outlining future possible research ideas in the same area.
Breaking Symmetry: Fluctuating Asymmetry and Geometric Morphometrics as Tools for Evaluating Developmental Instability under Diverse Agroecosystems

Fluctuating asymmetry (FA), in contrast with other asymmetries, is the bilateral asymmetry that represents small, random developmental differences between right and left sides. After nearly a century of using traditional morphometrics in the estimation of FA, geometric morphometrics (GM) now provides new insights into the use of FA as a tool, especially for assessing environmental and developmental stress. Thus, it will be possible to assess adaptation to various environmental stressors as particular triggers for unavoidable selection pressures. In this review, we describe measures of FA that use geometric morphometrics, and we include a flow chart of the methodology. We also describe how this combination (GM + FA) has been tested in several agroecosystems. Nutritional stress, temperature, chemical pollution, and population density are known stressors experienced by populations in agroecosystems.
Minibatch Stochastic Approximate Proximal Point Methods

We extend the Approximate-Proximal Point ( A P ROX ) family of model-based methods for solving stochastic convex optimization problems, including stochastic subgradient, proximal point, and bundle methods, to the minibatch setting. To do this, we propose two minibatched algorithms for which we prove a non-asymptotic upper bound on the rate of convergence, revealing a linear speedup in minibatch size. In contrast to standard stochastic gradient methods, these methods may have linear speedup in the minibatch setting even for non-smooth functions. Our algorithms maintain the desirable traits characteristic of the A P ROX family, such as robustness to initial step size choice. Additionally, we show improved convergence rates for "interpolation" problems, which (for example) gives a new parallelization strategy for alternating projections. We corroborate our theoretical results with extensive empirical testing, which demonstrates the gains provided by accurate modeling and minibatching.
Generalization Bound of Gradient Descent for Non-Convex Metric Learning

Metric learning aims to learn a distance measure that can beneﬁt distance-based 1 methods such as the nearest neighbor (NN) classiﬁer. While considerable efforts 2 have been made to improve its empirical performance and analyze its generalization 3 ability by focusing on the data structure and model complexity, an unresolved ques-4 tion is how choices of algorithmic parameters such as training time affect metric 5 learning as it is typically formulated as an optimization problem and nowadays 6 more often as a non-convex problem. In this paper, we theoretically address this 7 question and prove the agnostic Probably Approximately Correct (PAC) learnabil-8 ity for metric learning algorithms with non-convex objective functions optimized 9 via gradient descent (GD); in particular, our theoretical guarantee takes training 10 time into account. We ﬁrst show that the generalization PAC bound is a sufﬁcient 11 condition for agnostic PAC learnability and this bound can be obtained by ensuring 12 the uniform convergence on a densely concentrated subset of the parameter space. 13 We then show that, for classiﬁers optimized via GD, their generalizability can 14 be guaranteed if the classiﬁer and loss function are both Lipschitz smooth, and 15 further improved by using fewer iterations. To illustrate and exploit the theoretical 16 ﬁndings, we ﬁnally propose a novel metric learning method called S mooth M etric 17 and representative I nstance LE arning (SMILE), designed to satisfy the Lipschitz 18 smoothness property and learned via GD with an early stopping mechanism for 19 better discriminability and less computational cost of NN. 20
Preparation of a bi-hapten antigen and the broad-specific antibody for simultaneous immunoassay of penicillins and tetracyclines in milk

Abstract A novel bi-hapten immunogen was prepared by coupling amoxicillin and chlortetracycline to bovine serum albumin in turn. Then the immunogen was used to immunise New Zealand rabbits to produce the polyclonal antibody. The obtained antibody simultaneously cross-reacted with six penicillins (amoxicillin, penicillin G, ampicillin, penicillin V, carbencillin and sulbenicillin) and four tetracyclines (tetracycline, oxytetracycline, doxycycline and chlortetracycline) with crossreactivities in a range of 24–100%. Then a broad specificity enzyme linked immunosorbent assay was optimised for the simultaneous detection of the 10 drugs in milk. The limits of detection for these analytes were in a range of 0.4–3.7 ng/mL depending on the compound. The intra- and inter-assay recoveries of the 10 analytes from standards fortified blank milk ranged from 80.8 to 99.4% with coefficients of variation lower than 13%. This study provided a new strategy for the development of multi-analyte immunoassay through immunogen design.
Neural FFTs for Universal Texture Image Synthesis

Synthesizing larger texture images from a smaller exemplar is an important task in graphics and vision. The conventional CNNs, recently adopted for synthesis, require to train and test on the same set of images and fail to generalize to unseen images. This is mainly because those CNNs fully rely on convolutional and upsampling layers that operate locally and not suitable for a task as global as texture synthesis. In this work, inspired by the repetitive nature of texture patterns, we ﬁnd that texture synthesis can be viewed as (local) upsampling in the Fast Fourier Transform (FFT) domain. However, FFT of natural images exhibits high dynamic range and lacks local correlations. Therefore, to train CNNs we design a framework to perform FFT upsampling in feature space using deformable convolutions. Such design allows our framework to generalize to unseen images, and synthesize textures in a single pass. Extensive evaluations conﬁrm that our method achieves state-of-the-art performance both quantitatively and qualitatively.
Competitiveness of windmill grass (Chloris truncata) and feathertop Rhodes grass (Chloris virgata) in mungbean (Vigna radiata)

