Miracle-dz/UniFAR-FTUs huggingface dataset:
AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions

Artificial intelligence (AI) has significantly impacted thyroid cancer diagnosis in recent years, offering advanced tools and methodologies that promise to revolutionize patient outcomes. This review provides an exhaustive overview of the contemporary frameworks employed in the field, focusing on the objective of AI-driven analysis and dissecting methodologies across supervised, unsupervised, and ensemble learning. Specifically, we delve into techniques such as deep learning, artificial neural networks, traditional classification, and probabilistic models (PMs) under supervised learning. With its prowess in clustering and dimensionality reduction, unsupervised learning (USL) is explored alongside ensemble methods, including bagging and potent boosting algorithms. The thyroid cancer datasets (TCDs) are integral to our discussion, shedding light on vital features and elucidating feature selection and extraction techniques critical for AI-driven diagnostic systems. We lay out the standard assessment criteria across classification, regression, statistical, computer vision, and ranking metrics, punctuating the discourse with a real-world example of thyroid cancer detection using AI. Additionally, this study culminates in a critical analysis, elucidating current limitations and delineating the path forward by highlighting open challenges and prospective research avenues. Through this comprehensive exploration, we aim to offer readers a panoramic view of AI’s transformative role in thyroid cancer diagnosis, underscoring its potential and pointing toward an optimistic future.
Compositional Semantic Mix for Domain Adaptation in Point Cloud Segmentation

Deep-learning models for 3D point cloud semantic segmentation exhibit limited generalization capabilities when trained and tested on data captured with different sensors or in varying environments due to domain shift. Domain adaptation methods can be employed to mitigate this domain shift, for instance, by simulating sensor noise, developing domain-agnostic generators, or training point cloud completion networks. Often, these methods are tailored for range view maps or necessitate multi-modal input. In contrast, domain adaptation in the image domain can be executed through sample mixing, which emphasizes input data manipulation rather than employing distinct adaptation modules. In this study, we introduce compositional semantic mixing for point cloud domain adaptation, representing the first unsupervised domain adaptation technique for point cloud segmentation based on semantic and geometric sample mixing. We present a two-branch symmetric network architecture capable of concurrently processing point clouds from a source domain (e.g. synthetic) and point clouds from a target domain (e.g. real-world). Each branch operates within one domain by integrating selected data fragments from the other domain and utilizing semantic information derived from source labels and target (pseudo) labels. Additionally, our method can leverage a limited number of human point-level annotations (semi-supervised) to further enhance performance. We assess our approach in both synthetic-to-real and real-to-real scenarios using LiDAR datasets and demonstrate that it significantly outperforms state-of-the-art methods in both unsupervised and semi-supervised settings.
ChainedFilter: Combining Membership Filters by Chain Rule

Membership (membership query/membership testing) is a fundamental problem across databases, networks and security. However, previous research has primarily focused on either approximate solutions, such as Bloom Filters, or exact methods, like perfect hashing and dictionaries, without attempting to develop an integral theory. In this paper, we propose a unified and complete theory, namely chain rule, for general membership problems, which encompasses both approximate and exact membership as extreme cases. Building upon the chain rule, we introduce a straightforward yet versatile algorithm framework, namely ChainedFilter, to combine different elementary filters without losing information. Our evaluation results demonstrate that ChainedFilter improves performance of many applications including static dictionary, lossless data compression, Cuckoo Hashing, LSM-Tree and Learned Filters.
Neuropsychiatric assessments in patients with multiple sclerosis in early phases and with low disability

Background: In the early phases of multiple sclerosis (MS), patients exhibit slight neuropsychiatric deficits that can only be detected using reliable tools. Aim: The present investigation aimed to examine neuropsychological performance in 35 patients with incipient MS. Patients and methods: For the MS group, the inclusion criteria included time of disease , 3 years and low disability. The neuropsychological battery consisted of Rey Auditory Learning Test, Controlled Oral Word Association Test, Hooper Visual Organization Test, and Symbol Digit Modalities Test (SDMT). Results: After correction for the educational level, no significant effect of MS on performance was found for all the tests except for the number of errors of the SDMT (NE-SDMT). Higher levels of education were associated with better performances in all tests, except for the NE-SDMT. MS patients made more errors than the controls. Conclusion: The effect on the NE-SDMT may reflect difficulties in the ability to inhibit inad-equate responses. Patients may exhibit impulsive control disorders in incipient MS, independent of their educational level.
Rare Events Analysis and Computation for Stochastic Evolution of Bacterial Populations

