For example, here is a print out of the attributes of a single battery. VOLTAGE: 11.1 WEIGHT: 0.23 WIDTH: 35.0 Design Sequence As described in the paper, we convert the design tree into a design sequence which we find to be useful for sequence based machine learning approaches. Here we show an example sequence that corresponds to the tree above. Point Cloud One can easily convert the STL file into a point cloud Here is one we made earlier and have included in the data set.

Language-based agentic systems have shown great promise in recent years, transitioning from solving small-scale research problems to being deployed in challenging real-world tasks. However, optimizing these systems often requires substantial manual labor. Recent studies have demonstrated that these systems can be represented as computational graphs, enabling automatic optimization. Despite these advancements, most current efforts in Graph-based Agentic System Optimization (GASO) fail to properly assign feedback to the system's components given feedback on the system's output. To address this challenge, we formalize the concept of semantic backpropagation with semantic gradients -- a generalization that aligns several key optimization techniques, including reverse-mode automatic differentiation and the more recent TextGrad by exploiting the relationship among nodes with a common successor. This serves as a method for computing directional information about how changes to each component of an agentic system might improve the system's output. To use these gradients, we propose a method called semantic gradient descent which enables us to solve GASO effectively. Our results on both BIG-Bench Hard and GSM8K show that our approach outperforms existing state-of-the-art methods for solving GASO problems. A detailed ablation study on the LIAR dataset demonstrates the parsimonious nature of our method. A full copy of our implementation is publicly available at https://github.com/HishamAlyahya/semantic_backprop

The escalating complexity of modern computing frameworks has resulted in a surge in the cybersecurity vulnerabilities reported to the National Vulnerability Database (NVD) by practitioners. Despite the fact that the stature of NVD is one of the most significant databases for the latest insights into vulnerabilities, extracting meaningful trends from such a large amount of unstructured data is still challenging without the application of suitable technological methodologies. Previous efforts have mostly concentrated on software vulnerabilities; however, a holistic strategy incorporates approaches for mitigating vulnerabilities, score prediction, and a knowledge-generating system that may extract relevant insights from the Common Weakness Enumeration (CWE) and Common Vulnerability Exchange (CVE) databases is notably absent. As the number of hardware attacks on Internet of Things (IoT) devices continues to rapidly increase, we present the Hardware Vulnerability to Weakness Mapping (HW-V2W-Map) Framework, which is a Machine Learning (ML) framework focusing on hardware vulnerabilities and IoT security. The architecture that we have proposed incorporates an Ontology-driven Storytelling framework, which automates the process of updating the ontology in order to recognize patterns and evolution of vulnerabilities over time and provides approaches for mitigating the vulnerabilities. The repercussions of vulnerabilities can be mitigated as a result of this, and conversely, future exposures can be predicted and prevented. Furthermore, our proposed framework utilized Generative Pre-trained Transformer (GPT) Large Language Models (LLMs) to provide mitigation suggestions.

When extracting structured data from repetitively organized documents, such as dictionaries, directories, or even newspapers, a key challenge is to correctly segment what constitutes the basic text regions for the target database. Traditionally, such a problem was tackled as part of the layout analysis and was mostly based on visual clues for dividing (top-down) approaches. Some agglomerating (bottom-up) approaches started to consider textual information to link similar contents, but they required a proper over-segmentation of fine-grained units. In this work, we propose a new pragmatic approach whose efficiency is demonstrated on 19th century French Trade Directories. We propose to consider two sub-problems: coarse layout detection (text columns and reading order), which is assumed to be effective and not detailed here, and a fine-grained entry separation stage for which we propose to adapt a state-of-the-art Named Entity Recognition (NER) approach. By injecting special visual tokens, coding, for instance, indentation or breaks, into the token stream of the language model used for NER purpose, we can leverage both textual and visual knowledge simultaneously. Code, data, results and models are available at https://github.com/soduco/paper-entryseg-icdar23-code, https://huggingface.co/HueyNemud/ (icdar23-entrydetector* variants)

This is a key task in my opinion while writing code because you are not really doing regular typing when coding -- at least you shouldn't. What we are doing is going up & down in the file, copying some stuff, pasting it on top of something and changing some of the arguments etc. It involves much more repetitive tasks compared to writing a post or an essay. Hence, we can use shortcuts for those repetitive tasks. Start using Kite or Jedi or other code completion tools (Kite doesn't let new downloads at the moment for some reason though).

Python is an object oriented programming language. It has become one of the significant languages of the world because whether be its machine learning or AI or its web development, each and every feature of python makes it important. It is used by many large companies like Google or YouTube for their many projects. This is why the need to learn the python programming language has emerged. If you are a beginner and struggling with what is python, why should you learn python and other significant details about it then don't panic this article is made about python programming for the absolute beginner.

A group of hackers have built a robot that can crack a safe. A team from SparkFun Electronics in Colorado took their robot to the underground hacking convention, Def Con in Las Vegas. They bought a SentrySafe safe the day before the demonstration and opened it onstage Friday. The robot took about 30 minutes to crack the safe, discovering the combination was 51.36.93. The audience clapped and cheered when the safe was opened.