The birth of human consciousness may have truly been magic. Scientists have claimed that the consumption of the fungi psilocybin, also known as'magic mushrooms,' influenced pre-human hominids' brains six million years ago. They analyzed dozens of studies involving psilocybin and consciousness, finding the fungi increased connectivity between networks in the frontal brain region associated with expressive language, decision-making and memory. These'significant neurological and psychological effects' may have been the catalase ancient ancestors to interact with each other and the environment - spurring consciousness among our species. The idea that magic mushrooms sparked the pivotal point in humans has been touted by podcaster Joe Rogan, who has referenced the'Stoned Ape Theory' on his show multiple times.

It is with great pleasure that we introduce the second edition of the Communications of the ACM Latin American Regional Special Section. In this edition, we use this opportunity to showcase some of the region's most interesting as well as impactful advancements in computer science. Latin America is a highly heterogeneous continent with great diversity in culture, geography, demography, ethnicities, languages, and science and technology. When it comes to computer science, Latin American researchers have made significant contributions to multiple areas, such as software engineering, databases, networking and distributed systems, artificial intelligence, computer theory, and computer science education. In this Regional Special Section, we present only a small portion of the work researchers in Latin America are currently conducting. To create this edition, we put out a general call for contributions, receiving submissions from all regions of the continent.

Dr. Steven Pinker, a Harvard psychologist and prolific author, has often been described as a cheerleader for science, reason, and humanism. He is often maligned by his critics as a defender of the status quo. Much of his research focuses on slow and steady incremental improvements that have defined rapid human development, both in the United States and globally, over the past century. His 2018 book, "Enlightenment Now" was famously cited by Bill Gates as "his new favorite book," and became a focal point for global policymakers. He is a fierce defender of liberalism, democracy, and market economies, and believes a variety of forces are conspiring against them: populism of both the right and left, religious fundamentalism, and political correctness, among others. He also has emerged as a champion of reasoned, civil debate on college campuses, pushing back against cancel culture, and what he views as a'political monoculture' in academia.

Northern Ireland's rising prison population means an extra 75 Prison Service staff will have to be recruited at a cost of 3.5m, Justice Minister Naomi Long has announced. A disused cell block at Maghaberry is also being prepared for re-opening as part of contingency planning. The jail currently has 1,245 inmates – almost half of them are on remand, meaning they have not been convicted or sentenced. Mrs Long said the situation is challenging.PA MediaJustice minister Naomi Long says there has been a steep rise in prisoner numbers in recent years Northern Ireland has three prison sites: Maghaberry, Magilligan and Hydebank Wood, which houses women prisoners and young offenders. Over the last three years, inmate numbers across the sites have increased by 500 to 1,900.

Automatic speech translation (AST) modeling is usually plagued by lack of parallel training data, which limits the success of end-to-end models. Owing to their modular architecture, cascade models for AST have the advantage of leveraging the large amounts of data available to build automatic speech recognition (ASR) and machine translation (MT) models, respectively. The straightforward way of building cascade AST models is to send the 1-best ASR transcription to the text MT model. Yet another advantage of such an architecture is that it is in fact a multi-modal and multi-task one: besides speech, it also accepts text input for translation and it produces ASR output either in stand-alone mode or as a side-product of the AST task. This multi-input/modal view on the AST task is firmly anchored in the reality of practical applications, so we take it as a fundamental design choice: we aim to build a model that delivers both state of the art ASR and MT performance, while optimizing the AST performance within these constraints. Translating ASR 1-best output has the obvious disadvantage that any further training (fine-tuning) on AST parallel data specific to a given domain is unable to back-propagate cross-entropy loss gradient through the interface between the ASR and the MT model. For tighter coupling between ASR and MT modules we follow the approach of (Dalmia et al., 2021) that leverages the 1-best ASR alignment and sends the ASR encoder embeddings aligned with the 1-best ASR sequence to the MT model. This results in a cascade architecture that allows back-propagation gradient to flow from the MT model into the ASR components. The ASR model in our work uses a conformer encoder architecture (Gulati et al., 2020), pre-trained on a large amount of speech data as described in the Unified Speech Model (USM) work (Zhang et al., 2023).