Abstract. Windmill grass (Chloris truncata R.Br.) and feathertop Rhodes grass (Chloris virgata Sw.) are two weeds of the northern region of Australia that are rapidly expanding in range, being favoured by conservation agricultural systems and prevailing weed management using a narrow pool of herbicides. Information on competitiveness and seed-production dynamics of these weeds is lacking for mungbean (Vigna radiata (L.) Wilczek), a major summer crop in the region. Field studies were conducted to evaluate the effect of different densities of these weed species on crop yield of mungbean in the 2016–17 (2016) and 2017–18 (2017) seasons. Windmill grass reduced mungbean yields by 56% with 39 weed plants m–2 in 2016 and 55% with 47 weed plants m–2 in 2017. Windmill grass produced a maximum of 98 708 seeds m–2 in 2016 and 118 613 seeds m–2 in 2017, and there was 15–21% seed dispersal at crop harvest. Competition from feathertop Rhodes grass resulted in yield losses of 73% with 49 weed plants m–2 and 65% with 45 weed plants m–2. Feathertop Rhodes grass produced a maximum of 229 514 seeds m–2 in 2016 and 367 190 seeds m–2 in 2017, and seed dispersal at crop harvest was only 3–7%. Competition from both weed species resulted in a significant reduction in number of pods per m2, grains per pod and 1000-grain weight of mungbean. These results show that windmill grass and feathertop Rhodes are highly competitive against mungbean, and their timely management is crucial for minimising yield loss. Although both weeds produced a substantial number of seeds, seed dispersal at crop harvest was low, especially for feathertop Rhodes grass. The high weed-seed retention relative to maturity of mungbean may help in managing these problematic weeds through various means including weed-seed capturing and destruction
CLEARER: Multi-Scale Neural Architecture Search for Image Restoration

Multi-scale neural networks have shown effectiveness in image restoration tasks, which are usually designed and integrated in a handcrafted manner. Different from the existing labor-intensive handcrafted architecture design paradigms, we present a novel method, termed as multi-sCaLe nEural ARchitecture sEarch for image Restoration (CLEARER), which is a speciﬁcally designed neural architecture search (NAS) for image restoration. Our contributions are twofold. On one hand, we design a multi-scale search space that consists of three task-ﬂexible modules. Namely, 1) Parallel module that connects multi-resolution neural blocks in parallel, while preserving the channels and spatial-resolution in each neural block, 2) Transition module remains the existing multi-resolution features while extending them to a lower resolution, 3) Fusion module integrates multi-resolution features by passing the features of the parallel neural blocks to the current neural blocks. On the other hand, we present novel losses which could 1) balance the tradeoff between the model complexity and performance, which is highly expected to image restoration; and 2) relax the discrete architecture parameters into a continuous distribution which approximates to either 0 or 1. As a result, a differentiable strategy could be employed to search when to fuse or extract multi-resolution features, while the discretization issue faced by the gradient-based NAS could be alleviated. The proposed CLEARER could search a promising architecture in two GPU hours. Extensive experiments show the promising performance of our method comparing with nine image denoising methods and eight image deraining approaches in quantitative and qualitative evaluations. The codes are available at https://github.com/limit-scu .
Community detection using fast low-cardinality semidefinite programming 

Modularity maximization has been a fundamental tool for understanding the community structure of a network, but the underlying optimization problem is nonconvex and NP-hard to solve. State-of-the-art algorithms like the Louvain or Leiden methods focus on different heuristics to help escape local optima, but they still depend on a greedy step that moves node assignment locally and is prone to getting trapped. In this paper, we propose a new class of low-cardinality algorithm that generalizes the local update to maximize a semidefinite relaxation derived from max-k-cut. This proposed algorithm is scalable, empirically achieves the global semidefinite optimality for small cases, and outperforms the state-of-the-art algorithms in real-world datasets with little additional time cost. From the algorithmic perspective, it also opens a new avenue for scaling-up semidefinite programming when the solutions are sparse instead of low-rank.
R-learning in actor-critic model offers a biologically relevant mechanism for sequential decision-making

When should you continue with your ongoing plans and when should you instead decide to pursue better opportunities? We show in theory and experiment that such stay-or-leave decisions are consistent with deep R-learning both behaviorally and neuronally. Our results suggest that real-world agents leave depleting resources when their reward rate falls below its exponential average, which, we argue, is a Bayes optimal rule in dynamic natural environments. Our work links reinforcement learning, the marginal value theorem and Bayesian inference approaches to offer a learning algorithm and a decision rule for making sequential stay-or-leave choices