We study mean-field variational Bayesian inference using the TAP approach, for Z2-synchronization as a prototypical example of a high-dimensional Bayesian model. We show that for any signal strength $\lambda > 1$ (the weak-recovery threshold), there exists a unique local minimizer of the TAP free energy functional near the mean of the Bayes posterior law. Furthermore, the TAP free energy in a local neighborhood of this minimizer is strongly convex. Consequently, a natural-gradient/mirror-descent algorithm achieves linear convergence to this minimizer from a local initialization, which may be obtained by a constant number of iterates of Approximate Message Passing (AMP). This provides a rigorous foundation for variational inference in high dimensions via minimization of the TAP free energy. We also analyze the finite-sample convergence of AMP, showing that AMP is asymptotically stable at the TAP minimizer for any $\lambda > 1$, and is linearly convergent to this minimizer from a spectral initialization for sufficiently large $\lambda$. Such a guarantee is stronger than results obtainable by state evolution analyses, which only describe a fixed number of AMP iterations in the infinite-sample limit. Our proofs combine the Kac-Rice formula and Sudakov-Fernique Gaussian comparison inequality to analyze the complexity of critical points that satisfy strong convexity and stability conditions within their local neighborhoods.

Archaeology has long faced fundamental issues of sampling and scalar representation. Traditionally, the local-to-regional-scale views of settlement patterns are produced through systematic pedestrian surveys. Recently, systematic manual survey of satellite and aerial imagery has enabled continuous distributional views of archaeological phenomena at interregional scales. However, such 'brute force' manual imagery survey methods are both time- and labor-intensive, as well as prone to inter-observer differences in sensitivity and specificity. The development of self-supervised learning methods offers a scalable learning scheme for locating archaeological features using unlabeled satellite and historical aerial images. However, archaeological features are generally only visible in a very small proportion relative to the landscape, while the modern contrastive-supervised learning approach typically yields an inferior performance on highly imbalanced datasets. In this work, we propose a framework to address this long-tail problem. As opposed to the existing contrastive learning approaches that treat the labelled and unlabeled data separately, our proposed method reforms the learning paradigm under a semi-supervised setting in order to utilize the precious annotated data (<7% in our setting). Specifically, the highly unbalanced nature of the data is employed as the prior knowledge in order to form pseudo negative pairs by ranking the similarities between unannotated image patches and annotated anchor images. In this study, we used 95,358 unlabeled images and 5,830 labelled images in order to solve the issues associated with detecting ancient buildings from a long-tailed satellite image dataset. From the results, our semi-supervised contrastive learning model achieved a promising testing balanced accuracy of 79.0%, which is a 3.8% improvement as compared to other state-of-the-art approaches.

Time elapsed till an event of interest is often modeled using the survival analysis methodology, which estimates a survival score based on the input features. There is a resurgence of interest in developing more accurate prediction models for time-to-event prediction in personalized healthcare using modern tools such as neural networks. Higher quality features and more frequent observations improve the predictions for a patient, however, the impact of including a patient's geographic location-based public health statistics on individual predictions has not been studied. This paper proposes a complementary improvement to survival analysis models by incorporating public health statistics in the input features. We show that including geographic location-based public health information results in a statistically significant improvement in the concordance index evaluated on the Surveillance, Epidemiology, and End Results (SEER) dataset containing nationwide cancer incidence data. The improvement holds for both the standard Cox proportional hazards model and the state-of-the-art Deep Survival Machines model. Our results indicate the utility of geographic location-based public health features in survival analysis.

The article aims to analyze the performance of ChatGPT, a large language model developed by OpenAI, in the context of cardiology and vascular pathologies. The study evaluated the accuracy of ChatGPT in answering challenging multiple-choice questions (QCM) using a dataset of 190 questions from the Siamois-QCM platform. The goal was to assess ChatGPT potential as a valuable tool in medical education compared to two well-ranked students of medicine. The results showed that ChatGPT outperformed the students, scoring 175 out of 190 correct answers with a percentage of 92.10\%, while the two students achieved scores of 163 and 159 with percentages of 85.78\% and 82.63\%, respectively. These results showcase how ChatGPT has the potential to be highly effective in the fields of cardiology and vascular pathologies by providing accurate answers to relevant questions.

Artificial Intelligence (AI) has huge impact on our daily lives with applications such as voice assistants, facial recognition, chatbots, autonomously driving cars, etc. Natural Language Processing (NLP) is a cross-discipline of AI and Linguistics, dedicated to study the understanding of the text. This is a very challenging area due to unstructured nature of the language, with many ambiguous and corner cases. In this thesis we address a very specific area of NLP that involves the understanding of entities (e.g., names of people, organizations, locations) in text. First, we introduce a radically different, entity-centric view of the information in text. We argue that instead of using individual mentions in text to understand their meaning, we should build applications that would work in terms of entity concepts. Next, we present a more detailed model on how the entity-centric approach can be used for the entity linking task. In our work, we show that this task can be improved by considering performing entity linking at the coreference cluster level rather than each of the mentions individually. In our next work, we further study how information from Knowledge Base entities can be integrated into text. Finally, we analyze the evolution of the entities from the evolving temporal perspective.

