A 2023 law championed by Republicans requires the CDC have a director confirmed by the Senate. For months, though, it's had only acting directors--and the White House won't say when that will change. As the agency rotates through a cast of leaders, it's unclear when--or if--the US Centers for Disease Control and Prevention will get a permanent director under Donald Trump's second term as president. Following Jim O'Neill's departure as acting CDC director last week, National Institutes of Health director Jay Bhattacharya will now lead both agencies temporarily. It's the latest in a series of shakeups at Trump's CDC, which has lost about a quarter of its staff to mass layoffs carried out by Health and Human Services Secretary Robert F. Kennedy, Jr. last year.

We prove that when a PDE's coefficients are representable bysmall neural networks, theparameters required toapproximate its solution scale polynomially with the input dimensiond and proportionally to the parameter counts of the coefficient networks.

Precision agriculture and smart farming are increasingly adopted to improve productivity, reduce input waste, and maintain high product quality under growing demand. These approaches integrate sensing, automation, and data-driven decision-making to improve crop yield and post-harvest quality (Gupta, Abdelsalam, Khorsandroo, and Mittal (2020)). In this context, autonomous robotic harvesting is a key enabling technology for horticulture, where labor shortages and high labor costs directly affect production and consistency. Despite progress in mechanization, many conventional harvesting methods (e.g., combine harvesters, reapers, and trunk shakers) are unsuitable for soft and delicate crops such as tomatoes and strawberries because large contact forces and impacts can bruise or damage the fruit (Cho, Iida, Suguri, Masuda, and Kurita (2014); Shojaei (2021)). Selective harvesting, where fruits are picked individually at the appropriate ripeness stage, is therefore preferred for high-value crops. However, selective harvesting remains challenging because a robot must (i) detect the target fruit under occlusion, (ii) estimate its pose and identify the pedicel cutting location, and (iii) execute grasping and detachment without damaging the fruit or plant. In real cultivation environments, tomatoes are often densely packed and partially occluded by leaves and branches, making perception and reliable manipulation difficult (Chen et al. (2015)). Consequently, integrated harvesting systems that combine compliant end-effectors, robust perception, and closed-loop control remain an active research topic (Comba, Gay, Piccarolo, and Ricauda Aimonino (2010); Ling, Zhao, Gong, Liu, and Wang (2019)). A wide range of end-effectors has been explored for harvesting and handling soft produce.

Attitude stabilization of unmanned aerial vehicles in uncertain environments presents significant challenges due to nonlinear dynamics, parameter variations, and sensor limitations. This paper presents a comparative study of $\mathcal{H}_\infty$ and classical PID controllers for multi-rotor attitude regulation in the presence of wind disturbances and gyroscope noise. The flight dynamics are modeled using a linear parameter-varying (LPV) framework, where nonlinearities and parameter variations are systematically represented as structured uncertainties within a linear fractional transformation formulation. A robust controller based on $\mathcal{H}_\infty$ formulation is designed using only gyroscope measurements to ensure guaranteed performance bounds. Nonlinear simulation results demonstrate the effectiveness of the robust controllers compared to classical PID control, showing significant improvement in attitude regulation under severe wind disturbances.

Textbooks are important for shaping children's understanding of the world. Previous studies of individual countries have suggested that gender inequality exists. There lacks a study that compares gender inequality in textbooks around the world. This study uses NLP approaches to quantify gender inequality in English textbooks in 7 cultural spheres, 22 countries, by measuring the count, firstness, and TF IDF words by gender. The study also counted the names that appeared in TF IDF word lists and sorted the names by gender, found out that LLMs can distinguish between the different TF IDF word lists, and mapped the TF IDF words to GloVe to see that some keywords are closer to one gender than the other. The study found more male count, firstness, and names. The study found that there is significant gender inequality in all the textbooks. Gender inequality is demonstrated the least in textbooks of the Latin Cultural Sphere.

The annual meeting of the International Society for Autism Research took place in Seattle this week. The field's premiere scientific conference was scheduled to be held in the Emerald City five years ago, until COVID-19 dashed those plans. This time, U.S. autism researchers face a very different kind of crisis: massive cuts to federal funding, Cabinet members making false statements about the complex neurological condition they study, and a series of confusing and potentially worrisome policy announcements about autism research. In April, the U.S. Department of Health and Human Services disclosed that it's planning a 50-million "comprehensive research effort aimed at understanding the causes of [autism spectrum disorder] and improving treatments," a department spokesperson said. The effort was spurred by Secretary Robert F. Kennedy Jr.'s stated goal of determining the cause of autism, a neurological and developmental condition whose symptoms cluster around challenges with communication, social interaction and sensory processing.

