This paper investigates the fragility of contribution evaluation in federated learning, a critical mechanism for ensuring fairness and incentivizing participation. We argue that contribution scores are susceptible to significant distortions from two fundamental perspectives: architectural sensitivity and intentional manipulation. First, we explore how different model aggregation methods impact these scores. While most research assumes a basic averaging approach, we demonstrate that advanced techniques, including those designed to handle unreliable or diverse clients, can unintentionally yet significantly alter the final scores. Second, we explore vulnerabilities posed by poisoning attacks, where malicious participants strategically manipulate their model updates to inflate their own contribution scores or reduce the importance of other participants. Through extensive experiments across diverse datasets and model architectures, implemented within the Flower framework, we rigorously show that both the choice of aggregation method and the presence of attackers are potent vectors for distorting contribution scores, highlighting a critical need for more robust evaluation schemes.

This paper shows that the semantics of programs with aggregates implemented by the solvers clingo and dlv can be characterized as extended First-Order formulas with intensional functions in the logic of Here-and-There. Furthermore, this characterization can be used to study the strong equivalence of programs with aggregates under either semantics. We also present a transformation that reduces the task of checking strong equivalence to reasoning in classical First-Order logic, which serves as a foundation for automating this procedure.

The study presents a novel approach for quantifying cellular interactions in digital pathology using deep learning-based image cytometry. Traditional methods struggle with the diversity and heterogeneity of cells within tissues. To address this, we introduce the Spatial Interaction Potential (SIP) and the Co-Localization Index (CLI), leveraging deep learning classification probabilities. SIP assesses the potential for cell-to-cell interactions, similar to an electric field, while CLI incorporates distances between cells, accounting for dynamic cell movements. Our approach enhances traditional methods, providing a more sophisticated analysis of cellular interactions. We validate SIP and CLI through simulations and apply them to colorectal cancer specimens, demonstrating strong correlations with actual biological data. This innovative method offers significant improvements in understanding cellular interactions and has potential applications in various fields of digital pathology.

I bet that you have already seen in movies the IT guy hacking a system by writing commands inside a black window and thought "How cool is that!". Well, in reality, things are not that easy to hack but we do have some basic commands that can help interact with the computer, which is called command-line interface (CLI). The command-line interface is a program on your computer that allows you to create and delete files, run programs, and navigate through folders and files. On a Mac and Linux Systems, it's called Terminal, and on Windows, it's Command Prompt. CLI is not just a fancy method to interact with your computer.

Python is one of the most popular and widely known programming languages that has replaced many programming languages in the industry. It is one of the most loved programming languages that data science professionals use more because it is an ocean of libraries. Python is known as the beginner's level programming language because of its simplicity and easiness, its programming syntax is simple to learn and is of high level compared to C, Java, and C . Pytorch is an open source library, it basically a replacement of Numpy. PyTorch comes with higher-level functionality useful for building a deep neural network.

This paper reports on the open source project CLAI (Command Line AI), aimed at bringing the power of AI to the command line interface. The platform sets up the CLI as a new environment for AI researchers to conquer by surfacing the command line as a generic environment that researchers can interface to using a simple sense-act API much like the traditional AI agent architecture. In this paper, we discuss the design and implementation of the platform in detail, through illustrative use cases of new end user interaction patterns enabled by this design, and through quantitative evaluation of the system footprint of a CLAI-enabled terminal. We also report on some early user feedback on its features from an internal survey.

At this point, it should be a surprise to no one that Python is the most popular language for machine learning projects. While languages like R, C, and Julia have their proponents--and use cases--Python remains the most universally embraced language, being used in every major machine learning framework. So, naturally, our codebase at Cortex--an open source platform for deploying machine learning models as APIs--is 87.5% Go. Machine learning algorithms, where Python shines, are just one component of a production machine learning system. Cortex is built to automate all of this infrastructure, along with other concerns like logging and cost optimizations. A user can have many different models deployed as distinct APIs, all managed in the same Cortex cluster.

Trick-taking card games feature a large amount of private information that slowly gets revealed through a long sequence of actions. This makes the number of histories exponentially large in the action sequence length, as well as creating extremely large information sets. As a result, these games become too large to solve. To deal with these issues many algorithms employ inference, the estimation of the probability of states within an information set. In this paper, we demonstrate a Policy Based Inference (PI) algorithm that uses player modelling to infer the probability we are in a given state. We perform experiments in the German trick-taking card game Skat, in which we show that this method vastly improves the inference as compared to previous work, and increases the performance of the state-of-the-art Skat AI system Kermit when it is employed into its determinized search algorithm.

Okay so we've installed the Ubuntu partition last week, and now we're going to install the neural network Deep Style. This is where stuff is probably the most difficult. I'm going to equip you with the tools to solve those problems. When you use a program such as your internet browser, or Photoshop you are using a Graphical User Interface. Before GUI there were CLI .

You have your Tensorflow code running locally. Now you want to set it up in a production environment for all that extra GPU Power. There are a couple of alternatives out there. The two more popular managed ML cloud platforms are Google Cloud ML Engine and AWS Sage Maker. They let you quickly deploy your models and train them.