In this paper we introduce the Temporo-Spatial Vision Transformer (TSViT), a fully-attentional model for general Satellite Image Time Series (SITS) processing based on the Vision Transformer (ViT). TSViT splits a SITS record into non-overlapping patches in space and time which are tokenized and subsequently processed by a factorized temporo-spatial encoder. We argue, that in contrast to natural images, a temporal-then-spatial factorization is more intuitive for SITS processing and present experimental evidence for this claim. Additionally, we enhance the model's discriminative power by introducing two novel mechanisms for acquisition-time-specific temporal positional encodings and multiple learnable class tokens. The effect of all novel design choices is evaluated through an extensive ablation study. Our proposed architecture achieves state-of-the-art performance, surpassing previous approaches by a significant margin in three publicly available SITS semantic segmentation and classification datasets. All model, training and evaluation codes are made publicly available to facilitate further research.

Through The advent of instruction-following large language this process, we collect over 8,000 instruction models (LLMs), representative by Chat-data for supervised fine-tuning. Our model builds GPT(OpenAI, 2022), has generated significant interest upon the open-source LLaMa-7B base model, integrates due to their exceptional performance in understanding structured and unstructured medical knowledge instructions and generating human-like from the Chinese medical knowledge graph responses. Compared to smaller models, LLMs (CMeKG), and employs knowledge-based instruction exhibit strong generalization across various natural data for fine-tuning.

Rescue missions in mountain environments are hardly achievable by standard legged robots-because of the high slopes-or by flying robots-because of limited payload capacity. We present a concept for a rope-aided climbing robot which can negotiate up-to-vertical slopes and carry heavy payloads. The robot is attached to the mountain through a rope, and it is equipped with a leg to push against the mountain and initiate jumping maneuvers. Between jumps, a hoist is used to wind/unwind the rope to move vertically and affect the lateral motion. This simple (yet effective) two-fold actuation allows the system to achieve high safety and energy efficiency. Indeed, the rope prevents the robot from falling while compensating for most of its weight, drastically reducing the effort required by the leg actuator. We also present an optimal control strategy to generate point-to-point trajectories overcoming an obstacle. We achieve fast computation time (<1 s) thanks to the use of a custom simplified robot model. We validated the generated optimal movements in Gazebo simulations with a complete robot model with a < 5% error on a 16 m long jump, showing the effectiveness of the proposed approach, and confirming the interest of our concept. Finally, we performed a reachability analysis showing that the region of achievable targets is strongly affected by the friction properties of the foot-wall contact.

Current causal text mining datasets vary in objectives, data coverage, and annotation schemes. These inconsistent efforts prevent modeling capabilities and fair comparisons of model performance. Furthermore, few datasets include cause-effect span annotations, which are needed for end-to-end causal relation extraction. To address these issues, we propose UniCausal, a unified benchmark for causal text mining across three tasks: (I) Causal Sequence Classification, (II) Cause-Effect Span Detection and (III) Causal Pair Classification. We consolidated and aligned annotations of six high quality, mainly human-annotated, corpora, resulting in a total of 58,720, 12,144 and 69,165 examples for each task respectively. Since the definition of causality can be subjective, our framework was designed to allow researchers to work on some or all datasets and tasks. To create an initial benchmark, we fine-tuned BERT pre-trained language models to each task, achieving 70.10% Binary F1, 52.42% Macro F1, and 84.68% Binary F1 scores respectively.

Multilingual pre-training significantly improves many multilingual NLP tasks, including machine translation. Most existing methods are based on some variants of masked language modeling and text-denoising objectives on monolingual data. Multilingual pre-training on monolingual data ignores the availability of parallel data in many language pairs. Also, some other works integrate the available human-generated parallel translation data in their pre-training. This kind of parallel data is definitely helpful, but it is limited even in high-resource language pairs. This paper introduces a novel semi-supervised method, SPDG, that generates high-quality pseudo-parallel data for multilingual pre-training. First, a denoising model is pre-trained on monolingual data to reorder, add, remove, and substitute words, enhancing the pre-training documents' quality. Then, we generate different pseudo-translations for each pre-training document using dictionaries for word-by-word translation and applying the pre-trained denoising model. The resulting pseudo-parallel data is then used to pre-train our multilingual sequence-to-sequence model, PEACH. Our experiments show that PEACH outperforms existing approaches used in training mT5 and mBART on various translation tasks, including supervised, zero- and few-shot scenarios. Moreover, PEACH's ability to transfer knowledge between similar languages makes it particularly useful for low-resource languages. Our results demonstrate that with high-quality dictionaries for generating accurate pseudo-parallel, PEACH can be valuable for low-resource languages.