In this paper, we develop a computational approach for computing most likely trajectories describing rare events that correspond to the emergence of non-dominant genotypes. This work is based on the large deviations approach for discrete Markov chains describing the genetic evolution of large bacterial populations. We demonstrate that a gradient descent algorithm developed in this paper results in the fast and accurate computation of most-likely trajectories for a large number of bacterial genotypes. We supplement our analysis with extensive numerical simulations demonstrating the computational advantage of the designed gradient descent algorithm over other, more simplified, approaches.
Effect of Attention and Self-Supervised Speech Embeddings on Non-Semantic Speech Tasks

Human emotion understanding is pivotal in making conversational technology mainstream. We view speech emotion understanding as a perception task which is a more realistic setting. With varying contexts (languages, demographics etc.) different share of people perceive the same speech segment as a non-unanimous emotion. As part of the ACM Multimedia 2023 Computational Paralinguistics ChallengE (ComParE) in the EMotion Share track, we leverage their rich dataset of multilingual speakers and multi-label regression target of 'emotion share' or perception of that emotion. We demonstrate that the training scheme of different foundation models dictates their effectiveness for tasks beyond speech recognition, especially for non-semantic speech tasks like emotion understanding. This is a very complex task due to multilingual speakers, variability in the target labels, and inherent imbalance in the regression dataset. Our results show that HuBERT-Large with a self-attention-based light-weight sequence model provides 4.6% improvement over the reported baseline.
A Generalization of Continuous Relaxation in Structured Pruning

Deep learning harnesses massive parallel floating-point processing to train and evaluate large neural networks. Trends indicate that deeper and larger neural networks with an increasing number of parameters achieve higher accuracy than smaller neural networks. This performance improvement, which often requires heavy compute for both training and evaluation, eventually needs to translate well to resource-constrained hardware for practical value. Structured pruning asserts that while large networks enable us to find solutions to complex computer vision problems, a smaller, computationally efficient sub-network can be derived from the large neural network that retains model accuracy but significantly improves computational efficiency. We generalize structured pruning with algorithms for network augmentation, pruning, sub-network collapse and removal. In addition, we demonstrate efficient and stable convergence up to 93% sparsity and 95% FLOPs reduction without loss of inference accuracy using with continuous relaxation matching or exceeding the state of the art for all structured pruning methods. The resulting CNN executes efficiently on GPU hardware without computationally expensive sparse matrix operations. We achieve this with routine automatable operations on classification and segmentation problems using CIFAR-10, ImageNet, and CityScapes datasets with the ResNet and U-NET network architectures.
Revolutionizing Disease Diagnosis: A Microservices-Based Architecture for Privacy-Preserving and Efficient IoT Data Analytics Using Federated Learning

Deep learning-based disease diagnosis applications are essential for accurate diagnosis at various disease stages. However, using personal data exposes traditional centralized learning systems to privacy concerns. On the other hand, by positioning processing resources closer to the device and enabling more effective data analyses, a distributed computing paradigm has the potential to revolutionize disease diagnosis. Scalable architectures for data analytics are also crucial in healthcare, where data analytics results must have low latency and high dependability and reliability. This study proposes a microservices-based approach for IoT data analytics systems to satisfy privacy and performance requirements by arranging entities into fine-grained, loosely connected, and reusable collections. Our approach relies on federated learning, which can increase disease diagnosis accuracy while protecting data privacy. Additionally, we employ transfer learning to obtain more efficient models. Using more than 5800 chest X-ray images for pneumonia detection from a publicly available dataset, we ran experiments to assess the effectiveness of our approach. Our experiments reveal that our approach performs better in identifying pneumonia than other cutting-edge technologies, demonstrating our approach's promising potential detection performance.
Combining Automatic Coding and Instructor Input to Generate ENA Visualizations for Asynchronous Online Discussion

Asynchronous online discussions are a common fundamental tool to facilitate social interaction in hybrid and online courses. However, instructors lack the tools to accomplish the overwhelming task of evaluating asynchronous online discussion activities. In this paper we present an approach that uses Latent Dirichlet Analysis (LDA) and the instructor's keywords to automatically extract codes from a relatively small dataset. We use the generated codes to build an Epistemic Network Analysis (ENA) model and compare this model with a previous ENA model built by human coders. The results show that there is no statistical difference between the two models. We present an analysis of these models and discuss the potential use of ENA as a visualization to help instructors evaluating asynchronous online discussions.
Reinforcement Learning for Generative AI: A Survey

