Chronic pain is a multi-dimensional experience, and pain intensity plays an important part, impacting the patients emotional balance, psychology, and behaviour. Standard self-reporting tools, such as the Visual Analogue Scale for pain, fail to capture this burden. Moreover, this type of tools is susceptible to a degree of subjectivity, dependent on the patients clear understanding of how to use it, social biases, and their ability to translate a complex experience to a scale. To overcome these and other self-reporting challenges, pain intensity estimation has been previously studied based on facial expressions, electroencephalograms, brain imaging, and autonomic features. However, to the best of our knowledge, it has never been attempted to base this estimation on the patient narratives of the personal experience of chronic pain, which is what we propose in this work. Indeed, in the clinical assessment and management of chronic pain, verbal communication is essential to convey information to physicians that would otherwise not be easily accessible through standard reporting tools, since language, sociocultural, and psychosocial variables are intertwined. We show that language features from patient narratives indeed convey information relevant for pain intensity estimation, and that our computational models can take advantage of that. Specifically, our results show that patients with mild pain focus more on the use of verbs, whilst moderate and severe pain patients focus on adverbs, and nouns and adjectives, respectively, and that these differences allow for the distinction between these three pain classes.

慢性疼痛被认为是一个重大的健康问题，不仅受到经济，而且在社会和个人层面的影响。作为私人和主观的经验，它不可能从外部和公正地体验，描述和解释慢性疼痛，作为纯粹的有害刺激，直接指向因果症，并促进其缓解，与急性疼痛相反，对其进行评估通常是直截了当的。因此，口头沟通是将相关信息传达给卫生专业人员的关键，否则外部实体无法访问，即关于痛苦经验和患者的内在质量。我们提出并讨论了一个主题建模方法，以识别慢性疼痛的口头描述中的模式，并使用这些模式量化和限定疼痛的经验。我们的方法允许提取关于所获得的主题模型和潜在空间的慢性疼痛经验的新洞察。我们认为我们的结果在临床上与慢性疼痛的评估和管理有关。

目的：验证自然语言处理（NLP）技术的适用性，透露和量化，通过慢性疼痛（RRCP）数据集的新型Reddit报告，致力于慢性疼痛（RRCP）DataSet的报告，旨在成为未来研究的标准在这个欠发达地区。方法：定义和验证与慢性疼痛有关的一组病理学的RRCP数据集。对于每种病理学，确定慢性疼痛经历的主要品质。比较每种病理学的确定质量并验证临床研究。结果：RRCP数据集包含来自与慢性疼痛相关的12个底板的136,573 reddit提交。宏观分析表明，影响相同或相似的身体部位的病理结果导致语义上的疼痛描述。详细的分析表明，在给定的病理学中，存在慢性疼痛的素质，这些病理学的慢性疼痛是从另一个病理学中经历它，以及一些慢性疼痛的各种经验都是共同的。这些使我们能够比较慢性疼痛的主观经验（例如，对于RRCP人群，体验关节炎与在各种质量或疑虑中经历紧张的脊柱炎，同时经历纤维肌痛而包括相同的品质和其他两个病态的特质）。结论：我们对慢性疼痛描述的无监督语义分析反映了关于不同病理在慢性疼痛体验方面如何显现的临床知识。我们的结果验证了使用NLP技术从慢性疼痛经验的描述中自动提取和量化临床相关信息。

Some recent pieces of work in the Machine Learning (ML) literature have demonstrated the usefulness of assessing which observations are hardest to have their label predicted accurately. By identifying such instances, one may inspect whether they have any quality issues that should be addressed. Learning strategies based on the difficulty level of the observations can also be devised. This paper presents a set of meta-features that aim at characterizing which instances of a dataset are hardest to have their label predicted accurately and why they are so, aka instance hardness measures. Both classification and regression problems are considered. Synthetic datasets with different levels of complexity are built and analyzed. A Python package containing all implementations is also provided.

Learning efficient and interpretable policies has been a challenging task in reinforcement learning (RL), particularly in the visual RL setting with complex scenes. While neural networks have achieved competitive performance, the resulting policies are often over-parameterized black boxes that are difficult to interpret and deploy efficiently. More recent symbolic RL frameworks have shown that high-level domain-specific programming logic can be designed to handle both policy learning and symbolic planning. However, these approaches rely on coded primitives with little feature learning, and when applied to high-dimensional visual scenes, they can suffer from scalability issues and perform poorly when images have complex object interactions. To address these challenges, we propose \textit{Differentiable Symbolic Expression Search} (DiffSES), a novel symbolic learning approach that discovers discrete symbolic policies using partially differentiable optimization. By using object-level abstractions instead of raw pixel-level inputs, DiffSES is able to leverage the simplicity and scalability advantages of symbolic expressions, while also incorporating the strengths of neural networks for feature learning and optimization. Our experiments demonstrate that DiffSES is able to generate symbolic policies that are simpler and more and scalable than state-of-the-art symbolic RL methods, with a reduced amount of symbolic prior knowledge.

