Neuronestics is a company developing AI neurotwins to improve neuromodulation treatments for mental health disorders. Neurotwins are digital copies of a patient's brain created with the help of AI. The company believes that these neurotwins can be used to personalize treatment for patients with epilepsy and depression.
The speaker, Ana M., who is the CEO of Neuronestics, explains that epilepsy and depression are caused by disruptions in the brain's electrical activity. Neuronestics' neuro twin technology uses AI to create a model of a patient's brain based on their MRI and EEG data. This model can then simulate the effects of different neuromodulation treatments, such as transcranial direct current stimulation (tDCS). https://youtu.be/XP-FN-_hAFM?si=a63O9LS6M_LlYFI9
Neuronestics has conducted clinical trials of its neuro-twin technology for epilepsy and depression. In the epilepsy trial, the technology reduced seizures by 44%. In the depression trial, the technology reduced depression symptoms by 63.6%.
The article combines logic and machine learning to explore Combinatorial Binary Models (CBMs) for interpretable predictions. CBMs struggle with complex data due to the high computational cost of learning from long combinations of binary attributes. The authors introduce GRAB, a novel algorithm leveraging frequent itemset mining to learn CBMs efficiently. Experiments show that GRAB tackles complex data and achieves good prediction accuracy while providing valuable insights from the data.
This video is about applying AI technologies to empower smart city development. https://youtu.be/Ftv-5Kj0M3M?si=8r8UmE-DAv6jV1rw
The speaker, Jinang from China Unicon, discusses how AI can improve various aspects of city life, including public services, transportation, and safety.
Jinang outlines four important goals for smart city development:
Inclusiveness: AI solutions should be designed to be accessible and beneficial to all residents, regardless of age, ability, or socioeconomic status.
Safety: AI can be used to improve public safety by automating tasks such as traffic accident detection and emergency response.
Resilience: AI can be used to make cities more resilient to natural disasters and other disruptions.
Sustainability: AI can be used to optimize resource use and promote environmental sustainability.
The article discusses data warehouses and data lakes. It highlights the challenges of storing big data and explores two common storage solutions: data warehouses and data lakes. Data warehouses store structured, filtered data for specific purposes, while data lakes store raw, unfiltered data. Businesses can choose between cloud-based or on-premises storage for their data warehouse. Some important considerations for designing a data warehouse include user needs, data modelling, and choosing a standard architecture.
This research proposes a new method for optimizing pulse patterns in a 3-level NPC Active Front-End (AFE) converter using Selective Harmonic Elimination (SHE-PWM). Unlike other techniques, this Brute-Force (BF) approach tackles two goals: eliminating unwanted low-order harmonics while minimizing specific higher-order ones. It also considers practical limitations like minimum switching times and maintaining a smooth pulse pattern. Compared to other methods, the BF algorithm achieves better results in reducing higher-order harmonics and following constraints. Finally, successful implementation in a low-voltage converter setup validates the approach, suggesting its potential for future use in medium-voltage systems.
AI-driven lawtech startups. It discusses legal tech and the rise of alternative legal services. AI startups are transforming due diligence, legal document scanning, and predictive analytics. Some fear job losses in the legal sector, but others believe AI will make lawyers more efficient. See how they are https://outsideinsight.com/insights/4-ai-driven-lawtech-startups-changing-the-legal-landscape/
This research explores using fuzzy models for clustering constantly changing data (data streams). While fuzzy models aren't perfect (adapting to data shifts and rejecting outliers can be tricky), some methods within this framework can continuously cluster data streams. The possibilistic model shows particular promise. With proper training, it can track changes in the data while staying resistant to outliers, making it robust for this task.
Ada Lovelace's reimagination is about a live performance using generative AI to tell a story. The speaker, Marco Tempest, uses a laptop and a camera to create a real-time storytelling collaboration with AI. The story is about Ada Lovelace, a mathematician and visionary who believed that machines could be used to create art. The video https://youtu.be/0ADkO-9Xo7g?si=5DYmXzsIYoPRxN0m offers a comprehensive explanation.
This research explores genetic clustering methods for classifying satellite images. It compares fixed and automatic cluster number approaches, focusing on a method using "centre-based chromosomes." While effective, this method favours simple-shaped clusters. The paper proposes future directions:
Symmetry-aware distance measures for better cluster identification.
Addressing computational challenges in high-dimensional data.
Applying fuzzy clustering for more robust classification, especially for pixels with uncertain class membership (common at image boundaries).
Giga uses AI and machine learning to identify schools in satellite imagery. They are able to achieve over 90% accuracy using this method. https://youtu.be/SPmthpl4tHI?si=tpraEIFvOlA8krYS
This research examines how the arrangement of disordered spins affects a specific electrical property called anomalous Hall conductivity. They propose three distinct categories for scattering processes based on their influence on this conductivity. Each category has a unique signature. When a material experiences multiple types of scattering, the overall effect depends on the relative strengths of each. The study emphasizes that this classification scheme can be applied to various scattering mechanisms found in real materials. Interestingly, they discovered that magnon scattering (a specific type) has a different impact than typical impurity or phonon scattering. Additionally, this influence can be adjusted by factors like temperature.
Financial inclusion through AI https://youtu.be/fttrD0A4Vz4?si=FUpXz5gBqym3Tgd5
Finda leverages AI to promote financial inclusion, aligning with the UN's goal of economic growth for all. Their AI helps underserved individuals access loans by streamlining the process. This talk will explore the challenges and successes of such inclusive financial services, along with Finda's vision for a future where everyone has access to financial resources.
Consider IonQ if you're looking to explore the potential of quantum computing. This US-based company offers access to trapped-ion quantum computers through a cloud platform. This means you can run programs on their powerful machines without needing the physical hardware. IonQ goes beyond just hardware, though. They understand the importance of education and provide various resources and workshops to help users understand and leverage the capabilities of quantum computing. Visit their website (https://ionq.com/) to learn more and see if IonQ's cloud-based trapped-ion technology can accelerate your quantum computing endeavours.
Large-scale data mining often faces a roadblock: collecting all the data can be impossible due to limitations like bandwidth or processing power. This research tackles this problem specifically for anomaly detection, where spotting unusual patterns is crucial.
The authors propose a framework inspired by compressed sensing. They use random projections to shrink the data while keeping its essential characteristics. This compressed data allows them to detect anomalies directly, bypassing the need to collect everything. This approach is particularly useful for large sensor networks where complete data acquisition might be impractical.
The researchers tested their method extensively using network data and even real surveillance footage from train stations. The results are promising: the method is not only scalable but also performs competitively with traditional anomaly detection techniques with complete data access. This suggests a valuable approach for anomaly detection in situations where complete data collection is a challenge.
Journal Targeter is a free and open-source web application designed to assist researchers in finding suitable journals to publish their manuscripts. It accomplishes this by searching for relevant journals based on the manuscript's title, abstract, and references. The tool was created by the Townsend Lab, and the source code is freely available on GitHub for anyone to inspect or modify.
jot.publichealth.yale.edu
讨论圈