Deep Generative AI has been a long-standing essential topic in the machine learning community, which can impact a number of application areas like text generation and computer vision. The major paradigm to train a generative model is maximum likelihood estimation, which pushes the learner to capture and approximate the target data distribution by decreasing the divergence between the model distribution and the target distribution. This formulation successfully establishes the objective of generative tasks, while it is incapable of satisfying all the requirements that a user might expect from a generative model. Reinforcement learning, serving as a competitive option to inject new training signals by creating new objectives that exploit novel signals, has demonstrated its power and flexibility to incorporate human inductive bias from multiple angles, such as adversarial learning, hand-designed rules and learned reward model to build a performant model. Thereby, reinforcement learning has become a trending research field and has stretched the limits of generative AI in both model design and application. It is reasonable to summarize and conclude advances in recent years with a comprehensive review. Although there are surveys in different application areas recently, this survey aims to shed light on a high-level review that spans a range of application areas. We provide a rigorous taxonomy in this area and make sufficient coverage on various models and applications. Notably, we also surveyed the fast-developing large language model area. We conclude this survey by showing the potential directions that might tackle the limit of current models and expand the frontiers for generative AI.
Deep learning-based computational method for soft matter dynamics: Deep Onsager-Machlup method

A deep learning-based computational method has been proposed for soft matter dynamics -- the deep Onsager-Machlup method (DOMM), which combines the brute forces of deep neural networks (DNNs) with the fundamental physics principle -- Onsager-Machlup variational principle (OMVP). In the DOMM, the trial solution to the dynamics is constructed by DNNs that allow us to explore a rich and complex set of admissible functions. It outperforms the Ritz-type variational method where one has to impose carefully-chosen trial functions. This capability endows the DOMM with the potential to solve rather complex problems in soft matter dynamics that involve multiple physics with multiple slow variables, multiple scales, and multiple dissipative processes. Actually, the DOMM can be regarded as an extension of the deep Ritz method constructed using variational formulations of physics models to solve static problems in physics as discussed in our former work [Wang et al, Soft Matter, 2022, 18, 6015-6031]. In this work, as the first step, we focus on the validation of the DOMM as a useful computational method by using it to solve several typical soft matter dynamic problems: particle diffusion in dilute solutions, and two-phase dynamics with and without hydrodynamics. The predicted results agree very well with the analytical solution or numerical solution from traditional computational methods. These results show the accuracy and convergence of DOMM and justify it as an alternative computational method for solving soft matter dynamics.
Open Government Data Corpus for Table Search

Increasing amounts of structured data can provide value for research and business if the relevant data can be located. Often the data is in a data lake without a consistent schema, making locating useful data challenging. Table search is a growing research area, but existing benchmarks have been limited to displayed tables. Tables sized and formatted for display in a Wikipedia page or ArXiv paper are considerably different from data tables in both scale and style. By using metadata associated with open data from government portals, we create the first dataset to benchmark search over data tables at scale. We demonstrate three styles of table-to-table related table search. The three notions of table relatedness are: tables produced by the same organization, tables distributed as part of the same dataset, and tables with a high degree of overlap in the annotated tags. The keyword tags provided with the metadata also permit the automatic creation of a keyword search over tables benchmark. We provide baselines on this dataset using existing methods including traditional and neural approaches.
Robust Machine Learning Modeling for Predictive Control Using Lipschitz-Constrained Neural Networks

Neural networks (NNs) have emerged as a state-of-the-art method for modeling nonlinear systems in model predictive control (MPC). However, the robustness of NNs, in terms of sensitivity to small input perturbations, remains a critical challenge for practical applications. To address this, we develop Lipschitz-Constrained Neural Networks (LCNNs) for modeling nonlinear systems and derive rigorous theoretical results to demonstrate their effectiveness in approximating Lipschitz functions, reducing input sensitivity, and preventing over-fitting. Specifically, we first prove a universal approximation theorem to show that LCNNs using SpectralDense layers can approximate any 1-Lipschitz target function. Then, we prove a probabilistic generalization error bound for LCNNs using SpectralDense layers by using their empirical Rademacher complexity. Finally, the LCNNs are incorporated into the MPC scheme, and a chemical process example is utilized to show that LCNN-based MPC outperforms MPC using conventional feedforward NNs in the presence of training data noise.
Explaining with Attribute-based and Relational Near Misses: An Interpretable Approach to Distinguishing Facial Expressions of Pain and Disgust

Explaining concepts by contrasting examples is an efficient and convenient way of giving insights into the reasons behind a classification decision. This is of particular interest in decision-critical domains, such as medical diagnostics. One particular challenging use case is to distinguish facial expressions of pain and other states, such as disgust, due to high similarity of manifestation. In this paper, we present an approach for generating contrastive explanations to explain facial expressions of pain and disgust shown in video sequences. We implement and compare two approaches for contrastive explanation generation. The first approach explains a specific pain instance in contrast to the most similar disgust instance(s) based on the occurrence of facial expressions (attributes). The second approach takes into account which temporal relations hold between intervals of facial expressions within a sequence (relations). The input to our explanation generation approach is the output of an interpretable rule-based classifier for pain and disgust.We utilize two different similarity metrics to determine near misses and far misses as contrasting instances. Our results show that near miss explanations are shorter than far miss explanations, independent from the applied similarity metric. The outcome of our evaluation indicates that pain and disgust can be distinguished with the help of temporal relations. We currently plan experiments to evaluate how the explanations help in teaching concepts and how they could be enhanced by further modalities and interaction.
Calling the Shots: Immunization Finance Policies and Practices