Rigorous guarantees about the performance of predictive algorithms are necessary in order to ensure their responsible use. Previous work has largely focused on bounding the expected loss of a predictor, but this is not sufficient in many risk-sensitive applications where the distribution of errors is important. In this work, we propose a flexible framework to produce a family of bounds on quantiles of the loss distribution incurred by a predictor. Our method takes advantage of the order statistics of the observed loss values rather than relying on the sample mean alone. We show that a quantile is an informative way of quantifying predictive performance, and that our framework applies to a variety of quantile-based metrics, each targeting important subsets of the data distribution. We analyze the theoretical properties of our proposed method and demonstrate its ability to rigorously control loss quantiles on several real-world datasets.

Biometrics is the science of identifying an individual based on their intrinsic anatomical or behavioural characteristics, such as fingerprints, face, iris, gait, and voice. Iris recognition is one of the most successful methods because it exploits the rich texture of the human iris, which is unique even for twins and does not degrade with age. Modern approaches to iris recognition utilize deep learning to segment the valid portion of the iris from the rest of the eye, so it can then be encoded, stored and compared. This paper aims to improve the accuracy of iris semantic segmentation systems by introducing a novel data augmentation technique. Our method can transform an iris image with a certain dilation level into any desired dilation level, thus augmenting the variability and number of training examples from a small dataset. The proposed method is fast and does not require training. The results indicate that our data augmentation method can improve segmentation accuracy up to 15% for images with high pupil dilation, which creates a more reliable iris recognition pipeline, even under extreme dilation.

In 2019 Kerdels and Peters proposed a grid cell model (GCM) based on a Differential Growing Neural Gas (DGNG) network architecture as a computationally efficient way to model an Autoassociative Memory Cell (AMC) \cite{Kerdels_Peters_2019}. An important feature of the DGNG architecture with respect to possible applications in the field of computational neuroscience is its \textit{capacity} refering to its capability to process and uniquely distinguish input signals and therefore obtain a valid representation of the input space. This study evaluates the capacity of a two layered DGNG grid cell model on the Fashion-MNIST dataset. The focus on the study lies on the variation of layer sizes to improve the understanding of capacity properties in relation to network parameters as well as its scaling properties. Additionally, parameter discussions and a plausability check with a pixel/segment variation method are provided. It is concluded, that the DGNG model is able to obtain a meaningful and plausible representation of the input space and to cope with the complexity of the Fashion-MNIST dataset even at moderate layer sizes.

Much of the information of breathing is contained within the photoplethysmography (PPG) signal, through changes in venous blood flow, heart rate and stroke volume. We aim to leverage this fact, by employing a novel deep learning framework which is a based on a repurposed convolutional autoencoder. Our model aims to encode all of the relevant respiratory information contained within photoplethysmography waveform, and decode it into a waveform that is similar to a gold standard respiratory reference. The model is employed on two photoplethysmography data sets, namely Capnobase and BIDMC. We show that the model is capable of producing respiratory waveforms that approach the gold standard, while in turn producing state of the art respiratory rate estimates. We also show that when it comes to capturing more advanced respiratory waveform characteristics such as duty cycle, our model is for the most part unsuccessful. A suggested reason for this, in light of a previous study on in-ear PPG, is that the respiratory variations in finger-PPG are far weaker compared with other recording locations. Importantly, our model can perform these waveform estimates in a fraction of a millisecond, giving it the capacity to produce over 6 hours of respiratory waveforms in a single second. Moreover, we attempt to interpret the behaviour of the kernel weights within the model, showing that in part our model intuitively selects different breathing frequencies. The model proposed in this work could help to improve the usefulness of consumer PPG-based wearables for medical applications, where detailed respiratory information is required.

Recently, there has been an interest in improving the resources available in Intrusion Detection System (IDS) techniques. In this sense, several studies related to cybersecurity show that the environment invasions and information kidnapping are increasingly recurrent and complex. The criticality of the business involving operations in an environment using computing resources does not allow the vulnerability of the information. Cybersecurity has taken on a dimension within the universe of indispensable technology in corporations, and the prevention of risks of invasions into the environment is dealt with daily by Security teams. Thus, the main objective of the study was to investigate the Ensemble Learning technique using the Stacking method, supported by the Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) algorithms aiming at an optimization of the results for DDoS attack detection. For this, the Intrusion Detection System concept was used with the application of the Data Mining and Machine Learning Orange tool to obtain better results