Natural Language Processing (NLP) has become a cornerstone in many critical sectors, including healthcare, finance, and customer relationship management. This is especially true with the development and use of advanced models such as GPT-based architectures and BERT, which are widely used in decision-making processes. However, the black-box nature of these advanced NLP models has created an urgent need for transparency and explainability. This review explores explainable NLP (XNLP) with a focus on its practical deployment and real-world applications, examining its implementation and the challenges faced in domain-specific contexts. The paper underscores the importance of explainability in NLP and provides a comprehensive perspective on how XNLP can be designed to meet the unique demands of various sectors, from healthcare's need for clear insights to finance's emphasis on fraud detection and risk assessment. Additionally, this review aims to bridge the knowledge gap in XNLP literature by offering a domain-specific exploration and discussing underrepresented areas such as real-world applicability, metric evaluation, and the role of human interaction in model assessment. The paper concludes by suggesting future research directions that could enhance the understanding and broader application of XNLP.

Donald Trump granted clemency to 237 people during his administration. Some of the pardons--particularly those related to drug offenses--fit within the norms of the office. But a much larger portion were favors done for wealthy people who could access Trump through top-dollar lawyers, golf clubs, rich South Floridian social circles, and family. We revisited these pardons four years later to see what they could tell us about Trump's 2024 campaign. The biggest takeaway had to do with the shadowy political operatives--including Steve Bannon, Michael Flynn, and Roger Stone--who have spent the past four years pushing dangerous and wild election conspiracy theories in hopes they will be rewarded once more.

Multi-frequency Electrical Impedance Tomography (mfEIT) is a promising biomedical imaging technique that estimates tissue conductivities across different frequencies. Current state-of-the-art (SOTA) algorithms, which rely on supervised learning and Multiple Measurement Vectors (MMV), require extensive training data, making them time-consuming, costly, and less practical for widespread applications. Moreover, the dependency on training data in supervised MMV methods can introduce erroneous conductivity contrasts across frequencies, posing significant concerns in biomedical applications. To address these challenges, we propose a novel unsupervised learning approach based on Multi-Branch Attention Image Prior (MAIP) for mfEIT reconstruction. Our method employs a carefully designed Multi-Branch Attention Network (MBA-Net) to represent multiple frequency-dependent conductivity images and simultaneously reconstructs mfEIT images by iteratively updating its parameters. By leveraging the implicit regularization capability of the MBA-Net, our algorithm can capture significant inter- and intra-frequency correlations, enabling robust mfEIT reconstruction without the need for training data. Through simulation and real-world experiments, our approach demonstrates performance comparable to, or better than, SOTA algorithms while exhibiting superior generalization capability. These results suggest that the MAIP-based method can be used to improve the reliability and applicability of mfEIT in various settings.

OBJECTIVE: Ensuring that machine learning (ML) algorithms are safe and effective within all patient groups, and do not disadvantage particular patients, is essential to clinical decision making and preventing the reinforcement of existing healthcare inequities. The objective of this tutorial is to introduce the medical informatics community to the common notions of fairness within ML, focusing on clinical applications and implementation in practice. TARGET AUDIENCE: As gaps in fairness arise in a variety of healthcare applications, this tutorial is designed to provide an understanding of fairness, without assuming prior knowledge, to researchers and clinicians who make use of modern clinical data. SCOPE: We describe the fundamental concepts and methods used to define fairness in ML, including an overview of why models in healthcare may be unfair, a summary and comparison of the metrics used to quantify fairness, and a discussion of some ongoing research. We illustrate some of the fairness methods introduced through a case study of mortality prediction in a publicly available electronic health record dataset. Finally, we provide a user-friendly R package for comprehensive group fairness evaluation, enabling researchers and clinicians to assess fairness in their own ML work.

Parkinson's disease (PD) is a prevalent neurodegenerative disorder known for its impact on motor neurons, causing symptoms like tremors, stiffness, and gait difficulties. This study explores the potential of vocal feature alterations in PD patients as a means of early disease prediction. This research aims to predict the onset of Parkinson's disease. Utilizing a variety of advanced machine-learning algorithms, including XGBoost, LightGBM, Bagging, AdaBoost, and Support Vector Machine, among others, the study evaluates the predictive performance of these models using metrics such as accuracy, area under the curve (AUC), sensitivity, and specificity. The findings of this comprehensive analysis highlight LightGBM as the most effective model, achieving an impressive accuracy rate of 96% alongside a matching AUC of 96%. LightGBM exhibited a remarkable sensitivity of 100% and specificity of 94.43%, surpassing other machine learning algorithms in accuracy and AUC scores. Given the complexities of Parkinson's disease and its challenges in early diagnosis, this study underscores the significance of leveraging vocal biomarkers coupled with advanced machine-learning techniques for precise and timely PD detection.

