We present a new dataset for 6-DoF pose estimation of known objects, with a focus on robotic manipulation research. We propose a set of toy grocery objects, whose physical instantiations are readily available for purchase and are appropriately sized for robotic grasping and manipulation. We provide 3D scanned textured models of these objects, suitable for generating synthetic training data, as well as RGBD images of the objects in challenging, cluttered scenes exhibiting partial occlusion, extreme lighting variations, multiple instances per image, and a large variety of poses. Using semi-automated RGBD-to-model texture correspondences, the images are annotated with ground truth poses accurate within a few millimeters. We also propose a new pose evaluation metric called ADD-H based on the Hungarian assignment algorithm that is robust to symmetries in object geometry without requiring their explicit enumeration. We share pre-trained pose estimators for all the toy grocery objects, along with their baseline performance on both validation and test sets. We offer this dataset to the community to help connect the efforts of computer vision researchers with the needs of roboticists.

Transforming off-the-shelf deep neural network (DNN) models into dynamic multi-exit architectures can achieve inference and transmission efficiency by fragmenting and distributing a large DNN model in edge computing scenarios (e.g., edge devices and cloud servers). In this paper, we propose a novel backdoor attack specifically on the dynamic multi-exit DNN models. Particularly, we inject a backdoor by poisoning one DNN model's shallow hidden layers targeting not this vanilla DNN model but only its dynamically deployed multi-exit architectures. Our backdoored vanilla model behaves normally on performance and cannot be activated even with the correct trigger. However, the backdoor will be activated when the victims acquire this model and transform it into a dynamic multi-exit architecture at their deployment. We conduct extensive experiments to prove the effectiveness of our attack on three structures (ResNet-56, VGG-16, and MobileNet) with four datasets (CIFAR-10, SVHN, GTSRB, and Tiny-ImageNet) and our backdoor is stealthy to evade multiple state-of-the-art backdoor detection or removal methods.

The General Associative Memory Model (GAMM) has a constant state-dependant energy surface that leads the output dynamics to fixed points, retrieving single memories from a collection of memories that can be asynchronously preloaded. We introduce a new class of General Sequential Episodic Memory Models (GSEMM) that, in the adiabatic limit, exhibit temporally changing energy surface, leading to a series of meta-stable states that are sequential episodic memories. The dynamic energy surface is enabled by newly introduced asymmetric synapses with signal propagation delays in the network's hidden layer. We study the theoretical and empirical properties of two memory models from the GSEMM class, differing in their activation functions. LISEM has non-linearities in the feature layer, whereas DSEM has non-linearity in the hidden layer. In principle, DSEM has a storage capacity that grows exponentially with the number of neurons in the network. We introduce a learning rule for the synapses based on the energy minimization principle and show it can learn single memories and their sequential relationships online. This rule is similar to the Hebbian learning algorithm and Spike-Timing Dependent Plasticity (STDP), which describe conditions under which synapses between neurons change strength. Thus, GSEMM combines the static and dynamic properties of episodic memory under a single theoretical framework and bridges neuroscience, machine learning, and artificial intelligence.

We examined multiple deep neural network (DNN) architectures for suitability in predicting neurotransmitter concentrations from labeled in vitro fast scan cyclic voltammetry (FSCV) data collected on carbon fiber electrodes. Suitability is determined by the predictive performance in the "out-of-probe" case, the response to artificially induced electrical noise, and the ability to predict when the model will be errant for a given probe. This work extends prior comparisons of time series classification models by focusing on this specific task. It extends previous applications of machine learning to FSCV task by using a much larger data set and by incorporating recent advancements in deep neural networks. The InceptionTime architecture, a deep convolutional neural network, has the best absolute predictive performance of the models tested but was more susceptible to noise. A naive multilayer perceptron architecture had the second lowest prediction error and was less affected by the artificial noise, suggesting that convolutions may not be as important for this task as one might suspect.

Media has a substantial impact on the public perception of events. A one-sided or polarizing perspective on any topic is usually described as media bias. One of the ways how bias in news articles can be introduced is by altering word choice. Biased word choices are not always obvious, nor do they exhibit high context-dependency. Hence, detecting bias is often difficult. We propose a Transformer-based deep learning architecture trained via Multi-Task Learning using six bias-related data sets to tackle the media bias detection problem. Our best-performing implementation achieves a macro $F_{1}$ of 0.776, a performance boost of 3\% compared to our baseline, outperforming existing methods. Our results indicate Multi-Task Learning as a promising alternative to improve existing baseline models in identifying slanted reporting.

