Objective: This paper investigates the potential of learnable activation functions in Kolmogorov-Arnold Networks (KANs) for personalized control in a lower-limb prosthesis. In addition, user-specific vs. pooled training data is evaluated to improve machine learning (ML) and Deep Learning (DL) performance for turn intent prediction. Method: Inertial measurement unit (IMU) data from the shank were collected from five individuals with lower-limb amputation performing turning tasks in a laboratory setting. Ability to classify an upcoming turn was evaluated for Multilayer Perceptron (MLP), Kolmogorov-Arnold Network (KAN), convolutional neural network (CNN), and fractional Kolmogorov-Arnold Networks (FKAN). The comparison of MLP and KAN (for ML models) and FKAN and CNN (for DL models) assessed the effectiveness of learnable activation functions. Models were trained separately on user-specific and pooled data to evaluate the impact of training data on their performance. Results: Learnable activation functions in KAN and FKAN did not yield significant improvement compared to MLP and CNN, respectively. Training on user-specific data yielded superior results compared to pooled data for ML models ($p < 0.05$). In contrast, no significant difference was observed between user-specific and pooled training for DL models. Significance: These findings suggest that learnable activation functions may demonstrate distinct advantages in datasets involving more complex tasks and larger volumes. In addition, pooled training showed comparable performance to user-specific training in DL models, indicating that model training for prosthesis control can utilize data from multiple participants.

This paper presents a deep learning method using Natural Language Processing (NLP) techniques, to distinguish between Mild Cognitive Impairment (MCI) and Normal Cognitive (NC) conditions in older adults. We propose a framework that analyzes transcripts generated from video interviews collected within the I-CONECT study project, a randomized controlled trial aimed at improving cognitive functions through video chats. Our proposed NLP framework consists of two Transformer-based modules, namely Sentence Embedding (SE) and Sentence Cross Attention (SCA). First, the SE module captures contextual relationships between words within each sentence. Subsequently, the SCA module extracts temporal features from a sequence of sentences. This feature is then used by a Multi-Layer Perceptron (MLP) for the classification of subjects into MCI or NC. To build a robust model, we propose a novel loss function, called InfoLoss, that considers the reduction in entropy by observing each sequence of sentences to ultimately enhance the classification accuracy. The results of our comprehensive model evaluation using the I-CONECT dataset show that our framework can distinguish between MCI and NC with an average area under the curve of 84.75%.

The onshoring and buildout of dozens of fabs, many costing tens of billions of dollars, is beginning to spill over into other areas that are critical for chip manufacturing. Materials, in particular, which often gets little attention outside of chip manufacturing, witnessed a big spike in September 2022. In fact, seven materials companies covered in this report made up more than a third of the month's total reported investments, with three of the companies garnering more than $200 million. Other investment targets were sputtering equipment and evaporation materials for deposition, high-purity polycrystalline silicon, fluorine-containing electronic gases, and silicon carbide. In the AI hardware arena, numerous startups are focusing on in-memory and near-memory compute, reducing the volume of data that needs to be moved back and forth between memory and processing elements. Novel architectures also are appearing, such as one that uses sparse mathematics.

At Pearson, we're committed to a world that's always learning and to our talented team who make it all possible. By embracing a massive digital transformation that includes highly experiential and personalized learning, we are always re-examining and continuously improving the way people learn best, whether it's one child in our own backyard or an education community across the globe. We are bold thinkers and standout innovators who are mission-driven and motivate each other to explore new frontiers in an environment that supports and inspires us to always be better. We are currently looking for a hands-on Senior Manager to lead our Machine Learning Engineering team to work with our new and existing learning product platforms. The Senior Manager, Machine Learning Engineering will report to the Director of Adaptivity and Machine Learning Engineering and work with cross product and engineering teams to build real-time adaptive and personalized learning services that optimize learner interactions and behavior to allow more efficient and effective study and engagement.

One of the biggest news items in the digital workplace this week was the announcement of IBM and Salesforce's deepening partnership, with new integrations between IBM Cloud, Watson Services, Salesforce Quip and Service Cloud Einstein. The two companies announced their initial partnership last March. It envisaged a combination of Armonk, New York-based IBM Watson's capabilities with San Francisco-based Salesforce's CRM data to allow businesses to glean insights across structured and unstructured data. As part of this announcement, Salesforce named IBM a preferred cloud services provider, making IBM the third major provider to be named "preferred." The company first named AWS its preferred cloud provider in May 2016.

Recent studies have shown that information mined from Craigslist can be used for informing public health policy or monitoring risk behavior. This paper presents a text-mining method for conducting public health surveillance of marijuana use concerns in the U.S. using online classified ads in Craigslist. We scraped more than 200 thousands of rental ads in the housing categories in Craigslist and devised text-mining methods for efficiently and accurately extract rental ads associated with concerns about the uses of marijuana in different states across the U.S. We linked the extracted ads to their geographic locations and computed summary statistics of the ads having marijuana use concerns. Our data is then compared with the State Marijuana Laws Map published by the U.S. government and marijuana related keywords search in Google to verify our collected data with respect to the demographics of marijuana use concerns. Our data not only indicates strong correlations between Craigslist ads, Google search and the State Marijuana Laws Map in states where marijuana uses are legal, but also reveals some hidden world of marijuana use concerns in other states where marijuana use is illegal. Our approach can be utilized as a marijuana surveillance tool for policy makers to develop public health policy and regulations.

Uber and Lyft may have changed lives in the Big American City, but they're hardly ubiquitous. Just 15 percent of Americans use these services, according to the Pew Research Center. One-third have never heard of them. The ridesharing giants do have an excellent way to build a bigger, less urban customer base: teaming up with government. In Florida, in New Jersey, and in Colorado, Uber and Lyft have partnered with municipalities to solve first-mile, last-mile problems, ferrying riders to bus stops, train stations, or even their homes for subsidized fares.

Pearson has one defining goal: to help people progress in their lives through learning. We champion innovation and we invest in models for education that deliver on our promise for effective, accessible, and personal learning from early literacy, college and career readiness to professional education, through data informed instruction and inventive applications for mobile and digital learning. Pearson, the world's leading learning company, has global-reach and market leading businesses in education, business, and consumer publishing and is listed on the London and New York stock exchanges (UK: PSON; NYSE: PSO). Pearson is an Equal Opportunity and Affirmative Action Employer, and a member of E-Verify. All qualified applicants, including minorities, women, veterans, and people with disabilities are encouraged to apply.

Former Indy Racing League driver Sam Schmidt has done a lot in the 16 years since an accident left him paralyzed from the neck down. He runs a racing team and a foundation. He's raced a sailboat using his chin. But the man who raced in the Indianapolis 500 hasn't been able to drive around his neighborhood -- until now. On Wednesday, Schmidt is set to receive the first license restricted to an autonomous vehicle in the U.S. The license allows him to drive on Nevada roads in his specially modified Corvette, which requires no hands on its steering wheel or feet on its pedals.

US Transportation Secretary Anthony Foxx delivers an announcement in Washington, DC, in 2014. Automakers and ride-hail companies are racing to put self-driving cars on the road. In a few weeks, Uber passengers in Pittsburgh will be able to hail self-driving Volvos. Last month, Tesla announced its hopes to build an autonomous ride-hailing fleet. And this month, Ford said it plans to mass-produce autonomous vehicles by 2021.