Network alignment has attracted widespread attention in various fields. However, most existing works mainly focus on the problem of label sparsity, while overlooking the issue of noise in network alignment, which can substantially undermine model performance. Such noise mainly includes structural noise from noisy edges and labeling noise caused by human-induced and process-driven errors. To address these problems, we propose RANA, a Robust Active learning framework for noisy Network Alignment. RANA effectively tackles both structure noise and label noise while addressing the sparsity of anchor link annotations, which can improve the robustness of network alignment models. Specifically, RANA introduces the proposed Noise-aware Selection Module and the Label Denoising Module to address structural noise and labeling noise, respectively. In the first module, we design a noise-aware maximization objective to select node pairs, incorporating a cleanliness score to address structural noise. In the second module, we propose a novel multi-source fusion denoising strategy that leverages model and twin node pairs labeling to provide more accurate labels for node pairs. Empirical results on three real-world datasets demonstrate that RANA outperforms state-of-the-art active learning-based methods in alignment accuracy. Our code is available at https://github.com/YXNan0110/RANA.

Large Language Models (LLMs) are computationally intensive, particularly during inference. Neuron-adaptive techniques, which selectively activate neurons in Multi-Layer Perceptron (MLP) layers, offer some speedups but suffer from limitations in modern Transformers. These include reliance on sparse activations, incompatibility with attention layers, and the use of costly neuron masking techniques. To address these issues, we propose the Adaptive Rank Allocation framework and introduce the Rank and Neuron Allocator (RaNA) adapter. RaNA adapters leverage rank adapters, which operate on linear layers by applying both low-rank matrix decompositions and adaptive masking to efficiently allocate compute without depending on activation sparsity. This enables RaNA to be generally applied to MLPs and linear components of attention modules, while eliminating the need for expensive maskers found in neuron-adaptive methods. Notably, when compared to neuron adapters, RaNA improves perplexity by up to 7 points and increases accuracy by up to 8 percentage-points when reducing FLOPs by 44% in state-of-the-art Transformer architectures. As Large Language Models (LLMs) have grown in popularity and size, they have begun consuming a non-trivial amount of compute and time for training and inference (Kim et al. (2023), Pope et al. (2022)). Adaptive compute methods seek to speed up the inference stage of Transformers (Vaswani et al. (2023)), the de facto LLM architecture, by identifying and avoiding redundant computations to save I/O and floating-point operations (FLOPs).

Few-shot knowledge graph completion (FKGC) task aims to predict unseen facts of a relation with few-shot reference entity pairs. Current approaches randomly select one negative sample for each reference entity pair to minimize a margin-based ranking loss, which easily leads to a zero-loss problem if the negative sample is far away from the positive sample and then out of the margin. Moreover, the entity should have a different representation under a different context. To tackle these issues, we propose a novel Relation-Aware Network with Attention-Based Loss (RANA) framework. Specifically, to better utilize the plentiful negative samples and alleviate the zero-loss issue, we strategically select relevant negative samples and design an attention-based loss function to further differentiate the importance of each negative sample. The intuition is that negative samples more similar to positive samples will contribute more to the model. Further, we design a dynamic relation-aware entity encoder for learning a context-dependent entity representation. Experiments demonstrate that RANA outperforms the state-of-the-art models on two benchmark datasets.

For Henkel, a chemical and consumer goods company, using analytics to gain insight into the supply chain has become hard-coded into its DNA over the past several years. With $20 billion in sales, the Düsseldorf, Germany-based company has three divisions: adhesive technologies, laundry and home care, and beauty care. Like other organizations, Henkel began a supply chain digitization journey several years ago, primarily to curb costs and become more efficient. But its initial foray into deploying Internet of Things (IoT) sensors to track various aspects of its supply chain – from manufacture and product supply to truck logistics and customer demand – gave way to a digital way of doing business that is automated, data-driven and agile in the face of rapid change. As moderator of a session on supply chain digitization at the Industrial AI Summit, Richard Self noted that Henkel's digitization brought the kind of ROI that companies seek when they introduce sensors, analytics dashboards and IoT applications into their environments.

