Computational approaches to predicting mental health conditions in social media have been substantially explored in the past years. Multiple surveys have been published on this topic, providing the community with comprehensive accounts of the research in this area. Among all mental health conditions, depression is the most widely studied due to its worldwide prevalence. The COVID-19 global pandemic, starting in early 2020, has had a great impact on mental health worldwide. Harsh measures employed by governments to slow the spread of the virus (e.g., lockdowns) and the subsequent economic downturn experienced in many countries have significantly impacted people's lives and mental health. Studies have shown a substantial increase of above 50% in the rate of depression in the population. In this context, we present a survey on natural language processing (NLP) approaches to modeling depression in social media, providing the reader with a post-COVID-19 outlook. This survey contributes to the understanding of the impacts of the pandemic on modeling depression in social media. We outline how state-of-the-art approaches and new datasets have been used in the context of the COVID-19 pandemic. Finally, we also discuss ethical issues in collecting and processing mental health data, considering fairness, accountability, and ethics.

Zero-shot imitation learning algorithms hold the promise of reproducing unseen behavior from as little as a single demonstration at test time. Existing practical approaches view the expert demonstration as a sequence of goals, enabling imitation with a high-level goal selector, and a low-level goal-conditioned policy. However, this framework can suffer from myopic behavior: the agent's immediate actions towards achieving individual goals may undermine long-term objectives. We introduce a novel method that mitigates this issue by directly optimizing the occupancy matching objective that is intrinsic to imitation learning. We propose to lift a goal-conditioned value function to a distance between occupancies, which are in turn approximated via a learned world model. The resulting method can learn from offline, suboptimal data, and is capable of non-myopic, zero-shot imitation, as we demonstrate in complex, continuous benchmarks.

This paper presents the foundational framework and initial findings of the Balanced Arabic Readability Evaluation Corpus (BAREC) project, designed to address the need for comprehensive Arabic language resources aligned with diverse readability levels. Inspired by the Taha/Arabi21 readability reference, BAREC aims to provide a standardized reference for assessing sentence-level Arabic text readability across 19 distinct levels, ranging in targets from kindergarten to postgraduate comprehension. Our ultimate goal with BAREC is to create a comprehensive and balanced corpus that represents a wide range of genres, topics, and regional variations through a multifaceted approach combining manual annotation with AI-driven tools. This paper focuses on our meticulous annotation guidelines, demonstrated through the analysis of 10,631 sentences/phrases (113,651 words). The average pairwise inter-annotator agreement, measured by Quadratic Weighted Kappa, is 79.9%, reflecting a high level of substantial agreement. We also report competitive results for benchmarking automatic readability assessment. We will make the BAREC corpus and guidelines openly accessible to support Arabic language research and education.

Downsampling or under-sampling is a technique that is utilized in the context of large and highly imbalanced classification models. We study optimal downsampling for imbalanced classification using generalized linear models (GLMs). We propose a pseudo maximum likelihood estimator and study its asymptotic normality in the context of increasingly imbalanced populations relative to an increasingly large sample size. We provide theoretical guarantees for the introduced estimator. Additionally, we compute the optimal downsampling rate using a criterion that balances statistical accuracy and computational efficiency. Our numerical experiments, conducted on both synthetic and empirical data, further validate our theoretical results, and demonstrate that the introduced estimator outperforms commonly available alternatives.

A recent line of work in mechanistic interpretability has focused on reverse-engineering the computation performed by neural networks trained on the binary operation of finite groups. We investigate the internals of one-hidden-layer neural networks trained on this task, revealing previously unidentified structure and producing a more complete description of such models that unifies the explanations of previous works. Notably, these models approximate equivariance in each input argument. We verify that our explanation applies to a large fraction of networks trained on this task by translating it into a compact proof of model performance, a quantitative evaluation of model understanding. In particular, our explanation yields a guarantee of model accuracy that runs in 30% the time of brute force and gives a >=95% accuracy bound for 45% of the models we trained. We were unable to obtain nontrivial non-vacuous accuracy bounds using only explanations from previous works.

The Israeli military "repeatedly" fired at UNIFIL headquarters and positions in southern Lebanon, injuring two members of the peacekeeping force, the United Nations says, as Israel presses on with its assault on Hezbollah. UNIFIL – the UN Interim Force in Lebanon – said on Thursday that two of its peacekeepers were injured after an Israeli tank "fired its weapon" at a guard tower at the group's headquarters, located in the border area town of Naqoura. The attack on the tower had caused the two peacekeepers to fall. "The injuries are fortunately, this time, not serious, but they remain in hospital," said UNIFIL in a statement. The Israeli soldiers also fired on a UN position – named "1-31"- in the village of Labbouneh, "hitting the entrance to the bunker where peacekeepers were sheltering, and damaging vehicles and a communications system", it said. The peacekeeping force reported that it had observed an Israeli military drone flying inside the UN position up to the bunker entrance.