With the escalating demand for meal delivery services, this study delves into the Meal Delivery Routing Problem (MDRP) within the context of last-mile logis-tics. Focusing on the critical aspects of courier allocation and order fulfillment, we introduce a novel approach utilizing a GRASP metaheuristic. The algorithm optimizes the assignment of couriers to orders, considering dynamic factors such as courier availability, order demands, and geographical locations. Real-world in-stances from a Colombian delivery app form the basis of our computational anal-ysis. Calibration of GRASP parameters reveals a delicate trade-off between solu-tion quality and computational time. Comparative results with a simulation-optimization based study underscore GRASP's competitive performance, demon-strating strengths in fulfilling orders and routing efficiency across diverse in-stances. This research enhances operational efficiency in the burgeoning food de-livery industry, shedding light on practical algorithms for last-mile logistics opti-mization.

Large-scale pre-training methodologies for chemical language models represent a breakthrough in cheminformatics. These methods excel in tasks such as property prediction and molecule generation by learning contextualized representations of input tokens through self-supervised learning on large unlabeled corpora. Typically, this involves pre-training on unlabeled data followed by fine-tuning on specific tasks, reducing dependence on annotated datasets and broadening chemical language representation understanding. This paper introduces a large encoder-decoder chemical foundation models pre-trained on a curated dataset of 91 million SMILES samples sourced from PubChem, which is equivalent to 4 billion of molecular tokens. The proposed foundation model supports different complex tasks, including quantum property prediction, and offer flexibility with two main variants (289M and $8\times289M$). Our experiments across multiple benchmark datasets validate the capacity of the proposed model in providing state-of-the-art results for different tasks. We also provide a preliminary assessment of the compositionality of the embedding space as a prerequisite for the reasoning tasks. We demonstrate that the produced latent space is separable compared to the state-of-the-art with few-shot learning capabilities.

This is the first work to investigate the effectiveness of BERT-based contextual embeddings in active learning (AL) tasks on cold-start scenarios, where traditional fine-tuning is infeasible due to the absence of labeled data. Our primary contribution is the proposal of a more robust fine-tuning pipeline - DoTCAL - that diminishes the reliance on labeled data in AL using two steps: (1) fully leveraging unlabeled data through domain adaptation of the embeddings via masked language modeling and (2) further adjusting model weights using labeled data selected by AL. Our evaluation contrasts BERT-based embeddings with other prevalent text representation paradigms, including Bag of Words (BoW), Latent Semantic Indexing (LSI), and FastText, at two critical stages of the AL process: instance selection and classification. Experiments conducted on eight ATC benchmarks with varying AL budgets (number of labeled instances) and number of instances (about 5,000 to 300,000) demonstrate DoTCAL's superior effectiveness, achieving up to a 33% improvement in Macro-F1 while reducing labeling efforts by half compared to the traditional one-step method. We also found that in several tasks, BoW and LSI (due to information aggregation) produce results superior (up to 59% ) to BERT, especially in low-budget scenarios and hard-to-classify tasks, which is quite surprising.

In this paper, we experimented with the SpeechT5 model pre-trained on large-scale datasets. We pre-trained the foundation model from scratch and fine-tuned it on a large-scale robust multi-speaker text-to-speech (TTS) task. We tested the model capabilities in a zero- and few-shot scenario. Based on two listening tests, we evaluated the synthetic audio quality and the similarity of how synthetic voices resemble real voices. Our results showed that the SpeechT5 model can generate a synthetic voice for any speaker using only one minute of the target speaker's data. We successfully demonstrated the high quality and similarity of our synthetic voices on publicly known Czech politicians and celebrities.

Extracting social determinants of health (SDoH) from unstructured medical notes depends heavily on labor-intensive annotations, which are typically task-specific, hampering reusability and limiting sharing. In this study we introduced SDoH-GPT, a simple and effective few-shot Large Language Model (LLM) method leveraging contrastive examples and concise instructions to extract SDoH without relying on extensive medical annotations or costly human intervention. It achieved tenfold and twentyfold reductions in time and cost respectively, and superior consistency with human annotators measured by Cohen's kappa of up to 0.92. The innovative combination of SDoH-GPT and XGBoost leverages the strengths of both, ensuring high accuracy and computational efficiency while consistently maintaining 0.90+ AUROC scores. Testing across three distinct datasets has confirmed its robustness and accuracy. This study highlights the potential of leveraging LLMs to revolutionize medical note classification, demonstrating their capability to achieve highly accurate classifications with significantly reduced time and cost.