Acute Myeloid Leukemia (AML) is one of the most aggressive types of hematological neoplasm. To support the specialists' decision about the appropriate therapy, patients with AML receive a prognostic of outcomes according to their cytogenetic and molecular characteristics, often divided into three risk categories: favorable, intermediate, and adverse. However, the current risk classification has known problems, such as the heterogeneity between patients of the same risk group and no clear definition of the intermediate risk category. Moreover, as most patients with AML receive an intermediate-risk classification, specialists often demand other tests and analyses, leading to delayed treatment and worsening of the patient's clinical condition. This paper presents the data analysis and an explainable machine-learning model to support the decision about the most appropriate therapy protocol according to the patient's survival prediction. In addition to the prediction model being explainable, the results obtained are promising and indicate that it is possible to use it to support the specialists' decisions safely. Most importantly, the findings offered in this study have the potential to open new avenues of research toward better treatments and prognostic markers.

This paper proposes a Bayesian model to compare multiple algorithms on multiple data sets, on any metric. The model is based on the Bradley-Terry model, that counts the number of times one algorithm performs better than another on different data sets. Because of its Bayesian foundations, the Bayesian Bradley Terry model (BBT) has different characteristics than frequentist approaches to comparing multiple algorithms on multiple data sets, such as Demsar (2006) tests on mean rank, and Benavoli et al. (2016) multiple pairwise Wilcoxon tests with p-adjustment procedures. In particular, a Bayesian approach allows for more nuanced statements regarding the algorithms beyond claiming that the difference is or it is not statistically significant. Bayesian approaches also allow to define when two algorithms are equivalent for practical purposes, or the region of practical equivalence (ROPE). Different than a Bayesian signed rank comparison procedure proposed by Benavoli et al. (2017), our approach can define a ROPE for any metric, since it is based on probability statements, and not on differences of that metric. This paper also proposes a local ROPE concept, that evaluates whether a positive difference between a mean measure across some cross validation to the mean of some other algorithms is should be really seen as the first algorithm being better than the second, based on effect sizes. This local ROPE proposal is independent of a Bayesian use, and can be used in frequentist approaches based on ranks. A R package and a Python program that implements the BBT is available.

The task of Argument Mining, that is extracting argumentative sentences for a specific topic from large document sources, is an inherently difficult task for machine learning models and humans alike, as large Argument Mining datasets are rare and recognition of argumentative sentences requires expert knowledge. The task becomes even more difficult if it also involves stance detection of retrieved arguments. Given the cost and complexity of creating suitably large Argument Mining datasets, we ask whether it is necessary for acceptable performance to have datasets growing in size. Our findings show that, when using carefully composed training samples and a model pretrained on related tasks, we can reach 95% of the maximum performance while reducing the training sample size by at least 85%. This gain is consistent across three Argument Mining tasks on three different datasets. We also publish a new dataset for future benchmarking.

Hierarchies are the hidden backbones of complex systems and their analysis allows for a deeper understanding of their structure and how they evolve. We consider languages also to be complex adaptive systems with several intricate networks that capture their structure and function. Hence, we decided to analyze the hierarchical organization of historical syntactic networks to understand how syntax evolves over time. We created these networks from a corpus of German texts from the 11th to 17th centuries, focusing on the hierarchical levels of these networks. diachronically and to map them to specific communicative needs of speakers. We developed a framework to empirically track the emergence of syntactic structures diachronically, enabling us to map the communicative needs of speakers with these structures. We named these syntactic structures "syntactic communicative hierarchies." We showed that the communicative needs of speakers are the organizational force of syntax. Thus, we argue that the emergence of syntactic communicative hierarchies plays a crucial role in shaping syntax over time. This may indicate that languages evolve not only to increase the efficiency of transferring information, but also to increase our capacity, as a species, to communicate our needs with more and more sophisticated abstractions.

The age of social media has opened new opportunities for businesses. This flourishing wealth of information is outside traditional channels and frameworks of classical marketing research, including that of Marketing Mix Modeling (MMM). Textual data, in particular, poses many challenges that data analysis practitioners must tackle. Social media constitute massive, heterogeneous, and noisy document sources. Industrial data acquisition processes include some amount of ETL. However, the variability of noise in the data and the heterogeneity induced by different sources create the need for ad-hoc tools. Put otherwise, customer insight extraction in fully unsupervised, noisy contexts is an arduous task. This research addresses the challenge of fully unsupervised topic extraction in noisy, Big Data contexts. We present three approaches we built on the Variational Autoencoder framework: the Embedded Dirichlet Process, the Embedded Hierarchical Dirichlet Process, and the time-aware Dynamic Embedded Dirichlet Process. These nonparametric approaches concerning topics present the particularity of determining word embeddings and topic embeddings. These embeddings do not require transfer learning, but knowledge transfer remains possible. We test these approaches on benchmark and automotive industry-related datasets from a real-world use case. We show that our models achieve equal to better performance than state-of-the-art methods and that the field of topic modeling would benefit from improved evaluation metrics.