Software developers and engineers continue to write or run scripts to glue together components into workflows, even though it is a time-consuming task. However, adopting machine learning or other new technologies that could replace these tried-and-tested scripts can prove to be a challenge for many. In other words, it can be difficult to convince engineers to change how they work. In this edition of The New Stack Makers podcast, Tiffany Jachja, evangelist for software delivery platform provider Harness and Rajsi Rana, senior product manager, Oracle Cloud, discuss scripting and how machine learning, CI/CD and other processes can help guide a shift in engineering culture to make the most of time and resources. Alex Williams, founder and publisher of The New Stack, hosted this episode.

It's no secret that an unequal number of women in leadership roles remains a huge topic of discussion – and it should. I've been reflecting on the PR industry paradox: despite the fact that women make up between 60 to 80 percent of the PR workforce, only 20 percent of the senior leadership positions at PR agencies are held by women. March serves the tech industry specifically because of our fascination with and belief in the power of technology to shape lives; but tech can only serve a population as diverse as its designers. Last week, March supported our client Affectiva at their third Emotion AI summit, which explored a human-centric approach to AI with leaders from across the industry, 43 percent of which were women. Diversity in AI teams was an overarching theme throughout the day, and Rudina Seseri, Founder and Managing Partner of Glasswing Ventures, poignantly highlighted the need to mind gender disparity in technology.

Their work is published in Environmental Research Letters. At the southern tip of the Himalayas, farmers in Himachal Pradesh graze cattle among rolling hills and forests. While policies to manage the region's forest have been but in place by the Indian government, the impact that these policies have had remains unclear. In this study, scientists led by Dr. Pushpendra Rana at the University of Illinois applied machine learning algorithms to examine natural resources policy and governance, evaluating how policies actually work on the ground. Using satellite images from NASA, Rana's machine learning algorithm was able to simultaneously evaluate policy effectiveness in over 200 forest management regions in Kangra, covering a 14-year period.

When I started my first business in the mid-90's I did what most first-time entrepreneurs do -- I ordered business cards. Actually, I first had to get an address and order a phone. Then it was setting up an accounting system, doing the legal paperwork, building a website, and, of course, writing a really long business plan. I did everything except the things I should have been doing: telling my story and selling my solution. But as is so often the case, I got too caught up in the mechanics and lost sight of my purpose.

Rana El Kaliouby is cofounder and CEO of Affectiva, the pioneer in emotion-aware technology--the next frontier of artificial intelligence. Rana invented the company's award-winning emotion recognition technology, built on an emotion AI science platform that uses deep learning and the world's largest emotion data repository of nearly 4 million faces analyzed from 75 countries, amounting to more than 40 billion emotion data points. Prior to founding Affectiva, as a research scientist at MIT Media Lab, Rana spearheaded the application of emotion technology in a variety of fields, including mental health and autism research. Her work has appeared in numerous publications including the New Yorker, Wired, Forbes, Fast Company, the Wall Street Journal, the New York Times, CNN, CBS, Time magazine, Fortune, and Reddit. A TED speaker, Rana was recognized by Entrepreneur as one of the seven most powerful women to watch In 2014, inducted into the Women in Engineering Hall of Fame, recognized as a 2012 Technology Review top 35 innovators under 35, listed on Ad Age's 40 under 40, and given Smithsonian magazine's 2015 American Ingenuity Award for Technology.

The handset vendor has already set aside an initial corpus of 10 million for the development of this technologyRisk-prediction tool for diabetes patients. Read more ... » through a merger and acquisition or a jointToyota Goes To Silicon Valley, Enters Artificial Intelligence & Robotics Industry. "The budget is in tune of 10 million to start with, and as we see progress on this front and things go in right direction, then there will be no constraint on the budget part. We can spend as high as possible. Some part of this budget has been generated from the India business, while some portion has been allocated from Japan," Pankaj Rana, head of mobility division, India, South Asia, Middle East and Africa at Panasonic, told ET. "Our team would be traveling to Silicon Valley soon. We will have new products ready with AI in 9-12 months. In the last three months, we have finalized whatSingapore-based adtech startup wants to revolutionize multiscreen conversations. Read more ... » we will do and budgets have been allocated from Panasonic Japan and Panasonic India. Now we have to find a partner and start working on timeline, while understandingHow machine learning will take off in the cloud. Read more ... » the market," he said.