In lifelong learning systems based on artificial neural networks, one of the biggest obstacles is the inability to retain old knowledge as new information is encountered. This phenomenon is known as catastrophic forgetting. In this paper, we propose a new kind of connectionist architecture, the Sequential Neural Coding Network, that is robust to forgetting when learning from streams of data points and, unlike networks of today, does not learn via the popular back-propagation of errors. Grounded in the neurocognitive theory of predictive coding, our model adapts its synapses in a biologically-plausible fashion while another neural system learns to direct and control this cortex-like structure, mimicking some of the task-executive control functionality of the basal ganglia. In our experiments, we demonstrate that our self-organizing system experiences significantly less forgetting compared to standard neural models, outperforming a swath of previously proposed methods, including rehearsal/data buffer-based methods, on both standard (SplitMNIST, Split Fashion MNIST, etc.) and custom benchmarks even though it is trained in a stream-like fashion.

The claims could have been taken word-for-word from any one of numerous US far-right websites in recent months. "Reports are surfacing suggesting that [lawmakers] may have been involved in transporting large numbers of refugees and immigration applicants to polling stations to secure votes for individual candidates," the author of the article claimed. This wasn't a conspiracist asserting that Honduran migrants are being imported into the US to replace swing-state Republican voters, though; the claim came from a website called The Irish Channel. A new report published on Tuesday by researchers at the Institute for Strategic Dialogue outlines how the website has used generative AI to create articles that have been "heavily influenced by similar election denial efforts in the US." The anti-immigrant narrative, based on made-up quotes and fabricated academics, is just one of the conspiracies imported wholesale into Ireland from the US in recent months.

The research was partially supported by the Paul and Marcia Wythes Center on Contemporary China and Office of Population Research at Princeton University. We are grateful to Wen Liu, Gou Wu, and Dean Minello for their excellent research assistance. The ideas expressed herein are those of the authors. Abstract Liking it or not, ready or not, we are likely to enter a new phase of human history in which Artificial Intelligence (AI) will dominate economic production and social life - the AI Revolution. Before the actual arrival of the AI Revolution, it is time for us to speculate on how AI will impact the social world. In this article, we focus on the social impact of generative LLMbased AI (GELLMAI), discussing societal factors that contribute to its technological development and its potential roles in enhancing both between-country and within-country social inequality. There are good indications that the US and China will lead the field and will be the main competitors for domination of AI in the world. We conjecture the AI Revolution will likely give rise to a post-knowledge society in which knowledge per se will become less important than in today's world. Instead, individual relationships and social identity will become more important. With the advent of Generative Large Language Model (LLM)-based Artificial Intelligence (AI) tools such as ChatGPT from OpenAI and Bard from Google, it is natural to wonder about the social impact of this technology. In the remainder of this paper, we will refer to generative LLMbased AI simply as GELLMAI. The main objective of this paper is to explore, tentatively, the social impact of GELLMAI. While the question about the social impact of GELLMAI is undoubtedly important, any answers must be tentative and speculative at this point. We are still in the early stages of GELLMAI and may need to wait years, perhaps even decades, to fully understand its social implications. However, drawing from our experiences with past technologies in history, our current understanding of GELLMAI, empirical knowledge about the social world, and sociological reasoning, we can engage in preliminary and speculative discussions. We offer our account below. We believe that the social impact of GELLMAI is enormous, with the potential to revolutionize not only the production of goods and services but also to fundamentally alter the organization of human societies and the nature of daily life.

Deep learning has proven to be a highly effective tool for a wide range of applications, significantly when leveraging the power of multi-loss functions to optimize performance on multiple criteria simultaneously. However, optimal selection and weighting loss functions in deep learning tasks can significantly influence model performance, yet manual tuning of these functions is often inefficient and inflexible. We propose a novel framework called dynamic memory fusion for adaptive multi-loss function penalizing in real-time to address this. This framework leverages historical loss values data to dynamically adjust the weighting of multiple loss functions throughout the training process. Additionally, this framework integrates an auxiliary loss function to enhance model performance in the early stages. To further research horizons, we introduce the class-balanced dice loss function, designed to address class imbalance by prioritizing underrepresented classes. Experiments on breast ultrasound datasets demonstrate that the framework improves segmentation performance across various metrics. These results demonstrate the effectiveness of our proposed framework in ensuring that the model dynamically adjusts its focus to prioritize the most relevant criteria, leading to improved performance in evolving environments. The source code for our proposed methodology is publicly available on GitHub.