This paper addresses second-order stochastic optimization for estimating the minimizer of a convex function written as an expectation. A direct recursive estimation technique for the inverse Hessian matrix using a Robbins-Monro procedure is introduced. This approach enables to drastically reduces computational complexity. Above all, it allows to develop universal stochastic Newton methods and investigate the asymptotic efficiency of the proposed approach. This work so expands the application scope of secondorder algorithms in stochastic optimization.

In this paper, we consider the problem of partitioning a small data sample of size $n$ drawn from a mixture of $2$ sub-gaussian distributions. In particular, we analyze computational efficient algorithms proposed by the same author, to partition data into two groups approximately according to their population of origin given a small sample. This work is motivated by the application of clustering individuals according to their population of origin using $p$ markers, when the divergence between any two of the populations is small. We build upon the semidefinite relaxation of an integer quadratic program that is formulated essentially as finding the maximum cut on a graph, where edge weights in the cut represent dissimilarity scores between two nodes based on their $p$ features. Here we use $\Delta^2 :=p \gamma$ to denote the $\ell_2^2$ distance between two centers (mean vectors), namely, $\mu^{(1)}$, $\mu^{(2)}$ $\in$ $\mathbb{R}^p$. The goal is to allow a full range of tradeoffs between $n, p, \gamma$ in the sense that partial recovery (success rate $< 100\%$) is feasible once the signal to noise ratio $s^2 := \min\{np \gamma^2, \Delta^2\}$ is lower bounded by a constant. Importantly, we prove that the misclassification error decays exponentially with respect to the SNR $s^2$. This result was introduced earlier without a full proof. We therefore present the full proof in the present work. Finally, for balanced partitions, we consider a variant of the SDP1, and show that the new estimator has a superb debiasing property. This is novel to the best of our knowledge.

Beyond 5G and 6G networks are expected to support new and challenging use cases and applications that depend on a certain level of Quality of Service (QoS) to operate smoothly. Predicting the QoS in a timely manner is of high importance, especially for safety-critical applications as in the case of vehicular communications. Although until recent years the QoS prediction has been carried out by centralized Artificial Intelligence (AI) solutions, a number of privacy, computational, and operational concerns have emerged. Alternative solutions have been surfaced (e.g. Split Learning, Federated Learning), distributing AI tasks of reduced complexity across nodes, while preserving the privacy of the data. However, new challenges rise when it comes to scalable distributed learning approaches, taking into account the heterogeneous nature of future wireless networks. The current work proposes DISTINQT, a privacy-aware distributed learning framework for QoS prediction. Our framework supports multiple heterogeneous nodes, in terms of data types and model architectures, by sharing computations across them. This, enables the incorporation of diverse knowledge into a sole learning process that will enhance the robustness and generalization capabilities of the final QoS prediction model. DISTINQT also contributes to data privacy preservation by encoding any raw input data into a non-linear latent representation before any transmission. Evaluation results showcase that our framework achieves a statistically identical performance compared to its centralized version and an average performance improvement of up to 65% against six state-of-the-art centralized baseline solutions in the Tele-Operated Driving use case.

