There's Never Been a Worse Time to Be Authentic at Work Workers have been told to bring themselves to work, only to be disappointed time and time again, argues author Jodi-Ann Burey in her new book. Jodi-Ann Burey was only two weeks into her new role as an inclusion marketing manager for an outdoor retail company when she was accused of having a "race agenda." Burey, who is Black, was no stranger to workplace hypocrisy; as she sees it, the office is a petri dish where the knotty dynamics of society are concentrated. At the time of the accusation in February 2020, however, all she could do was laugh. "I was like, you knew who I was before you poached me. This is exactly what you wanted me to do," she says over Zoom.

Pain is an inherent part of human existence, manifesting as both physical and emotional experiences, and can be categorized as either acute or chronic. Over the years, extensive research has been conducted to understand the causes of pain and explore potential treatments, with contributions from various scientific disciplines. However, earlier studies often overlooked the role of gender in pain experiences. In this study, we utilized Natural Language Processing (NLP) to analyze and gain deeper insights into individuals' pain experiences, with a particular focus on gender differences. We successfully classified posts into male and female corpora using the Hidden Attribute Model-Convolutional Neural Network (HAM-CNN), achieving an F1 score of 0.86 by aggregating posts based on usernames. Our analysis revealed linguistic differences between genders, with female posts tending to be more emotionally focused. Additionally, the study highlighted that conditions such as migraine and sinusitis are more prevalent among females and explored how pain medication affects individuals differently based on gender.

In recent years, the intersection of Natural Language Processing (NLP) and public health has opened innovative pathways for investigating various domains, including chronic pain in textual datasets. Despite the promise of NLP in chronic pain, the literature is dispersed across various disciplines, and there is a need to consolidate existing knowledge, identify knowledge gaps in the literature, and inform future research directions in this emerging field. This review aims to investigate the state of the research on NLP-based interventions designed for chronic pain research. A search strategy was formulated and executed across PubMed, Web of Science, IEEE Xplore, Scopus, and ACL Anthology to find studies published in English between 2014 and 2024. After screening 132 papers, 26 studies were included in the final review. Key findings from this review underscore the significant potential of NLP techniques to address pressing challenges in chronic pain research. The past 10 years in this field have showcased the utilization of advanced methods (transformers like RoBERTa and BERT) achieving high-performance metrics (e.g., F1>0.8) in classification tasks, while unsupervised approaches like Latent Dirichlet Allocation (LDA) and k-means clustering have proven effective for exploratory analyses. Results also reveal persistent challenges such as limited dataset diversity, inadequate sample sizes, and insufficient representation of underrepresented populations. Future research studies should explore multimodal data validation systems, context-aware mechanistic modeling, and the development of standardized evaluation metrics to enhance reproducibility and equity in chronic pain research.

Abstract-- Human physical function is governed by selfefficacy, the belief in one's motor capacity. In chronic pain patients, this capacity may remain reduced long after the damage causing the pain has been cured. Chronic pain alters body schema, affecting how patients perceive the dimension and pose of their bodies. We exploit this deficit using robotic manipulation technology and augmented sensory stimuli through virtual reality technology. We propose a sensory stimuli manipulation method aimed at modifying body schema to restore lost selfefficacy. Pharmaceuticals alone cannot cure this complex condition, which is influenced by biological, psychological, and social factors [1].

We present an automatic approach that works on resting-state raw EEG data for chronic pain detection. A new feature selection algorithm - modified Sequential Floating Forward Selection (mSFFS) - is proposed. The improved feature selection scheme is rather compact but displays better class separability as indicated by the Bhattacharyya distance measures and better visualization results. It also outperforms selections generated by other benchmark methods, boosting the test accuracy to 97.5% and yielding a test accuracy of 81.4% on an external dataset that contains different types of chronic pain

Instruction-tuned large language models have revolutionized natural language processing and have shown great potential in applications such as conversational agents. These models, such as GPT-4, can not only master language but also solve complex tasks in areas like mathematics, coding, medicine, and law. Despite their impressive capabilities, there is still a lack of comprehensive understanding regarding their full potential, primarily due to the black-box nature of many models and the absence of holistic evaluation studies. To address these challenges, we present INSTRUCTEVAL, a more comprehensive evaluation suite designed specifically for instruction-tuned large language models. Unlike previous works, our evaluation involves a rigorous assessment of models based on problem-solving, writing ability, and alignment to human values. We take a holistic approach to analyze various factors affecting model performance, including the pretraining foundation, instruction-tuning data, and training methods. Our findings reveal that the quality of instruction data is the most crucial factor in scaling model performance. While open-source models demonstrate impressive writing abilities, there is substantial room for improvement in problem-solving and alignment. We are encouraged by the rapid development of models by the open-source community, but we also highlight the need for rigorous evaluation to support claims made about these models. Through INSTRUCTEVAL, we aim to foster a deeper understanding of instruction-tuned models and advancements in their capabilities. INSTRUCTEVAL is publicly available at https://github.com/declare-lab/instruct-eval.

Care and Feeding is Slate's parenting advice column. Have a question for Care and Feeding? Submit it here or post it in the Slate Parenting Facebook group. My husband is very involved with the kids. He's a good father--he does the hard parts of parenting, happily.

The news: Brain signals can be used to detect how much pain a person is experiencing, which could overhaul how we treat certain chronic pain conditions, a new study has suggested. Electrodes implanted in the brains of four people living with chronic pain helped researchers to track their individual levels of pain, and to train an AI model to accurately predict its severity. Why it's important: Because we still don't really understand how chronic pain affects the brain, it's very difficult to treat. This research is the first time a human's chronic-pain-related brain signals have been recorded, and it could aid the development of personalized therapies for the most severe forms of pain. Meta's new AI models can recognize and produce speech for more than 1,000 languages What's happened: Meta has built AI models that can recognize and produce speech for more than 1,000 languages--a tenfold increase on what's currently available.

Signatures of electrical activity have been identified in the brains of people with chronic pain. Although a small study, the discovery could one day lead to more effective treatments. Chronic pain, which lasts longer than 3 months, affects more than 30 per cent of the world's population, with existing therapies often having limited effectiveness. To help in the development of new treatments, Prasad Shirvalkar at the University of California, San Francisco, and his colleagues set out to better understand how the brain regulates pain. The team implanted electrodes and stimulators into the brains of four people with chronic pain as a result of a stroke or amputation.

The link between chronic pain and a loss of appetite may finally be understood – in mice at least. Zhi Zhang at the University of Science and Technology of China in Hefei and his colleagues injected mice with bacteria that provoke chronic pain. Ten days later, these mice were eating less frequently and for shorter periods of time compared with control mice that had been injected with saline. When the first group of mice were later given pain medication, they ate normally, the researchers wrote in a paper published in Nature Metabolism. To better understand the neuronal activity responsible for this change in behaviour, the researchers analysed the brains of the first group of mice while the animals were in chronic pain.