Hyperspectral imaging (HSI) provides rich spectral information for medical imaging, yet encounters significant challenges due to data limitations and hardware variations. We introduce SAMSA, a novel interactive segmentation framework that combines an RGB foundation model with spectral analysis. SAMSA efficiently utilizes user clicks to guide both RGB segmentation and spectral similarity computations. The method addresses key limitations in HSI segmentation through a unique spectral feature fusion strategy that operates independently of spectral band count and resolution. Performance evaluation on publicly available datasets has shown 81.0% 1-click and 93.4% 5-click DICE on a neurosurgical and 81.1% 1-click and 89.2% 5-click DICE on an intraoperative porcine hyperspectral dataset. Experimental results demonstrate SAMSA's effectiveness in few-shot and zero-shot learning scenarios and using minimal training examples. Our approach enables seamless integration of datasets with different spectral characteristics, providing a flexible framework for hyperspectral medical image analysis.

In this paper, we will present SketchQL, a video database management system (VDBMS) for retrieving video moments with a sketch-based query interface. This novel interface allows users to specify object trajectory events with simple mouse drag-and-drop operations. Users can use trajectories of single objects as building blocks to compose complex events. Using a pre-trained model that encodes trajectory similarity, SketchQL achieves zero-shot video moments retrieval by performing similarity searches over the video to identify clips that are the most similar to the visual query. In this demonstration, we introduce the graphic user interface of SketchQL and detail its functionalities and interaction mechanisms. We also demonstrate the end-to-end usage of SketchQL from query composition to video moments retrieval using real-world scenarios.

The recently released Segment Anything Model (SAM) has shown powerful zero-shot segmentation capabilities through a semi-automatic annotation setup in which the user can provide a prompt in the form of clicks or bounding boxes. There is growing interest around applying this to medical imaging, where the cost of obtaining expert annotations is high, privacy restrictions may limit sharing of patient data, and model generalisation is often poor. However, there are large amounts of inherent uncertainty in medical images, due to unclear object boundaries, low-contrast media, and differences in expert labelling style. Currently, SAM is known to struggle in a zero-shot setting to adequately annotate the contours of the structure of interest in medical images, where the uncertainty is often greatest, thus requiring significant manual correction. To mitigate this, we introduce \textbf{Sim}ulated Interaction for \textbf{S}egment \textbf{A}nything \textbf{M}odel (\textsc{\textbf{SimSAM}}), an approach that leverages simulated user interaction to generate an arbitrary number of candidate masks, and uses a novel aggregation approach to output the most compatible mask. Crucially, our method can be used during inference directly on top of SAM, without any additional training requirement. Quantitatively, we evaluate our method across three publicly available medical imaging datasets, and find that our approach leads to up to a 15.5\% improvement in contour segmentation accuracy compared to zero-shot SAM. Our code is available at \url{https://github.com/BenjaminTowle/SimSAM}.

A research initiative from the US, Australia and China has identified a new strain of click fraud, dubbed'Humanoid Attack' that slips past conventional detection frameworks, and exploits real-life user interactions in mobile apps in order to generate revenue from fake clicks on embedded third-party framework advertisements. The paper, led by Shanghai Jiao Tong University, contends that this new variation on click fraud is already widely diffused, and identifies 157 infected apps out of the top-rated 20,000 apps across the Google Play and Huawei app markets. One HA-infected social and communication app discussed in the study is reported to have 570 million downloads. The report notes that four other apps'produced by the same company are manifested to have similar click fraud codes'. To detect apps which feature Humanoid Attack (HA), the researchers developed a tool entitled ClickScanner, which generates data dependency graphs, based on static analysis, from bytecode-level inspection of Android apps.

I am creating the web deployment for a book I am writing for Manning Publications on deep learning with structured data. The audience for this book is interested in how to deploy a simple deep learning model. They need a deployment example that is straightforward and doesn't force them to wade through a bunch of web programming details. For this reason, I wanted a web deployment solution that kept as much of the coding as possible in Python. With this in mind, I looked at two Python-based options for web deployment: Flask and Django.

Online learning to rank is a core problem in machine learning. In Lattimore et al. (2018), a novel online learning algorithm was proposed based on topological sorting. In the paper they provided a set of self-normalized inequalities (a) in the algorithm as a criterion in iterations and (b) to provide an upper bound for cumulative regret, which is a measure of algorithm performance. In this work, we utilized method of mixtures and asymptotic expansions of certain implicit function to provide a tighter, iterated-log-like boundary for the inequalities, and as a consequence improve both the algorithm itself as well as its performance estimation.

With the information explosion of news articles, personalized news recommendation has become important for users to quickly find news that they are interested in. Existing methods on news recommendation mainly include collaborative filtering methods which rely on direct user-item interactions and content based methods which characterize the content of user reading history. Although these methods have achieved good performances, they still suffer from data sparse problem, since most of them fail to extensively exploit high-order structure information (similar users tend to read similar news articles) in news recommendation systems. In this paper, we propose to build a heterogeneous graph to explicitly model the interactions among users, news and latent topics. The incorporated topic information would help indicate a user's interest and alleviate the sparsity of user-item interactions. Then we take advantage of graph neural networks to learn user and news representations that encode high-order structure information by propagating embeddings over the graph. The learned user embeddings with complete historic user clicks capture the users' long-term interests. We also consider a user's short-term interest using the recent reading history with an attention based LSTM model. Experimental results on real-world datasets show that our proposed model significantly outperforms state-of-the-art methods on news recommendation.

The last decade was spent building products that were cloud native and mobile native, and this disrupted several industries and changed the way we live. Mobile made it possible for entrepreneurs to use camera and location to build products such as Instagram or Uber. As we look to the future, it is clear that something very exciting lies beyond cloud-native and mobile-native, and that is AI-native. This also means that entrepreneurs and product managers need to rethink both products and the way products are built to make them AI-native. AI-first products are closer to living things.

Chatbots has become the essential part in digital marketing strategy. They are integrated with virtual assistants & AI giving them an ability of engaging the customer conversations. It plays very crucial role in offering versatile services. It helps customers to answer questions & guide them to their requirements. According to Niko Bonatsos, Managing Director at General Catalyst "90% of our time on mobile is spent on email and messaging platforms. Audience would love to back teams that build stuff for places where the consumers hang out" A Chatbot is a service powered by artificial intelligence & virtual assistant that you can interact by chat interface.

Business is booming at Google's parent company, Alphabet, even as it loses billions of dollars on kooky-sounding projects that may never produce any revenue. Most of the losses are concentrated in Alphabet's'X'' lab, a wellspring of far-out ideas that has become known as a'moonshot factory' since Google co-founder Sergey Brin launched it about six years ago. The lab is responsible for some once-zany projects, such as Google's self-driving cars, that matured into potentially revolutionary technology. FILE - In this Monday, Feb. 1, 2016, file photo, electronic screens post prices of Alphabet stock at the Nasdaq MarketSite in New York. Business is booming at Google¿s parent company, Alphabet Inc., even as it loses billions of dollars on risky projects that may never produce any revenue.