ABSTRACT Considering data insufficiency in metal additive manufacturing (AM), transfer learning (TL) has been adopted to extract knowledge from source domains (e.g., completed printings) to improve the modeling performance in target domains (e.g., new printings). Current applications use all accessible source data directly in TL with no regard to the similarity between source and target data. This paper proposes a systematic method to find appropriate subsets of source data based on similarities between the source and target datasets for a given set of limited target domain data. Such similarity is characterized by the spatial and model distance metrics. A Pareto frontier-based source data selection method is developed, where the source data located on the Pareto frontier defined by two similarity distance metrics are selected iteratively. The method is integrated into an instance-based TL method (decision tree regression model) and a model-based TL method (fine-tuned artificial neural network). Both models are then tested on several regression tasks in metal AM. Comparison results demonstrate that 1) the source data selection method is general and supports integration with various TL methods and distance metrics, 2) compared with using all source data, the proposed method can find a small subset of source data from the same domain with better TL performance in metal AM regression tasks involving different processes and machines, and 3) when multiple source domains exist, the source data selection method could find the subset from one source domain to obtain comparable or better TL performance than the model constructed using data from all source domains. Keywords: metal additive manufacturing, transfer learning, source data selection, Pareto frontier 1 Introduction Metal additive manufacturing (AM) fabricates parts by depositing metal materials layer by layer with various heat sources, e.g., the laser beam and electric arc. Although metal AM has been adopted in electronics (Pang et al. 2020), automotive (Vasco 2021), aerospace (Blakey-Milner et al. 2021), and other industries, low productivity and unstable quality are two drawbacks that restrict the applications of metal AM. To alleviate the two drawbacks, constructing data-driven models to reveal correlations among processes, structures, and properties has attracted attention in both industry and academia. These models are built based on collected data from experiments or simulations and adopted for process optimization, control, or monitoring to improve the quality of printed parts.

Breast cancer prognosis is crucial for effective treatment, with the disease more common in women over 40 years old but rare under 40 years old, where less than 5 percent of cases occur in the U.S. Studies indicate a worse prognosis in younger women, which varies by ethnicity. Breast cancers are classified based on receptors like estrogen, progesterone, and HER2. Triple-negative breast cancer (TNBC), lacking these receptors, accounts for about 15 percent of cases and is more prevalent in younger patients, often resulting in poorer outcomes. Nevertheless, the impact of age on TNBC prognosis remains unclear. Factors like age, race, tumor grade, size, and lymph node status are studied for their role in TNBC's clinical outcomes, but current research is inconclusive about age-related differences. This study uses SEER data set to examine the influence of younger age on survivability in TNBC patients, aiming to determine if age is a significant prognostic factor. Our experimental results on SEER dataset confirm the existing research reports that TNBC patients have worse prognosis compared to non-TNBC based on age. Our main goal was to investigate whether younger age has any significance on the survivability of TNBC patients. Experimental results do not show that younger age has any significance on the prognosis and survival rate of the TNBC patients

Unsupervised image segmentation aims at grouping different semantic patterns in an image without the use of human annotation. Similarly, image clustering searches for groupings of images based on their semantic content without supervision. Classically, both problems have captivated researchers as they drew from sound mathematical concepts to produce concrete applications. With the emergence of deep learning, the scientific community turned its attention to complex neural network-based solvers that achieved impressive results in those domains but rarely leveraged the advances made by classical methods. In this work, we propose a patch-based unsupervised image segmentation strategy that bridges advances in unsupervised feature extraction from deep clustering methods with the algorithmic help of classical graph-based methods. We show that a simple convolutional neural network, trained to classify image patches and iteratively regularized using graph cuts, naturally leads to a state-of-the-art fully-convolutional unsupervised pixel-level segmenter. Furthermore, we demonstrate that this is the ideal setting for leveraging the patch-level pairwise features generated by vision transformer models. Our results on real image data demonstrate the effectiveness of our proposed methodology.

With the growing application of AI-based systems in our lives and society, there is a rising need to ensure that AI-based systems are developed and used in a responsible way. Fairness is one of the socio-technical concerns that must be addressed in AI-based systems for this purpose. Unfair AI-based systems, particularly, unfair AI-based mobile apps, can pose difficulties for a significant proportion of the global populace. This paper aims to deeply analyze fairness concerns in AI-based app reviews. We first manually constructed a ground-truth dataset including a statistical sample of fairness and non-fairness reviews. Leveraging the ground-truth dataset, we then developed and evaluated a set of machine learning and deep learning classifiers that distinguish fairness reviews from non-fairness reviews. Our experiments show that our best-performing classifier can detect fairness reviews with a precision of 94%. We then applied the best-performing classifier on approximately 9.5M reviews collected from 108 AI-based apps and identified around 92K fairness reviews. While the fairness reviews appear in 23 app categories, we found that the 'communication' and 'social' app categories have the highest percentage of fairness reviews. Next, applying the K-means clustering technique to the 92K fairness reviews, followed by manual analysis, led to the identification of six distinct types of fairness concerns (e.g., 'receiving different quality of features and services in different platforms and devices' and 'lack of transparency and fairness in dealing with user-generated content'). Finally, the manual analysis of 2,248 app owners' responses to the fairness reviews identified six root causes (e.g., 'copyright issues', 'external factors', 'development cost') that app owners report to justify fairness concerns.

