This study aims to build a pre-trained Graph Neural Network (GNN) model on molecules without human annotations or prior knowledge. Although various attempts have been proposed to overcome limitations in acquiring labeled molecules, the previous pre-training methods still rely on semantic subgraphs, i.e., functional groups. Only focusing on the functional groups could overlook the graph-level distinctions. The key challenge to build a pre-trained GNN on molecules is how to (1) generate well-distinguished graph-level representations and (2) automatically discover the functional groups without prior knowledge. To solve it, we propose a novel Subgraph-conditioned Graph Information Bottleneck, named S-CGIB, for pre-training GNNs to recognize core subgraphs (graph cores) and significant subgraphs. The main idea is that the graph cores contain compressed and sufficient information that could generate well-distinguished graph-level representations and reconstruct the input graph conditioned on significant subgraphs across molecules under the S-CGIB principle. To discover significant subgraphs without prior knowledge about functional groups, we propose generating a set of functional group candidates, i.e., ego networks, and using an attention-based interaction between the graph core and the candidates. Despite being identified from self-supervised learning, our learned subgraphs match the real-world functional groups. Extensive experiments on molecule datasets across various domains demonstrate the superiority of S-CGIB.

Matrix-product states (MPS) have proven to be a versatile ansatz for modeling quantum many-body physics. For many applications, and particularly in one-dimension, they capture relevant quantum correlations in many-body wavefunctions while remaining tractable to store and manipulate on a classical computer. This has motivated researchers to also apply the MPS ansatz to machine learning (ML) problems where capturing complex correlations in datasets is also a key requirement. Here, we develop and apply an MPS-based algorithm, MPSTime, for learning a joint probability distribution underlying an observed time-series dataset, and show how it can be used to tackle important time-series ML problems, including classification and imputation. MPSTime can efficiently learn complicated time-series probability distributions directly from data, requires only moderate maximum MPS bond dimension $\chi_{\rm max}$, with values for our applications ranging between $\chi_{\rm max} = 20-150$, and can be trained for both classification and imputation tasks under a single logarithmic loss function. Using synthetic and publicly available real-world datasets, spanning applications in medicine, energy, and astronomy, we demonstrate performance competitive with state-of-the-art ML approaches, but with the key advantage of encoding the full joint probability distribution learned from the data. By sampling from the joint probability distribution and calculating its conditional entanglement entropy, we show how its underlying structure can be uncovered and interpreted. This manuscript is supplemented with the release of a publicly available code package MPSTime that implements our approach. The efficiency of the MPS-based ansatz for learning complex correlation structures from time-series data is likely to underpin interpretable advances to challenging time-series ML problems across science, industry, and medicine.

The integration of multi-omic data is pivotal for understanding complex diseases, but its high dimensionality and noise present significant challenges. Graph Neural Networks (GNNs) offer a robust framework for analyzing large-scale signaling pathways and protein-protein interaction networks, yet they face limitations in expressivity when capturing intricate biological relationships. To address this, we propose Graph Sequence Language Model (GraphSeqLM), a framework that enhances GNNs with biological sequence embeddings generated by Large Language Models (LLMs). These embeddings encode structural and biological properties of DNA, RNA, and proteins, augmenting GNNs with enriched features for analyzing sample-specific multi-omic data. By integrating topological, sequence-derived, and biological information, GraphSeqLM demonstrates superior predictive accuracy and outperforms existing methods, paving the way for more effective multi-omic data integration in precision medicine.

Study Objectives: We investigate using Mamba-based deep learning approaches for sleep staging on signals from ANNE One (Sibel Health, Evanston, IL), a minimally intrusive dual-sensor wireless wearable system measuring chest electrocardiography (ECG), triaxial accelerometry, and temperature, as well as finger photoplethysmography (PPG) and temperature. Methods: We obtained wearable sensor recordings from 360 adults undergoing concurrent clinical polysomnography (PSG) at a tertiary care sleep lab. PSG recordings were scored according to AASM criteria. PSG and wearable sensor data were automatically aligned using their ECG channels with manual confirmation by visual inspection. We trained Mamba-based models with both convolutional-recurrent neural network (CRNN) and the recurrent neural network (RNN) architectures on these recordings. Ensembling of model variants with similar architectures was performed. Results: Our best approach, after ensembling, attains a 3-class (wake, NREM, REM) balanced accuracy of 83.50%, F1 score of 84.16%, Cohen's $\kappa$ of 72.68%, and a MCC score of 72.84%; a 4-class (wake, N1/N2, N3, REM) balanced accuracy of 74.64%, F1 score of 74.56%, Cohen's $\kappa$ of 61.63%, and MCC score of 62.04%; a 5-class (wake, N1, N2, N3, REM) balanced accuracy of 64.30%, F1 score of 66.97%, Cohen's $\kappa$ of 53.23%, MCC score of 54.38%. Conclusions: Deep learning models can infer major sleep stages from a wearable system without electroencephalography (EEG) and can be successfully applied to data from adults attending a tertiary care sleep clinic.