Executive Summary Introduction Background Charge to the Committee Study Context: The National Immunization Partnership Six Roles of the National Immunization System Study Approach Organization of the Report Change and Complexity in the National Immunization System Immunization System Key Changes Increasing Complexity Successes and Persistent Problems Financing Vaccine Purchase and Delivery Private Insurance Coverage of Immunization Medicaid, Vaccines for Children, and State Children's Health Insurance Program Medicare Section 317 Vaccine Purchase Grants State Vaccine Purchase Issues in Vaccine Purchase, Summing Up, Building, Monitoring, and Sustaining, Immunization Capacity Infectious Disease Prevention and Control Surveillance of Vaccine Coverage and Safety Efforts to Improve and Sustain High Vaccine Coverage Rates Summing Up Immunization Finance Policies and Practices Private-Sector Roles and Responsibilities Local Health Department Roles and Responsibilities State Roles and Responsibilities Federal Roles and Responsibilities Summing Up Summary Findings, Conclusions, and Recommendations Six Questions and Six Answers Conclusions Summary Recommendations References Appendixes A Public Health Services Act, Section B Immunization Time-Line C List of Contributors D Overview of State Survey E Overview of Case Studies and Site Visits F Annual Section 317 Awards to States, 1995-1999 G State Immunization Requirements for School Children H Committee and Staff Biographies Index
From Atmospheric Dynamics to Insurance Losses: An Interdisciplinary Workshop on European Storms

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. From Atmospheric Dynamics to Insurance Losses: An Interdisciplinary Workshop on European Storms Joaquim Pinto, Florian Pantillon, Patrick Ludwig, Madeleine-Sophie Deroche, Giovanni Leoncini, Christoph Raible, Len Shaffrey, David Stephenson
Symbolic & Acoustic: Multi-domain Music Emotion Modeling for Instrumental Music

Music Emotion Recognition involves the automatic identification of emotional elements within music tracks, and it has garnered significant attention due to its broad applicability in the field of Music Information Retrieval. It can also be used as the upstream task of many other human-related tasks such as emotional music generation and music recommendation. Due to existing psychology research, music emotion is determined by multiple factors such as the Timbre, Velocity, and Structure of the music. Incorporating multiple factors in MER helps achieve more interpretable and finer-grained methods. However, most prior works were uni-domain and showed weak consistency between arousal modeling performance and valence modeling performance. Based on this background, we designed a multi-domain emotion modeling method for instrumental music that combines symbolic analysis and acoustic analysis. At the same time, because of the rarity of music data and the difficulty of labeling, our multi-domain approach can make full use of limited data. Our approach was implemented and assessed using the publicly available piano dataset EMOPIA, resulting in a notable improvement over our baseline model with a 2.4% increase in overall accuracy, establishing its state-of-the-art performance.
Heterogeneous integration of spin-photon interfaces with a scalable CMOS platform

Color centers in diamonds have emerged as a leading solid-state platform for advancing quantum technologies, satisfying the DiVincenzo criteria and recently achieving a quantum advantage in secret key distribution. Recent theoretical works estimate that general-purpose quantum computing using local quantum communication networks will require millions of physical qubits to encode thousands of logical qubits, which presents a substantial challenge to the hardware architecture at this scale. To address the unanswered scaling problem, in this work, we first introduce a scalable hardware modular architecture"Quantum System-on-Chip"(QSoC) that features compact two-dimensional arrays"quantum microchiplets"(QMCs) containing tin-vacancy (SnV-) spin qubits integrated on a cryogenic application-specific integrated circuit (ASIC). We demonstrate crucial architectural subcomponents, including (1) QSoC fabrication via a lock-and-release method for large-scale heterogeneous integration; (2) a high-throughput calibration of the QSoC for spin qubit spectral inhomogenous registration; (3) spin qubit spectral tuning functionality for inhomogenous compensation; (4) efficient spin-state preparation and measurement for improved spin and optical properties. QSoC architecture supports full connectivity for quantum memory arrays in a set of different resonant frequencies and offers the possibility for further scaling the number of solid-state physical qubits via larger and denser QMC arrays and optical frequency multiplexing networking.