Traumatic Brain Injury (TBI) presents a broad spectrum of clinical presentations and outcomes due to its inherent heterogeneity, leading to diverse recovery trajectories and varied therapeutic responses. While many studies have delved into TBI phenotyping for distinct patient populations, identifying TBI phenotypes that consistently generalize across various settings and populations remains a critical research gap. Our research addresses this by employing multivariate time-series clustering to unveil TBI's dynamic intricates. Utilizing a self-supervised learning-based approach to clustering multivariate time-Series data with missing values (SLAC-Time), we analyzed both the research-centric TRACK-TBI and the real-world MIMIC-IV datasets. Remarkably, the optimal hyperparameters of SLAC-Time and the ideal number of clusters remained consistent across these datasets, underscoring SLAC-Time's stability across heterogeneous datasets. Our analysis revealed three generalizable TBI phenotypes ({\alpha}, \b{eta}, and {\gamma}), each exhibiting distinct non-temporal features during emergency department visits, and temporal feature profiles throughout ICU stays. Specifically, phenotype {\alpha} represents mild TBI with a remarkably consistent clinical presentation. In contrast, phenotype \b{eta} signifies severe TBI with diverse clinical manifestations, and phenotype {\gamma} represents a moderate TBI profile in terms of severity and clinical diversity. Age is a significant determinant of TBI outcomes, with older cohorts recording higher mortality rates. Importantly, while certain features varied by age, the core characteristics of TBI manifestations tied to each phenotype remain consistent across diverse populations.

A novel freestyle rap software, MCMChaos 0.0.1, based on rap music transcriptions created in previous research is presented. The software has three different versions, each making use of different mathematical simulation methods: collapsed gibbs sampler and lorenz attractor simulation. As far as we know, these simulation methods have never been used in rap music generation before. The software implements Python Text-to-Speech processing (pyttxs) to convert text wrangled from the MCFlow corpus into English speech. In each version, values simulated from each respective mathematical model alter the rate of speech, volume, and (in the multiple voice case) the voice of the text-to-speech engine on a line-by-line basis. The user of the software is presented with a real-time graphical user interface (GUI) which instantaneously changes the initial values read into the mathematical simulation methods. Future research might attempt to allow for more user control and autonomy.

High-throughput omics profiling advancements have greatly enhanced cancer patient stratification. However, incomplete data in multi-omics integration presents a significant challenge, as traditional methods like sample exclusion or imputation often compromise biological diversity and dependencies. Furthermore, the critical task of accurately classifying new patients with partial omics data into existing subtypes is commonly overlooked. To address these issues, we introduce IntegrAO (Integrate Any Omics), an unsupervised framework for integrating incomplete multi-omics data and classifying new samples. IntegrAO first combines partially overlapping patient graphs from diverse omics sources and utilizes graph neural networks to produce unified patient embeddings. Our systematic evaluation across five cancer cohorts involving six omics modalities demonstrates IntegrAO's robustness to missing data and its accuracy in classifying new samples with partial profiles. An acute myeloid leukemia case study further validates its capability to uncover biological and clinical heterogeneity in incomplete datasets. IntegrAO's ability to handle heterogeneous and incomplete data makes it an essential tool for precision oncology, offering a holistic approach to patient characterization.