In this section, we formalize and substantiate the claims of Theorem 1 . Theorem 1 has three parts, which we address in the following sections. First, in Section A.2, we show that the classifier makes progress during the early-learning phase: over the first We prove this rigorously in Section A.3, which shows that the overall magnitude of the gradient terms Finally, in Section A.4, we prove In terms of and ", the gradient ( 2) reads rL We will use the phrase "with high probability" to denote an event which happens with probability We will prove the claim by induction. We proceed with the induction. We now show that the classifier's accuracy on the mislabeled This proves the first claim.

People exhibit unique emotional responses. In the same scenario, the emotional reactions of two individuals can be either similar or vastly different. For instance, consider one person's reaction to an invitation to smoke versus another person's response to a query about their sleep quality. The identification of these individual traits through the observation of common physical parameters opens the door to a wide range of applications, including psychological analysis, criminology, disease prediction, addiction control, and more. While there has been previous research in the fields of psychometrics, inertial sensors, computer vision, and audio analysis, this paper introduces a novel technique for identifying human traits in real time using brainwave data. To achieve this, we begin with an extensive study of brainwave data collected from 80 participants using a portable EEG headset. We also conduct a statistical analysis of the collected data utilizing box plots. Our analysis uncovers several new insights, leading us to a groundbreaking unified approach for identifying diverse human traits by leveraging machine learning techniques on EEG data. Our analysis demonstrates that this proposed solution achieves high accuracy. Moreover, we explore two deep-learning models to compare the performance of our solution. Consequently, we have developed an integrated, real-time trait identification solution using EEG data, based on the insights from our analysis. To validate our approach, we conducted a rigorous user evaluation with an additional 20 participants. The outcomes of this evaluation illustrate both high accuracy and favorable user ratings, emphasizing the robust potential of our proposed method to serve as a versatile solution for human trait identification.

Machine learning is shaping up to be the transformative technology of our times: Many of us have played with (and marveled at) models like ChatGPT, new breakthroughs in applications like healthcare research are announced on an almost daily basis, and new avenues for integrating these tools into scientific research are opening up, with some mathematicians already using large language models as proof assistants. This article aims to lift the veil and dispel some myths about machine learning; along the way, it will also show how machine learning itself can benefit from mathematical concepts. Indeed, from the outside, machine learning might look like a homogeneous entity, but in fact, the field is fractured and highly diverse. While the main thrust of the field arises from the undeniable engineering advances, with bigger and better models, there is also a strong community of applied mathematicians. Next to the classical drivers of machine-learning architectures, i.e., linear algebra and statistics, topology recently started to provide novel insights into the foundations of machine learning: Point-set topology, harnessing concepts like neighborhoods, can be used to extend existing algorithms from graphs to cell complexes [4]. Algebraic topology, making use of effective invariants like homology, improves the results of models for volume reconstruction [13]. Finally, differential topology, providing tools to study smooth properties of data, results in efficient methods for analyzing embedded (simplicial) complexes [6]. These (and many more) methods have now found a home in the nascent field of topological deep learning [8]. Before diving into concrete examples, let us first take a step back and discuss machine learning as such.

A group of Japanese researchers has announced that it has developed a deep-learning model to predict the cancer onset risk from fatty liver images. The team, led by the University of Tokyo, created the model by using some of digital images of fatty liver tissues collected from 46 people who developed liver cancer within seven years of biopsy and 639 others who did not. For the artificial intelligence application project, the researchers gathered such images from a total of 2,432 people who had undergone the live tissue examination at nine medical institutions in the country. The model has proved that it can predict cancer onset risk with 82.3% accuracy, which compares with 78.2% for biopsy-based manual analyses, the researchers said. They also saw AI judge cell dysplasia and declining fat deposition despite the progression of fat liver as cancer risk factors and predict a high probability of mild fibrosis developing into cancer.

Real-time bioaerosol monitoring is improving the quality of life for people affected by allergies, but it often relies on deep-learning models which pose challenges for widespread adoption. These models are typically trained in a supervised fashion and require considerable effort to produce large amounts of annotated data, an effort that must be repeated for new particles, geographical regions, or measurement systems. In this work, we show that self-supervised learning and few-shot learning can be combined to classify holographic images of bioaerosol particles using a large collection of unlabelled data and only a few examples for each particle type. We first demonstrate that self-supervision on pictures of unidentified particles from ambient air measurements enhances identification even when labelled data is abundant. Most importantly, it greatly improves few-shot classification when only a handful of labelled images are available. Our findings suggest that real-time bioaerosol monitoring workflows can be substantially optimized, and the effort required to adapt models for different situations considerably reduced.

