在过去的十年中，神经网络在各种各样的反问题中取得了显着的成功，从医学成像到地震分析等学科中的采用促进了他们的收养。但是，这种反问题的高维度同时使当前理论预测，网络应在问题的维度上成倍扩展，无法解释为什么在这些设置中使用的看似很小的网络在实践中也可以正常工作。为了减少理论和实践之间的差距，在本文中提供了一种在具有低复杂性结构的高维置的神经网络近似Lipschitz函数所需的复杂性的一般方法。该方法基于这样的观察，即在\ mathbb {r}^in \ mathbb {r}^{d \ times d} $ in \ mathbb {a} \ in \ mathbb {a} \ in \ mathcal集合$ \ mathcal {S } \ subset \ mathbb {r}^d $中的低维立方体$ [ - m，m]^d $意味着对于任何Lipschitz函数$ f：\ mathcal {s} \ to \ mathbb {r}^p $ ，存在lipschitz函数$ g：[-m，m]^d \ to \ mathbb {r}^p $，使得$ g（\ mathbf {a} \ mathbf {x}）= f（\ mathbf {x }）$用于所有$ \ mathbf {x} \ in \ mathcal {s} $。因此，如果一个人具有一个近似$ g的神经网络：[-m，m]^d \ to \ mathbb {r}^p $，则可以添加一个图层，以实现JL嵌入$ \ mathbf {A a} $要获得一个近似于$ f的神经网络：\ mathcal {s} \ to \ mathbb {r}^p $。通过将JL嵌入结果与神经网络近似Lipschitz函数的近似结果配对，然后获得了一个结果，这些结果绑定了神经网络所需的复杂性，以近似Lipschitz在高尺寸集合上的功能。最终结果是一个一般的理论框架，然后可以用它来更好地解释比当前理论所允许的更广泛的逆问题中较小的网络的经验成功。

令$ \ mathcal {m} $为$ \ mathbb {r}^n $的紧凑型$ d $ - d $ dimensional submanifold，带有覆盖率$ \ tau $和volume $ v _ {\ mathcal m} $。修复$ \ epsilon \ in（0,1）$。在本文中，我们证明了非线性函数$ f：\ mathbb {r}^n \ rightArrow \ mathbb {r}^{m} $在$ m \ leq c \ left（d / \ epsilon^2 \ right）中存在\ log \ left（\ frac {\ sqrt [d] {v _ {\ mathcal m}}}​​}}}} {\ tau} \ right）$，因此$（1- \ epsilon）\ | {\ bf x} - {\ bf y} \ | _2 \ leq \ left \ | f（{\ bf x}） - f（{\ bf y}）\ right \ | _2 \ leq（1 + \ epsilon）\ | {\ bf x} - {\ bf y} \ | _2 $$所有$ {\ bf x} \ in \ mathcal {m} $和$ {\ bf y} \ in \ mathbb {r} 。实际上，$ f $不仅用$ \ mathcal {m} $作为bi-lipschitz函数作为$ \ mathbb {r}^{m}^{m} $，带有bi-lipschitz常数接近一个，而且还保留了所有距离从$ \ Mathcal {m} $中的点从其图像中的$ \ Mathcal {m} $中的所有点。此外，证明具有建设性，并产生了一种在实践中效果很好的算法。特别是，本文在经验上证明，这种非线性函数允许比标准线性线性约翰逊·林登斯特劳斯（Johnson-Lindenstrauss Embeddings）在实践中更准确的压缩邻居分类。

Research on automated essay scoring has become increasing important because it serves as a method for evaluating students' written-responses at scale. Scalable methods for scoring written responses are needed as students migrate to online learning environments resulting in the need to evaluate large numbers of written-response assessments. The purpose of this study is to describe and evaluate three active learning methods than can be used to minimize the number of essays that must be scored by human raters while still providing the data needed to train a modern automated essay scoring system. The three active learning methods are the uncertainty-based, the topological-based, and the hybrid method. These three methods were used to select essays included as part of the Automated Student Assessment Prize competition that were then classified using a scoring model that was training with the bidirectional encoder representations from transformer language model. All three active learning methods produced strong results, with the topological-based method producing the most efficient classification. Growth rate accuracy was also evaluated. The active learning methods produced different levels of efficiency under different sample size allocations but, overall, all three methods were highly efficient and produced classifications that were similar to one another.

This paper presents a novel framework for planning in unknown and occluded urban spaces. We specifically focus on turns and intersections where occlusions significantly impact navigability. Our approach uses an inpainting model to fill in a sparse, occluded, semantic lidar point cloud and plans dynamically feasible paths for a vehicle to traverse through the open and inpainted spaces. We demonstrate our approach using a car's lidar data with real-time occlusions, and show that by inpainting occluded areas, we can plan longer paths, with more turn options compared to without inpainting; in addition, our approach more closely follows paths derived from a planner with no occlusions (called the ground truth) compared to other state of the art approaches.

Feature acquisition algorithms address the problem of acquiring informative features while balancing the costs of acquisition to improve the learning performances of ML models. Previous approaches have focused on calculating the expected utility values of features to determine the acquisition sequences. Other approaches formulated the problem as a Markov Decision Process (MDP) and applied reinforcement learning based algorithms. In comparison to previous approaches, we focus on 1) formulating the feature acquisition problem as a MDP and applying Monte Carlo Tree Search, 2) calculating the intermediary rewards for each acquisition step based on model improvements and acquisition costs and 3) simultaneously optimizing model improvement and acquisition costs with multi-objective Monte Carlo Tree Search. With Proximal Policy Optimization and Deep Q-Network algorithms as benchmark, we show the effectiveness of our proposed approach with experimental study.