Frank Rosenblatt, associate pro fessor of neurobiology at Cor nell University, died here yes terday in a boating accident. It was his 43d birthday. He lived in Brooktondale, N. Y., an Ithaca suburb. An originator of perception theory, he had developed an experimental machine that could be trained to identify automatically objects or pat terns such as letters of the al phabet. The instrument was an electromechanical device con sisting of a sensory unit of photo cells that viewed the pat tern shown to the machine, as sociation units that contained the machine's memory and re sponse units that displayed vis ually its pattern‐recognition re sponse.

Approximately 1.25 million people in the United States are treated each year for burn injuries. Precise burn injury classification is an important aspect of the medical AI field. In this work, we propose an explainable human-in-the-loop framework for improving burn ultrasound classification models. Our framework leverages an explanation system based on the LIME classification explainer to corroborate and integrate a burn expert's knowledge -- suggesting new features and ensuring the validity of the model. Using this framework, we discover that B-mode ultrasound classifiers can be enhanced by supplying textural features. More specifically, we confirm that texture features based on the Gray Level Co-occurance Matrix (GLCM) of ultrasound frames can increase the accuracy of transfer learned burn depth classifiers. We test our hypothesis on real data from porcine subjects. We show improvements in the accuracy of burn depth classification -- from ~88% to ~94% -- once modified according to our framework.

We introduce a new open information extraction (OIE) benchmark for pre-trained language models (LM). Recent studies have demonstrated that pre-trained LMs, such as BERT and GPT, may store linguistic and relational knowledge. In particular, LMs are able to answer ``fill-in-the-blank'' questions when given a pre-defined relation category. Instead of focusing on pre-defined relations, we create an OIE benchmark aiming to fully examine the open relational information present in the pre-trained LMs. We accomplish this by turning pre-trained LMs into zero-shot OIE systems. Surprisingly, pre-trained LMs are able to obtain competitive performance on both standard OIE datasets (CaRB and Re-OIE2016) and two new large-scale factual OIE datasets (TAC KBP-OIE and Wikidata-OIE) that we establish via distant supervision. For instance, the zero-shot pre-trained LMs outperform the F1 score of the state-of-the-art supervised OIE methods on our factual OIE datasets without needing to use any training sets. Our code and datasets are available at https://github.com/cgraywang/IELM

FutureTech II Acquisition Corp. (NASDAQ:FTII) has raised approximately $100 million from an IPO at a price of $10.00 per unit, according to the terms of its most recent S-1/A regulatory filing. The SPAC (Special Purpose Acquisition Company) intends to pursue a merger with a company in the sectors of'disruptive technologies, for example, artificial intelligence, robotics, and any other technology innovations.' My approach is to seek SPACs where the executives have significant industry operating experience as well as at least one SPAC with a track record of success. So, absent those two characteristics, I'm on Hold for FTII at the present time. FutureTech II has 2 executives leading its sponsor, FutureTech II Partners LLC. Chief Executive Officer Yuquan Wang, who was the founding partner of Haiyin Capital and has been a board member of robotics companies and other technology firms.

On July 7, 1958, several men gathered around a machine inside the offices of the United States Weather Bureau in Washington, D.C., about fifteen blocks west of the White House. As wide as a kitchen refrigerator, twice as deep, and nearly as tall, the machine was just one piece of a mainframe computer that fanned across the room like a multipiece furniture set. It was encased in silvery plastic, reflecting the light from above, and the front panel held row after row of small round lightbulbs, red square buttons, and thick plastic switches, some white and some gray. Normally, this $2 million machine ran calculations for the Weather Bureau, the forerunner of the National Weather Service, but on this day, it was on loan to the U.S. Navy and a twenty-nine-year-old Cornell University professor named Frank Rosenblatt. As a newspaper reporter looked on, Rosenblatt and his Navy cohorts fed two white cards into the machine, one marked with a small square on the left, the other marked on the right.