Objective: To report imaging protocol and scheduling variance in routine care of glioblastoma patients in order to demonstrate challenges of integrating deep-learning models in glioblastoma care pathways. Additionally, to understand the most common imaging studies and image contrasts to inform the development of potentially robust deep-learning models. Methods: MR imaging data were analysed from a random sample of five patients from the prospective cohort across five participating sites of the ZGBM consortium. Reported clinical and treatment data alongside DICOM header information were analysed to understand treatment pathway imaging schedules. Results: All sites perform all structural imaging at every stage in the pathway except for the presurgical study, where in some sites only contrast-enhanced T1-weighted imaging is performed. Diffusion MRI is the most common non-structural imaging type, performed at every site. Conclusion: The imaging protocol and scheduling varies across the UK, making it challenging to develop machine-learning models that could perform robustly at other centres. Structural imaging is performed most consistently across all centres. Advances in knowledge: Successful translation of deep-learning models will likely be based on structural post-treatment imaging unless there is significant effort made to standardise non-structural or peri-operative imaging protocols and schedules.

In remote sensing, we are interested in modeling various modalities for some geographic location. Several works have focused on learning the relationship between a location and type of landscape, habitability, audio, textual descriptions, etc. Recently, a common way to approach these problems is to train a deep-learning model that uses satellite images to infer some unique characteristics of the location. In this work, we present a deep-learning model, GeoBind, that can infer about multiple modalities, specifically text, image, and audio, from satellite imagery of a location. To do this, we use satellite images as the binding element and contrastively align all other modalities to the satellite image data. Our training results in a joint embedding space with multiple types of data: satellite image, ground-level image, audio, and text. Furthermore, our approach does not require a single complex dataset that contains all the modalities mentioned above. Rather it only requires multiple satellite-image paired data. While we only align three modalities in this paper, we present a general framework that can be used to create an embedding space with any number of modalities by using satellite images as the binding element. Our results show that, unlike traditional unimodal models, GeoBind is versatile and can reason about multiple modalities for a given satellite image input.

Maritime transportation is paramount in achieving global economic growth, entailing concurrent ecological obligations in sustainability and safeguarding endangered marine species, most notably preserving large whale populations. In this regard, the Automatic Identification System (AIS) data plays a significant role by offering real-time streaming data on vessel movement, allowing enhanced traffic monitoring. This study explores using AIS data to prevent vessel-to-whale collisions by forecasting long-term vessel trajectories from engineered AIS data sequences. For such a task, we have developed an encoder-decoder model architecture using Bidirectional Long Short-Term Memory Networks (Bi-LSTM) to predict the next 12 hours of vessel trajectories using 1 to 3 hours of AIS data as input. We feed the model with probabilistic features engineered from historical AIS data that refer to each trajectory's potential route and destination. The model then predicts the vessel's trajectory, considering these additional features by leveraging convolutional layers for spatial feature learning and a position-aware attention mechanism that increases the importance of recent timesteps of a sequence during temporal feature learning. The probabilistic features have an F1 Score of approximately 85% and 75% for each feature type, respectively, demonstrating their effectiveness in augmenting information to the neural network. We test our model on the Gulf of St. Lawrence, a region known to be the habitat of North Atlantic Right Whales (NARW). Our model achieved a high R2 score of over 98% using various techniques and features. It stands out among other approaches as it can make complex decisions during turnings and path selection. Our study highlights the potential of data engineering and trajectory forecasting models for marine life species preservation.

The introduction of machine learning marks a pivotal shift, presenting Nailfold capillaroscopy is a well-established method for automated medical image analysis as a promising alternative assessing health conditions, but the untapped potential of automated due to its higher accuracy compared to traditional image medical image analysis using machine learning remains processing algorithms[5]. Recent studies have attempted to despite recent advancements. In this groundbreaking use single deep-learning models for tasks such as nailfold study, we present a pioneering effort in constructing a comprehensive capillary segmentation[4, 8], measurement of capillary size dataset--321 images, 219 videos, 68 clinic reports, and density[5], and white cell counting[9]. Despite notable with expert annotations--that serves as a crucial resource achievements, the untapped potential of automated medical for training deep-learning models. Leveraging this image analysis persists due to the urgent need for annotated dataset, we propose an end-to-end nailfold capillary analysis and extensive datasets essential for effective training and pipeline capable of automatically detecting and measuring diverse fine-tuning deep neural networks.