Shoe print evidence recovered from crime scenes plays a key role in forensic investigations. By examining shoe prints, investigators can determine details of the footwear worn by suspects. However, establishing that a suspect's shoes match the make and model of a crime scene print may not be sufficient. Typically, thousands of shoes of the same size, make, and model are manufactured, any of which could be responsible for the print. Accordingly, a popular approach used by investigators is to examine the print for signs of ``accidentals,'' i.e., cuts, scrapes, and other features that accumulate on shoe soles after purchase due to wear. While some patterns of accidentals are common on certain types of shoes, others are highly distinctive, potentially distinguishing the suspect's shoe from all others. Quantifying the rarity of a pattern is thus essential to accurately measuring the strength of forensic evidence. In this study, we address this task by developing a hierarchical Bayesian model. Our improvement over existing methods primarily stems from two advancements. First, we frame our approach in terms of a latent Gaussian model, thus enabling inference to be efficiently scaled to large collections of annotated shoe prints via integrated nested Laplace approximations. Second, we incorporate spatially varying coefficients to model the relationship between shoes' tread patterns and accidental locations. We demonstrate these improvements through superior performance on held-out data, which enhances accuracy and reliability in forensic shoe print analysis.

There's a lesson I once learned from a CEO--a leader admired not just for his strategic acumen but also for his unerring eye for quality. He's renowned for respecting the creative people in his company. Yet he's also unflinching in offering pointed feedback. When asked what guided his input, he said, "I may not be a creative genius, but I've come to trust my taste." That comment stuck with me. I've spent much of my career thinking about leadership.

Training large language models (LLMs) is resource-intensive and expensive, making intellectual property (IP) protection essential. Most existing model fingerprint methods inject fingerprints into LLMs to protect model ownership. These methods create fingerprint pairs with weak semantic correlations, lacking the contextual coherence and semantic relatedness founded in normal question-answer (QA) pairs in LLMs. In this paper, we propose a Generation Revision Intervention (GRI) attack that can effectively exploit this flaw to erase fingerprints, highlighting the need for more secure model fingerprint methods. Thus, we propose a novel injected fingerprint paradigm called Implicit Fingerprints (ImF). ImF constructs fingerprint pairs with strong semantic correlations, disguising them as natural QA pairs within LLMs. This ensures the fingerprints are consistent with normal model behavior, making them indistinguishable and robust against detection and removal. Our experiment on multiple LLMs demonstrates that ImF retains high verification success rates under adversarial conditions, offering a reliable solution for protecting LLM ownership.

Several neutrino detectors, KamLAND, Daya Bay, Double Chooz, RENO, and the forthcoming large-scale JUNO, rely on liquid scintillator to detect reactor antineutrino interactions. In this context, inverse beta decay represents the golden channel for antineutrino detection, providing a pair of correlated events, thus a strong experimental signature to distinguish the signal from a variety of backgrounds. However, given the low cross-section of antineutrino interactions, the development of a powerful event selection algorithm becomes imperative to achieve effective discrimination between signal and backgrounds. In this study, we introduce a machine learning (ML) model to achieve this goal: a fully connected neural network as a powerful signal-background discriminator for a large liquid scintillator detector. We demonstrate, using the JUNO detector as an example, that, despite the already high efficiency of a cut-based approach, the presented ML model can further improve the overall event selection efficiency. Moreover, it allows for the retention of signal events at the detector edges that would otherwise be rejected because of the overwhelming amount of background events in that region. We also present the first interpretable analysis of the ML approach for event selection in reactor neutrino experiments. This method provides insights into the decision-making process of the model and offers valuable information for improving and updating traditional event selection approaches.