Despite the recent success of multi-task learning and pre-finetuning for natural language understanding, few works have studied the effects of task families on abstractive text summarization. Task families are a form of task grouping during the pre-finetuning stage to learn common skills, such as reading comprehension. To close this gap, we analyze the influence of multi-task learning strategies using task families for the English abstractive text summarization task. We group tasks into one of three strategies, i.e., sequential, simultaneous, and continual multi-task learning, and evaluate trained models through two downstream tasks. We find that certain combinations of task families (e.g., advanced reading comprehension and natural language inference) positively impact downstream performance. Further, we find that choice and combinations of task families influence downstream performance more than the training scheme, supporting the use of task families for abstractive text summarization.

The recent success of large language models for text generation poses a severe threat to academic integrity, as plagiarists can generate realistic paraphrases indistinguishable from original work. However, the role of large autoregressive transformers in generating machine-paraphrased plagiarism and their detection is still developing in the literature. This work explores T5 and GPT-3 for machine-paraphrase generation on scientific articles from arXiv, student theses, and Wikipedia. We evaluate the detection performance of six automated solutions and one commercial plagiarism detection software and perform a human study with 105 participants regarding their detection performance and the quality of generated examples. Our results suggest that large models can rewrite text humans have difficulty identifying as machine-paraphrased (53% mean acc.). Human experts rate the quality of paraphrases generated by GPT-3 as high as original texts (clarity 4.0/5, fluency 4.2/5, coherence 3.8/5). The best-performing detection model (GPT-3) achieves a 66% F1-score in detecting paraphrases.

媒体报道对公众对事件的看法具有重大影响。尽管如此，媒体媒体经常有偏见。偏见新闻文章的一种方法是改变选择一词。通过单词选择对偏见的自动识别是具有挑战性的，这主要是由于缺乏黄金标准数据集和高环境依赖性。本文介绍了Babe，这是由训练有素的专家创建的强大而多样化的数据集，用于媒体偏见研究。我们还分析了为什么专家标签在该域中至关重要。与现有工作相比，我们的数据集提供了更好的注释质量和更高的通知者协议。它由主题和插座之间平衡的3,700个句子组成，其中包含单词和句子级别上的媒体偏见标签。基于我们的数据，我们还引入了一种自动检测新闻文章中偏见的句子的方法。我们最佳性能基于BERT的模型是在由遥远标签组成的较大语料库中进行预训练的。对我们提出的监督数据集进行微调和评估模型，我们达到了0.804的宏F1得分，表现优于现有方法。

通常需要平行语料库来使用BLEU，流星和Bertscore等指标自动评估翻译质量。尽管基于参考的评估范式被广泛用于许多机器翻译任务中，但由于这些语言遭受了语料库的不足，因此很难将其应用于使用低资源语言的翻译。往返翻译提供了一种令人鼓舞的方法来减轻平行语料库的紧急要求，尽管不幸的是，在统计机器翻译时代，没有观察到与转发翻译相关。在本文中，我们首先观察到，正向翻译质量始终与神经机器翻译范围中相应的往返翻译质量相关。然后，我们仔细分析并揭示了统计机器翻译系统上矛盾结果的原因。其次，我们提出了一种简单而有效的回归方法，以根据各种语言对的往返翻译分数（包括非常低的资源语言之间的往返翻译得分）来预测前向翻译得分的性能。我们进行了广泛的实验，以显示1,000多个语言对的预测模型的有效性和鲁棒性。最后，我们测试了有关挑战性设置的方法，例如预测分数：i）在培训中看不见的语言对，ii）在现实世界中，WMT共享任务但在新领域中。广泛的实验证明了我们方法的鲁棒性和效用。我们相信我们的工作将激发有关非常低资源的多语言机器翻译的工作。

强化学习中的固有问题是应对不确定要采取的行动（或状态价值）的政策。模型不确定性，更正式地称为认知不确定性，是指超出采样噪声的模型的预期预测误差。在本文中，我们提出了Q值函数中认知不确定性估计的度量，我们将其称为路线上的认知不确定性。我们进一步开发了一种计算其近似上限的方法，我们称之为f值。我们通过实验将后者应用于深Q-Networks（DQN），并表明增强学习中的不确定性估计是学习进步的有用指标。然后，我们提出了一种新的方法，通过从现有（以前学过的或硬编码）的甲骨文政策中学习不确定性的同时，旨在避免在训练过程中避免非生产性的随机操作，从而提高参与者批评算法的样本效率。我们认为这位评论家的信心指导了探索（CCGE）。我们使用我们的F-Value指标在软演奏者（SAC）上实施CCGE，我们将其应用于少数流行的健身环境，并表明它比有限的背景下的香草囊获得了更好的样本效率和全部情节奖励。