We present a Julia-based interface to the precompiled HALLaR and cuHALLaR binaries for large-scale semidefinite programs (SDPs). Both solvers are established as fast and numerically stable, and accept problem data in formats compatible with SDPA and a new enhanced data format taking advantage of Hybrid Sparse Low-Rank (HSLR) structure. The interface allows users to load custom data files, configure solver options, and execute experiments directly from Julia. A collection of example problems is included, including the SDP relaxations of the Matrix Completion and Maximum Stable Set problems.

Quantum neural networks (QNN) hold immense potential for the future of quantum machine learning (QML). However, QNN security and robustness remain largely unexplored. In this work, we proposed novel Trojan attacks based on the quantum computing properties in a QNN-based binary classifier. Our proposed Quantum Properties Trojans (QuPTs) are based on the unitary property of quantum gates to insert noise and Hadamard gates to enable superposition to develop Trojans and attack QNNs. We showed that the proposed QuPTs are significantly stealthier and heavily impact the quantum circuits' performance, specifically QNNs. The most impactful QuPT caused a deterioration of 23% accuracy of the compromised QNN under the experimental setup. To the best of our knowledge, this is the first work on the Trojan attack on a fully quantum neural network independent of any hybrid classical-quantum architecture.

Therefore, directly sampling from the models, comparing the methods or becoming acquainted with them may present challenges. To mitigate this issue we introduce models - contains the models currently implemented Symbotunes, an open-source unified hub for symbolic in our hub. Each model is in a separate sub-directory, music generative models. Symbotunes contains modern which also contains an example training script, Python implementations of well-known methods for data - contains all the data handling utilities available symbolic music generation, as well as a unified pipeline in the hub - datasets, tokenizers, and data transforms.

Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with environments, humans, and other agents using natural language interfaces. We consider language agents as a promising direction towards artificial general intelligence and release Agents, an open-source library with the goal of opening up these advances to a wider non-specialist audience. Agents is carefully engineered to support important features including planning, memory, tool usage, multi-agent communication, and fine-grained symbolic control. Agents is user-friendly as it enables non-specialists to build, customize, test, tune, and deploy state-of-the-art autonomous language agents without much coding. The library is also research-friendly as its modularized design makes it easily extensible for researchers. Agents is available at https://github.com/aiwaves-cn/agents.

Social media is a veritable gold mine of information and a window into the collective psychology of people across the world. Be it politicians, celebrities, creative artists, professors or students - everyone seems to be on Twitter. It has become increasingly popular with tweets from famous personalities influencing millions of followers and the markets too! So Twitter data is used for sentiment analysis in various spheres including trading. This blog will show how we can fetch data from Twitter using the Twitter API.

In this tutorial, you will learn to train a custom image classification network for OAK-D using the TensorFlow framework. Furthermore, this tutorial aims to develop an image classification model that can learn to classify one of the 15 vegetables (e.g., tomato, brinjal, and bottle gourd). If you are a regular PyImageSearch reader and have even basic knowledge of Deep Learning in Computer Vision, then this tutorial should be easy to understand. Furthermore, this tutorial acts as a foundation for the following tutorial, where we learn to deploy this trained image classification model on OAK-D. To learn how to train an image classification network for OAK-D, just keep reading. Before we start data loading, analysis, and training the classification network on the data, we must carefully pick the suitable classification architecture as it would finally be deployed on the OAK. Although OAK can process 4 trillion operations per second, it is still an edge device.

It contains car images with damages. It can be used to train a model to detect damages on cars and car parts. The dataset has already been annotated, and the corresponding COCO files are provided. If you have a custom dataset you'd like to use, then you have to do the labeling and annotation yourself. There are many tools and online platforms that can help you achieve this.

Almost everything we see around us today comes from factories. However, manufacturing as we see it today is mostly outdated. Manufacturers spend up to 15–20% of their sales revenue due to the cost of poor quality (COPQ) [link]. This includes the cost of detecting and preventing product failures. The later a defect is detected, the more resources have been wasted on the defective part.

Working on a machine learning project means we need to experiment. Having a way to configure your script easily will help you move faster. In Python, we have a way to adapt the code from command line. In this tutorial, we are going to see how we can leverage the command line arguments to a Python script to help you work better in your machine learning project. There are many ways to run a Python script.

Going from notebooks to creating machine learning systems that work in the real-world means shifting context from writing simple scripts, notebooks and visualizing data in the lab environment. Now it's the time to think about building a system and good systems have key characteristics namely resilience, performance and reliability. Often, as we design systems the frictional drag of complexity starts to become apparent due to incremental decisions taken during the developmental process. These incremental decisions are a result of several constraints such as deadlines, budget constraints, and technical skills of the development team. Complexity in the context of a system is anything that makes it hard to understand the system and makes it difficult to modify the system at a later stage.