Opioid related aberrant behaviors (ORAB) present novel risk factors for opioid overdose. Previously, ORAB have been mainly assessed by survey results and by monitoring drug administrations. Such methods however, cannot scale up and do not cover the entire spectrum of aberrant behaviors. On the other hand, ORAB are widely documented in electronic health record notes. This paper introduces a novel biomedical natural language processing benchmark dataset named ODD, for ORAB Detection Dataset. ODD is an expert-annotated dataset comprising of more than 750 publicly available EHR notes. ODD has been designed to identify ORAB from patients' EHR notes and classify them into nine categories; 1) Confirmed Aberrant Behavior, 2) Suggested Aberrant Behavior, 3) Opioids, 4) Indication, 5) Diagnosed opioid dependency, 6) Benzodiapines, 7) Medication Changes, 8) Central Nervous System-related, and 9) Social Determinants of Health. We explored two state-of-the-art natural language processing (NLP) models (finetuning pretrained language models and prompt-tuning approaches) to identify ORAB. Experimental results show that the prompt-tuning models outperformed the finetuning models in most cateogories and the gains were especially higher among uncommon categories (Suggested aberrant behavior, Diagnosed opioid dependency and Medication change). Although the best model achieved the highest 83.92% on area under precision recall curve, uncommon classes (Suggested Aberrant Behavior, Diagnosed Opioid Dependence, and Medication Change) still have a large room for performance improvement.

By that same logic, when Hollywood actors start tweeting about a once-obscure part of artificial intelligence (A.I.), you know that something big is happening, too. That's exactly what occurred recently when Zach Braff, the actor-director still best known for his performance as J.D. on the medical comedy series Scrubs, recorded himself reading a Scrubs-style monolog written by an A.I. "What is a hospital?" Braff reads, adopting the thoughtful tone J.D. used to wrap up each episode in the series. "A hospital is a lot like a high school: the most amazing man is dying, and you're the only one who wants to steal stuff from his dad. Being in a hospital is a lot like being in a sorority. You have greasers and surgeons. And even though it sucks about Doctor Tapioca, not even that's sad."

In late June, the MIT Technology Review reported on the ways that some of the world's largest job search sites--including LinkedIn, Monster, and ZipRecruiter--have attempted to eliminate bias in their artificial intelligence job-interview software. These remedies came after incidents in which A.I. video-interviewing software was found to discriminate against people with disabilities that affect facial expression and exhibit bias against candidates identified as women. When artificial intelligence software produces differential and unequal results for marginalized groups along lines such as race, gender, and socioeconomic status, Silicon Valley rushes to acknowledge the errors, apply technical fixes, and apologize for the differential outcomes. We saw this when Twitter apologized after its image-cropping algorithm was shown to automatically focus on white faces over Black ones and when TikTok expressed contrition for a technical glitch that suppressed the Black Lives Matter hashtag. They claim that these incidents are unintentional moments of unconscious bias or bad training data spilling over into an algorithm--that the bias is a bug, not a feature. But the fact that these incidents continue to occur across products and companies suggests that discrimination against marginalized groups is actually central to the functioning of technology.

For some time now, I have been wanting to write about some of the problems I observed during my time in the Learning Analytics world (which also crosses over into Artificial Intelligence, Personalization, Sentiment Analysis, and many other areas as well). I'm hesitant to do so because I know the pitchforks will come out, so I guess I should point out that all fields have problems. Even my main field of instructional design is far from perfect. Examining issues with in a field (should be) a healthy part of the growth of a field. So this will probably be a series of blog posts as I look at publications, conferences, videos, and other aspects of the LA/PA/ML/AI etc world that are in need of a critical examination.

Are you ready for Skynet? What about synths destroying the colonies of Mars as seen in Picard? With so much fiction bleeding apocalyptic images of artificial intelligence (AI) gone wrong, we'll take a look at some possible scenarios of what could actually happen in the rise of artificial intelligence. While many researchers and computer experts aren't worried, new technologies need risk-assessment. But, some high profile scientists like Elon Musk and the late Stephen Hawking sounded the alarm years ago, and there is some reason for concern.

Joe Kennedy, father of the late President John F. Kennedy, once said that, when shoeshine boys start giving you stock tips, the financial bubble is getting too big for its own good. By that same logic, when Hollywood actors start tweeting about a once-obscure part of artificial intelligence (A.I.), you know that something big is happening, too. That's exactly what occurred recently when Zach Braff, the actor-director still best known for his performance as J.D. on the medical comedy series Scrubs, recorded himself reading a Scrubs-style monolog written by an A.I. Braff reads, adopting the thoughtful tone J.D. used to wrap up each episode in the series. "A hospital is a lot like a high school: the most amazing man is dying, and you're the only one who wants to steal stuff from his dad. Being in a hospital is a lot like being in a sorority. You have greasers and surgeons. And even though it sucks about Doctor Tapioca, not even that's sad."