在本文中，我们推出了一种新的通用依赖树木库，用于亚马逊尼亚的一种濒危语言：秘鲁在秘鲁说的Panoan语言Kakataibo。我们首先讨论实施的协作方法，事实证明，在本科生的计算语言课程的背景下创建树库有效。然后，我们描述了树库的一般细节以及针对拟议的注释实施的特定于语言的注意事项。我们最终对词性标记和句法依赖性解析进行了一些实验。我们专注于单语和转移学习设置，在这里我们研究了另一种Panoan语言资源的Shipibo-Konibo Treebos的影响。

An important class of techniques for resonant anomaly detection in high energy physics builds models that can distinguish between reference and target datasets, where only the latter has appreciable signal. Such techniques, including Classification Without Labels (CWoLa) and Simulation Assisted Likelihood-free Anomaly Detection (SALAD) rely on a single reference dataset. They cannot take advantage of commonly-available multiple datasets and thus cannot fully exploit available information. In this work, we propose generalizations of CWoLa and SALAD for settings where multiple reference datasets are available, building on weak supervision techniques. We demonstrate improved performance in a number of settings with realistic and synthetic data. As an added benefit, our generalizations enable us to provide finite-sample guarantees, improving on existing asymptotic analyses.

Development of navigation algorithms is essential for the successful deployment of robots in rapidly changing hazardous environments for which prior knowledge of configuration is often limited or unavailable. Use of traditional path-planning algorithms, which are based on localization and require detailed obstacle maps with goal locations, is not possible. In this regard, vision-based algorithms hold great promise, as visual information can be readily acquired by a robot's onboard sensors and provides a much richer source of information from which deep neural networks can extract complex patterns. Deep reinforcement learning has been used to achieve vision-based robot navigation. However, the efficacy of these algorithms in environments with dynamic obstacles and high variation in the configuration space has not been thoroughly investigated. In this paper, we employ a deep Dyna-Q learning algorithm for room evacuation and obstacle avoidance in partially observable environments based on low-resolution raw image data from an onboard camera. We explore the performance of a robotic agent in environments containing no obstacles, convex obstacles, and concave obstacles, both static and dynamic. Obstacles and the exit are initialized in random positions at the start of each episode of reinforcement learning. Overall, we show that our algorithm and training approach can generalize learning for collision-free evacuation of environments with complex obstacle configurations. It is evident that the agent can navigate to a goal location while avoiding multiple static and dynamic obstacles, and can escape from a concave obstacle while searching for and navigating to the exit.

Following the advent of immersive technologies and the increasing interest in representing interactive geometrical format, 3D Point Clouds (PC) have emerged as a promising solution and effective means to display 3D visual information. In addition to other challenges in immersive applications, objective and subjective quality assessments of compressed 3D content remain open problems and an area of research interest. Yet most of the efforts in the research area ignore the local geometrical structures between points representation. In this paper, we overcome this limitation by introducing a novel and efficient objective metric for Point Clouds Quality Assessment, by learning local intrinsic dependencies using Graph Neural Network (GNN). To evaluate the performance of our method, two well-known datasets have been used. The results demonstrate the effectiveness and reliability of our solution compared to state-of-the-art metrics.

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.

Deep generative models parametrized up to a normalizing constant (e.g. energy-based models) are difficult to train by maximizing the likelihood of the data because the likelihood and/or gradients thereof cannot be explicitly or efficiently written down. Score matching is a training method, whereby instead of fitting the likelihood $\log p(x)$ for the training data, we instead fit the score function $\nabla_x \log p(x)$ -- obviating the need to evaluate the partition function. Though this estimator is known to be consistent, its unclear whether (and when) its statistical efficiency is comparable to that of maximum likelihood -- which is known to be (asymptotically) optimal. We initiate this line of inquiry in this paper, and show a tight connection between statistical efficiency of score matching and the isoperimetric properties of the distribution being estimated -- i.e. the Poincar\'e, log-Sobolev and isoperimetric constant -- quantities which govern the mixing time of Markov processes like Langevin dynamics. Roughly, we show that the score matching estimator is statistically comparable to the maximum likelihood when the distribution has a small isoperimetric constant. Conversely, if the distribution has a large isoperimetric constant -- even for simple families of distributions like exponential families with rich enough sufficient statistics -- score matching will be substantially less efficient than maximum likelihood. We suitably formalize these results both in the finite sample regime, and in the asymptotic regime. Finally, we identify a direct parallel in the discrete setting, where we connect the statistical properties of pseudolikelihood estimation with approximate tensorization of entropy and the Glauber dynamics.

分布式机器学习实现可扩展性和计算卸载，但需要大量的通信。因此，分布式学习设置中的沟通效率是一个重要的考虑因素，尤其是当通信是无线且采用电池驱动设备时。在本文中，我们开发了一种基于审查的重球（CHB）方法，用于在服务器工作者体系结构中分布式学习。除非其本地梯度与先前传播的梯度完全不同，否则每个工人的自我审查员。 HB学习问题的显着实际优势是众所周知的，但是尚未解决降低通信的问题。 CHB充分利用HB平滑来消除报告的微小变化，并证明达到了与经典HB方法相当的线性收敛速率，以平滑和强烈凸出目标函数。 CHB的收敛保证在理论上是合理的，对于凸和非凸案。此外，我们证明，在某些情况下，至少可以消除所有通信的一半，而不会对收敛率产生任何影响。广泛的数值结果验证了CHB在合成和真实数据集（凸，非凸和非不同情况）上的通信效率。鉴于目标准确性，与现有算法相比，CHB可以显着减少通信数量，从而实现相同的精度而不减慢优化过程。

部署AI驱动的系统需要支持有效人类互动的值得信赖的模型，超出了原始预测准确性。概念瓶颈模型通过在类似人类的概念的中间级别调节分类任务来促进可信度。这使得人类干预措施可以纠正错误预测的概念以改善模型的性能。但是，现有的概念瓶颈模型无法在高任务准确性，基于概念的强大解释和对概念的有效干预措施之间找到最佳的妥协，尤其是在稀缺完整和准确的概念主管的现实情况下。为了解决这个问题，我们提出了概念嵌入模型，这是一种新型的概念瓶颈模型，它通过学习可解释的高维概念表示形式而超出了当前的准确性-VS解关性权衡。我们的实验表明，嵌入模型（1）达到更好或竞争性的任务准确性W.R.T. W.R.T.没有概念的标准神经模型，（2）提供概念表示，以捕获有意义的语义，包括其地面真相标签，（3）支持测试时间概念干预措施，其在测试准确性中的影响超过了标准概念瓶颈模型，以及（4）规模对于稀缺的完整概念监督的现实条件。

自适应多机构系统（AMAS）将机器学习问题转变为代理之间的本地合作问题。我们提出了Smapy，这是一种基于合奏的AMA用于移动性预测的实施，除合作规则外，还为其代理提供了机器学习模型。通过详细的方法，我们表明，如果将线性模型集成到合作多代理结构中，则可以在基准传输模式检测数据集上使用线性模型进行非线性分类。获得的结果表明，由于多代理方法，在非线性环境中线性模型的性能有了显着改善。