Elucidating the rationale behind neural models' outputs has been challenging in the machine learning field, which is indeed applicable in this age of large language models (LLMs) and in-context learning (ICL). When it comes to estimating input attributions (IA), ICL poses a new issue of interpreting which example in the prompt, consisting of a set of examples, contributed to identifying the task/rule to be solved. To this end, in this paper, we introduce synthetic diagnostic tasks inspired by the poverty of the stimulus design in inductive reasoning; here, most in-context examples are ambiguous w.r.t. their underlying rule, and one critical example disambiguates the task demonstrated. The question is whether conventional IA methods can identify such an example in interpreting the inductive reasoning process in ICL. Our experiments provide several practical findings; for example, a certain simple IA method works the best, and the larger the model, the generally harder it is to interpret the ICL with gradient-based IA methods.

When it comes to classifying child sexual abuse images, managing similar inter-class correlations and diverse intra-class correlations poses a significant challenge. Vision transformer models, unlike conventional deep convolutional network models, leverage a self-attention mechanism to capture global interactions among contextual local elements. This allows them to navigate through image patches effectively, avoiding incorrect correlations and reducing ambiguity in attention maps, thus proving their efficacy in computer vision tasks. Rather than directly analyzing child sexual abuse data, we constructed two datasets: one comprising clean and pornographic images and another with three classes, which additionally include images indicative of pornography, sourced from Reddit and Google Open Images data. In our experiments, we also employ an adult content image benchmark dataset. These datasets served as a basis for assessing the performance of vision transformer models in pornographic image classification. In our study, we conducted a comparative analysis between various popular vision transformer models and traditional pre-trained ResNet models. Furthermore, we compared them with established methods for sensitive image detection such as attention and metric learning based CNN and Bumble. The findings demonstrated that vision transformer networks surpassed the benchmark pre-trained models, showcasing their superior classification and detection capabilities in this task.

Forensic science plays a crucial role in legal investigations, and the use of advanced technologies, such as object detection based on machine learning methods, can enhance the efficiency and accuracy of forensic analysis. Human hands are unique and can leave distinct patterns, marks, or prints that can be utilized for forensic examinations. This paper compares various machine learning approaches to hand detection and presents the application results of employing the best-performing model to identify images of significant importance in forensic contexts. We fine-tune YOLOv8 and vision transformer-based object detection models on four hand image datasets, including the 11k hands dataset with our own bounding boxes annotated by a semi-automatic approach. Two YOLOv8 variants, i.e., YOLOv8 nano (YOLOv8n) and YOLOv8 extra-large (YOLOv8x), and two vision transformer variants, i.e., DEtection TRansformer (DETR) and Detection Transformers with Assignment (DETA), are employed for the experiments. Experimental results demonstrate that the YOLOv8 models outperform DETR and DETA on all datasets. The experiments also show that YOLOv8 approaches result in superior performance compared with existing hand detection methods, which were based on YOLOv3 and YOLOv4 models. Applications of our fine-tuned YOLOv8 models for identifying hand images (or frames in a video) with high forensic values produce excellent results, significantly reducing the time required by forensic experts. This implies that our approaches can be implemented effectively for real-world applications in forensics or related fields.

My godson, Michael, is a playful, energetic 15-year-old, with a deep love of Star Wars, a wry smile, and an IQ in the low 70s. His learning disabilities and autism have made his journey a hard one. His parents, like so many others, sometimes rely on screens to reduce stress and keep him occupied. They monitor the apps and websites he uses, but things are not always as they initially appear. When Michael asked them to approve installing Linky AI, a quick review didn't reveal anything alarming, just a cartoonish platform to pass the time.

It looks like a cross between a toy airplane and a drone, but this British solar-powered aircraft could be the future of aerial surveillance. PHASA-35, built by British company BAE Systems, is a 150kg solar-electric aircraft that can quietly cruise through the stratosphere for months at a time. Named after its 35-metre wingspan, the unmanned aerial vehicle (UAV) travels at a maximum height of 70,000 feet, at a leisurely speed of 55mph. Designed as a cheaper and lighter alternative to satellites, it can be used for Earth observation and surveillance, border control, communications and disaster relief. Now, BAE Systems reveals that PHASA-35 has just completed a second round of test flights into the stratosphere – the second layer of Earth's atmosphere.

Most pronouns are referring expressions, computers need to resolve what do the pronouns refer to, and there are divergences on pronoun usage across languages. Thus, dealing with these divergences and translating pronouns is a challenge in machine translation. Mentions are referring candidates of pronouns and have closer relations with pronouns compared to general tokens. We assume that extracting additional mention features can help pronoun translation. Therefore, we introduce an additional mention attention module in the decoder to pay extra attention to source mentions but not non-mention tokens. Our mention attention module not only extracts features from source mentions, but also considers target-side context which benefits pronoun translation. In addition, we also introduce two mention classifiers to train models to recognize mentions, whose outputs guide the mention attention. We conduct experiments on the WMT17 English-German translation task, and evaluate our models on general translation and pronoun translation, using BLEU, APT, and contrastive evaluation metrics. Our proposed model outperforms the baseline Transformer model in terms of APT and BLEU scores, this confirms our hypothesis that we can improve pronoun translation by paying additional attention to source mentions, and shows that our introduced additional modules do not have negative effect on the general translation quality.