Things to Do in L.A. Tap to enable a layout that focuses on the article. These candidates for mayor are long shots. But they hope to lead the city of L.A. Hyman is a hip-hop artist and Grammy-nominated songwriter. This is read by an automated voice. Please report any issues or inconsistencies here .

Our muscles are nature's actuators. The sinewy tissue is what generates the forces that make our bodies move. In recent years, engineers have used real muscle tissue to actuate "biohybrid robots" made from both living tissue and synthetic parts. By pairing lab-grown muscles with synthetic skeletons, researchers are engineering a menagerie of muscle-powered crawlers, walkers, swimmers, and grippers. But for the most part, these designs are limited in the amount of motion and power they can produce.

--High-quality, multi-channel neural recording is indispensable for neuroscience research and clinical applications. Large-scale brain recordings often produce vast amounts of data that must be wirelessly transmitted for subsequent offline analysis and decoding, especially in brain-computer interfaces (BCIs) utilizing high-density intracortical recordings with hundreds or thousands of electrodes. However, transmitting raw neural data presents significant challenges due to limited communication bandwidth and resultant excessive heating. T o address this challenge, we propose a neural signal compression scheme utilizing Convolutional Autoencoders (CAEs), which achieves a compression ratio of up to 150 for compressing local field potentials (LFPs). The CAE encoder section is implemented on RAMAN, an energy-efficient tinyML accelerator designed for edge computing. Additionally, we employ hardware-software co-optimization by pruning the CAE encoder model parameters using a hardware-aware balanced stochastic pruning strategy, resolving workload imbalance issues and eliminating indexing overhead to reduce parameter storage requirements by up to 32.4%. Post layout simulation shows that the RAMAN encoder can be implemented in a TSMC 65-nm CMOS process, occupying a core area of 0.0187 mm Operating at a clock frequency of 2 MHz and a supply voltage of 1.2 V, the estimated power consumption is 15.1 µ W per channel for the proposed DS-CAE1 model. The compressed neural data from RAMAN is reconstructed offline with signal-to-noise and distortion ratios (SNDR) of 22.6 dB and 27.4 dB, along with R2 scores of 0.81 and 0.94, respectively, evaluated on two monkey neural recordings. A. Krishna is with the Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore - 560012, India, and also with the International Centre for Neuromorphic Systems, The MARCS Institute, Western Sydney University, Australia. S. Debnath, M. Srivatsav, M. Mehendale, and C. S. Thakur (Email: csthakur@iisc.ac.in) are with the Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore - 560012, India. A. van Schaik is with the International Centre for Neuromorphic Systems, The MARCS Institute, Western Sydney University, Australia. This work was supported by the Pratiksha Trust grant BCD - FG/SMCH-22-2106 and INAE grant INAE/121/AKF/48 (SAP code - SP/INAE-23-0001). BCIs have emerged as a revolutionary tool for advancing our understanding of the brain and are increasingly being utilized across various clinical applications [5]-[7], providing inventive solutions for communication [8], control [1], [9], and rehabilitation [10]-[13]. Ongoing improvements in signal processing, machine learning algorithms, and neurotechnology pave the way for BCIs to revolutionize healthcare, human-computer interaction, and beyond.

We investigate language generation in the limit - a model by Kleinberg and Mullainathan [NeurIPS 2024] and extended by Li, Raman, and Tewari [COLT 2025]. While Kleinberg and Mullainathan proved generation is possible for all countable collections, Li et al. defined a hierarchy of generation notions (uniform, non-uniform, and generatable) and explored their feasibility for uncountable collections. Our first set of results resolve two open questions of Li et al. by proving finite unions of generatable or non-uniformly generatable classes need not be generatable. These follow from a stronger result: there is a non-uniformly generatable class and a uniformly generatable class whose union is non-generatable. This adds to the aspects along which language generation in the limit is different from traditional tasks in statistical learning theory like classification, which are closed under finite unions. In particular, it implies that given two generators for different collections, one cannot combine them to obtain a single "more powerful" generator, prohibiting this notion of boosting. Our construction also addresses a third open question of Li et al. on whether there are uncountable classes that are non-uniformly generatable and do not satisfy the eventually unbounded closure (EUC) condition introduced by Li, Raman, and Tewari. Our approach utilizes carefully constructed classes along with a novel diagonalization argument that could be of independent interest in the growing area of language generation.

This work proposes an algorithm for explicitly constructing a pair of neural networks that linearize and reconstruct an embedded submanifold, from finite samples of this manifold. Our such-generated neural networks, called Flattening Networks (FlatNet), are theoretically interpretable, computationally feasible at scale, and generalize well to test data, a balance not typically found in manifold-based learning methods. We present empirical results and comparisons to other models on synthetic high-dimensional manifold data and 2D image data. Our code is publicly available.

According to the World Health Organisation, cancer accounted for nearly one in six deaths in 2020. Cancer can occur due to mutations in oncogenes or tumour suppressor genes or both. However, not all mutations result in cancer. Therefore, it is important to identify the genes causing cancer to devise personalised treatment strategies. IIT Madras researchers have developed an artificial intelligence-based tool, 'PIVOT', that can predict cancer-causing genes in an individual.

This synthetic fish is powered by human heart cells. Scientists say that they could help lead the way toward building replacement hearts from human tissue. This synthetic fish is powered by human heart cells. Scientists say that they could help lead the way toward building replacement hearts from human tissue. Scientists have built a school of robotic fish powered by human heart cells.

Data science interviews can be cumbersome, and rejections are merely the beginning. While an academic degree, relevant training, skills, and course work are essential to break into data science, it does not guarantee a job or job satisfaction. When it comes to interviews, there are hundreds of reasons for a company to reject a candidate. Of course, it makes more sense for a company to reject a good candidate than to hire a bad one. But, a talented data science professional stands above all, making sure to stay ahead of the curve.

Three decades after a US university student called Robert Tappan Morris was convicted of launching the first widely known malware attack on the internet, cybercrime has become big business, costing the global economy an estimated £2.1m a minute. Internet service provider Beaming reports that cybercriminals are launching increasingly sophisticated attacks on an "unprecedented scale". The pandemic has exacerbated the situation because it has prompted a sharp rise in remote working, which has enabled them to target vulnerabilities in domestic internet connections to attack corporate systems. In 2020, the average UK business faced 686,961 attempts to breach its systems – 20% up on the previous year's figure – according to Beaming. That equates to an attack every 46 seconds.

IMAGE: ECMO machines such as this one save countless lives, but in some cases can lead to brain injury. A UT Southwestern study used machine learning to accurately predict which babies... view more DALLAS - Oct. 1, 2020 - When newborn babies or children with heart or lung distress are struggling to survive, doctors often turn to a form of life support that uses artificial lungs. This treatment, called Extracorporeal Membrane Oxygenation (ECMO), has been credited with saving countless lives. But in some cases, it can also lead to long-term brain injury. Now, a research team led by UT Southwestern scientists has shown that a machine learning program can predict, more accurately than doctors, which babies and children are most likely to suffer brain injury after ECMO.