We study the problem of regret minimization for a single bidder in a sequence of first-price auctions where the bidder knows the item's value only if the auction is won. Our main contribution is a complete characterization, up to logarithmic factors, of the minimax regret in terms of the auction's transparency, which regulates the amount of information on competing bids disclosed by the auctioneer at the end of each auction. Our results hold under different assumptions (stochastic, adversarial, and their smoothed variants) on the environment generating the bidder's valuations and competing bids. These minimax rates reveal how the interplay between transparency and the nature of the environment affects how fast one can learn to bid optimally in first-price auctions.

Large language models (LLMs), such as GPT-3 and GPT-4, have demonstrated exceptional performance in various natural language processing tasks and have shown the ability to solve certain reasoning problems. However, their reasoning capabilities are limited and relatively shallow, despite the application of various prompting techniques. In contrast, formal logic is adept at handling complex reasoning, but translating natural language descriptions into formal logic is a challenging task that non-experts struggle with. This paper proposes a neuro-symbolic method that combines the strengths of large language models and answer set programming. Specifically, we employ an LLM to transform natural language descriptions of logic puzzles into answer set programs. We carefully design prompts for an LLM to convert natural language descriptions into answer set programs in a step by step manner. Surprisingly, with just a few in-context learning examples, LLMs can generate reasonably complex answer set programs. The majority of errors made are relatively simple and can be easily corrected by humans, thus enabling LLMs to effectively assist in the creation of answer set programs.

While large language models (LLMs), such as GPT-3, appear to be robust and general, their reasoning ability is not at a level to compete with the best models trained for specific natural language reasoning problems. In this study, we observe that a large language model can serve as a highly effective few-shot semantic parser. It can convert natural language sentences into a logical form that serves as input for answer set programs, a logic-based declarative knowledge representation formalism. The combination results in a robust and general system that can handle multiple question-answering tasks without requiring retraining for each new task. It only needs a few examples to guide the LLM's adaptation to a specific task, along with reusable ASP knowledge modules that can be applied to multiple tasks. We demonstrate that this method achieves state-of-the-art performance on several NLP benchmarks, including bAbI, StepGame, CLUTRR, and gSCAN. Additionally, it successfully tackles robot planning tasks that an LLM alone fails to solve.

Bounding box regression (BBR) has been widely used in object detection and instance segmentation, which is an important step in object localization. However, most of the existing loss functions for bounding box regression cannot be optimized when the predicted box has the same aspect ratio as the groundtruth box, but the width and height values are exactly different. In order to tackle the issues mentioned above, we fully explore the geometric features of horizontal rectangle and propose a novel bounding box similarity comparison metric MPDIoU based on minimum point distance, which contains all of the relevant factors considered in the existing loss functions, namely overlapping or non-overlapping area, central points distance, and deviation of width and height, while simplifying the calculation process. On this basis, we propose a bounding box regression loss function based on MPDIoU, called LMPDIoU . Experimental results show that the MPDIoU loss function is applied to state-of-the-art instance segmentation (e.g., YOLACT) and object detection (e.g., YOLOv7) model trained on PASCAL VOC, MS COCO, and IIIT5k outperforms existing loss functions.

Identifying and understanding implicit attitudes toward food can help efforts to mitigate social prejudice due to food's pervasive role as a marker of cultural and ethnic identity. Stereotypes about food are a form of microaggression that contribute to harmful public discourse that may in turn perpetuate prejudice toward ethnic groups and negatively impact economic outcomes for restaurants. Through careful linguistic analyses, we evaluate social theories about attitudes toward immigrant cuisine in a large-scale study of framing differences in 2.1M English language Yelp reviews of restaurants in 14 US states. Controlling for factors such as restaurant price and neighborhood racial diversity, we find that immigrant cuisines are more likely to be framed in objectifying and othering terms of authenticity (e.g., authentic, traditional), exoticism (e.g., exotic, different), and prototypicality (e.g., typical, usual), but that non-Western immigrant cuisines (e.g., Indian, Mexican) receive more othering than European cuisines (e.g., French, Italian). We further find that non-Western immigrant cuisines are framed less positively and as lower status, being evaluated in terms of affordability and hygiene. Finally, we show that reviews generated by large language models (LLMs) reproduce many of the same framing tendencies. Our results empirically corroborate social theories of taste and gastronomic stereotyping, and reveal linguistic processes by which such attitudes are reified.