The celebrated proverb that "speech is silver, silence is golden" has a long multinational history and multiple specific meanings. In written texts punctuation can in fact be considered one of its manifestations. Indeed, the virtue of effectively speaking and writing involves - often decisively - the capacity to apply the properly placed breaks. In the present study, based on a large corpus of world-famous and representative literary texts in seven major Western languages, it is shown that the distribution of intervals between consecutive punctuation marks in almost all texts can universally be characterised by only two parameters of the discrete Weibull distribution which can be given an intuitive interpretation in terms of the so-called hazard function. The values of these two parameters tend to be language-specific, however, and even appear to navigate translations. The properties of the computed hazard functions indicate that among the studied languages, English turns out to be the least constrained by the necessity to place a consecutive punctuation mark to partition a sequence of words. This may suggest that when compared to other studied languages, English is more flexible, in the sense of allowing longer uninterrupted sequences of words. Spanish reveals similar tendency to only a bit lesser extent.

This report summarizes the 3rd International Verification of Neural Networks Competition (VNN-COMP 2022), held as a part of the 5th Workshop on Formal Methods for ML-Enabled Autonomous Systems (FoMLAS), which was collocated with the 34th International Conference on Computer-Aided Verification (CAV). VNN-COMP is held annually to facilitate the fair and objective comparison of state-of-the-art neural network verification tools, encourage the standardization of tool interfaces, and bring together the neural network verification community. To this end, standardized formats for networks (ONNX) and specification (VNN-LIB) were defined, tools were evaluated on equal-cost hardware (using an automatic evaluation pipeline based on AWS instances), and tool parameters were chosen by the participants before the final test sets were made public. In the 2022 iteration, 11 teams participated on a diverse set of 12 scored benchmarks. This report summarizes the rules, benchmarks, participating tools, results, and lessons learned from this iteration of this competition.

Automatic machine translation (MT) metrics are widely used to distinguish the translation qualities of machine translation systems across relatively large test sets (system-level evaluation). However, it is unclear if automatic metrics are reliable at distinguishing good translations from bad translations at the sentence level (segment-level evaluation). In this paper, we investigate how useful MT metrics are at detecting the success of a machine translation component when placed in a larger platform with a downstream task. We evaluate the segment-level performance of the most widely used MT metrics (chrF, COMET, BERTScore, etc.) on three downstream cross-lingual tasks (dialogue state tracking, question answering, and semantic parsing). For each task, we only have access to a monolingual task-specific model. We calculate the correlation between the metric's ability to predict a good/bad translation with the success/failure on the final task for the Translate-Test setup. Our experiments demonstrate that all metrics exhibit negligible correlation with the extrinsic evaluation of the downstream outcomes. We also find that the scores provided by neural metrics are not interpretable mostly because of undefined ranges. Our analysis suggests that future MT metrics be designed to produce error labels rather than scores to facilitate extrinsic evaluation.

Reliable and automated 3D plant shoot segmentation is a core prerequisite for the extraction of plant phenotypic traits at the organ level. Combining deep learning and point clouds can provide effective ways to address the challenge. However, fully supervised deep learning methods require datasets to be point-wise annotated, which is extremely expensive and time-consuming. In our work, we proposed a novel weakly supervised framework, Eff-3DPSeg, for 3D plant shoot segmentation. First, high-resolution point clouds of soybean were reconstructed using a low-cost photogrammetry system, and the Meshlab-based Plant Annotator was developed for plant point cloud annotation. Second, a weakly-supervised deep learning method was proposed for plant organ segmentation. The method contained: (1) Pretraining a self-supervised network using Viewpoint Bottleneck loss to learn meaningful intrinsic structure representation from the raw point clouds; (2) Fine-tuning the pre-trained model with about only 0.5% points being annotated to implement plant organ segmentation. After, three phenotypic traits (stem diameter, leaf width, and leaf length) were extracted. To test the generality of the proposed method, the public dataset Pheno4D was included in this study. Experimental results showed that the weakly-supervised network obtained similar segmentation performance compared with the fully-supervised setting. Our method achieved 95.1%, 96.6%, 95.8% and 92.2% in the Precision, Recall, F1-score, and mIoU for stem leaf segmentation and 53%, 62.8% and 70.3% in the AP, AP@25, and AP@50 for leaf instance segmentation. This study provides an effective way for characterizing 3D plant architecture, which will become useful for plant breeders to enhance selection processes.

Large language models (LLMs) have demonstrated strong performance in zero-shot reasoning tasks, including abductive reasoning. This is reflected in their ability to perform well on current benchmarks in this area. However, to truly test the limits of LLMs in abductive reasoning, a more challenging benchmark is needed. In this paper, we present such a benchmark, consisting of 191 long-form mystery stories, each approximately 1200 words in length and presented in the form of detective puzzles. Each puzzle includes a multiple-choice question for evaluation sourced from the "5 Minute Mystery" platform. Our results show that state-of-the-art GPT models perform significantly worse than human solvers on this benchmark, with an accuracy of 28\% compared to 47\% for humans. This indicates that there is still a significant gap in the abductive reasoning abilities of LLMs and highlights the need for further research in this area. Our work provides a challenging benchmark for future studies on reasoning in language models and contributes to a better understanding of the limits of LLMs' abilities.