Representation bias is one of the most common types of biases in artificial intelligence (AI) systems, causing AI models to perform poorly on underrepresented data segments. Although AI practitioners use various methods to reduce representation bias, their effectiveness is often constrained by insufficient domain knowledge in the debiasing process. To address this gap, this paper introduces a set of generic design guidelines for effectively involving domain experts in representation debiasing. We instantiated our proposed guidelines in a healthcare-focused application and evaluated them through a comprehensive mixed-methods user study with 35 healthcare experts. Our findings show that involving domain experts can reduce representation bias without compromising model accuracy. Based on our findings, we also offer recommendations for developers to build robust debiasing systems guided by our generic design guidelines, ensuring more effective inclusion of domain experts in the debiasing process.

In this study, we immerse in the intricate world of patterns, examining the structural details of Indian medieval architecture for the discovery of motifs with great application potential from the mechanical metastructure perspective. The motifs that specifically engrossed us are derived from the tomb of I'timad-ud-Daula, situated in the city of Agra, close to the Taj Mahal. In an exploratory study, we designed nine interlaced metastructures inspired from the tomb's motifs. We fabricated the metastructures using additive manufacturing and studied their vibration characteristics experimentally and numerically. We also investigated bandgap modulation with metallic inserts in honeycomb interlaced metastructures. The comprehensive study of these metastructure panels reveals their high performance in controlling elastic wave propagation and generating suitable frequency bandgaps, hence having potential applications as waveguides for noise and vibration control. Finally, we developed a novel AI-based model trained on numerical datasets for the inverse design of metastructures with a desired bandgap.

With the increasing adoption of Artificial Intelligence (AI) systems in high-stake domains, such as healthcare, effective collaboration between domain experts and AI is imperative. To facilitate effective collaboration between domain experts and AI systems, we introduce an Explanatory Model Steering system that allows domain experts to steer prediction models using their domain knowledge. The system includes an explanation dashboard that combines different types of data-centric and model-centric explanations and allows prediction models to be steered through manual and automated data configuration approaches. It allows domain experts to apply their prior knowledge for configuring the underlying training data and refining prediction models. Additionally, our model steering system has been evaluated for a healthcare-focused scenario with 174 healthcare experts through three extensive user studies. Our findings highlight the importance of involving domain experts during model steering, ultimately leading to improved human-AI collaboration.

In this paper, we present Paramanu-Ganita, a 208 million parameter novel Auto Regressive (AR) decoder based language model on mathematics. The model is pretrained from scratch at context size of 4096 on our curated mixed mathematical corpus. We evaluate our model on both perplexity metric and GSM8k mathematical benchmark. Paramanu-Ganita despite being 35 times smaller than 7B LLMs, outperformed generalist LLMs such as LLaMa-1 7B by 28.4% points, LLaMa-2 7B by 27.6% points, Falcon 7B by 32.6% points, PaLM 8B by 35.3% points, and math specialised LLMs such as Minerva 8B by 23.2% points, and LLEMMA-7B by 3.0% points in GSM8k test accuracy metric respectively. Paramanu-Ganita also outperformed giant LLMs like PaLM 62B by 6.4% points, Falcon 40B by 19.8% points, LLaMa-1 33B by 3.8% points and Vicuna 13B by 11.8% points respectively. The large significant margin improvement in performance of our math model over the existing LLMs signifies that reasoning capabilities of language model are just not restricted to LLMs with humongous number of parameters. Paramanu-Ganita took 146 hours of A100 training whereas math specialised LLM, LLEMMA 7B, was trained for 23,000 A100 hours of training equivalent. Thus, our approach of pretraining powerful domain specialised language models from scratch for domain adaptation is much more cost-effective than performing continual training of LLMs for domain adaptation. Hence, we conclude that for strong mathematical reasoning abilities of language model, we do not need giant LLMs and immense computing power to our end. In the end, we want to point out that we have only trained Paramanu-Ganita only on a part of our entire mathematical corpus and yet to explore the full potential of our model.