Artificial intelligence reaches new adoption levels each year. As its adoption becomes more ubiquitous, industries like supply chain management and logistics have begun to leverage AI in innovative ways - taking the front row in the AI show-time. A recent report by Research and Markets "Artificial Intelligence in Supply Chain Management Market" finds that AI in SCM solutions as a whole will reach $15.5B globally by 2026. The large volumes of data generated by these industry verticals, the number of devices employed and the challenges associated with the process require a more defined, elaborate structure to ensure transparency through digital automation. Events such as the unexpected blocking of the world's busiest trade route Suez Canal by a large shipping vessel demonstrate how supply-chain optimization and diversification have become an essential need of the hour.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Facing a growing threat from China, the Navy envisions drone ships keeping an electronic eye on enemy forces across the vast Pacific Ocean, extending the reach of firepower, and keeping sailors out of harm's way. The Navy is speeding development of those robotic ships as an affordable way to keep pace with China's growing fleet while vowing not to repeat costly shipbuilding blunders from recent years. The four largest drone ships are being used together this summer during a multination naval exercise in the Pacific Ocean.

"The fact that newer drones, such as the Mohajer-6, are now being seen in places like the Horn of Africa shows that countries see them as a potential game-changer," he added, referring to an advanced Iranian drone claimed to have a range of about 125 miles and the ability to carry precision-guided munitions. "It's amazing warfare on the cheap," said Mr. Frantzman, adding that Iranian drones cost less than other models on the market but were growing in sophistication, and had proved their worth on battlefields across the Middle East. Tehran began drone development in the 1980s during the Iran-Iraq war. Despite crippling sanctions imposed on Iran over its nuclear and missile programs in recent years, it has managed to produce and field a vast array of military drones, used for both surveillance and attack, according to analysis by experts. That program has become a major concern for Israel and the United States in recent years.

Language is our lifeline to the world. But because high-quality translation tools don't exist for hundreds of languages, billions of people today can't access digital content or participate fully in conversations and communities online in their preferred or native languages. This is particularly an issue for hundreds of millions of people who speak the many languages of Africa and Asia. To help people connect better today and be part of the metaverse of tomorrow, our AI researchers created No Language Left Behind (NLLB), an effort to develop high-quality machine translation capabilities for most of the world's languages. Today, we're announcing an important breakthrough in NLLB: We've built a single AI model called NLLB-200, which translates 200 different languages with results far more accurate than what previous technology could accomplish.

Signal Processing Society Magazine Best Paper Award (ICASSPにて) A. Cichocki (Skoltech) L. De Lathauwer (KULeuven) 19 テンソル分解のパイオニアと重要な文献 Sidiropoulosらのレビュー論文 Tensor Decomposition for Signal Processing and Machine Learning Sidiropoulos, IEEE TSP, 2017 [pdf] Cichockiらの書籍 Tensor Networks for Dimensionality Reduction and Large-Scale Optimization: Part 1 [link], Part 2 [pdf] Cichocki, Foundations and Trends in Machine Learning, 2016 [link] N. Sidiropoulos (Univ. of Virginia) 20 宣伝 Book chapterを書きました Tensors for Data Processing, Elsevier, 2021 [link] 目次 1章 Tensor decompositions: Computations, applications, and challenges 2章 Transform-based tensor SVD in multidimensional image recovery 3章 Partensor 4章 A Riemannian approach to low-rank tensor learning 5章 Generalized thresholding for low-rank tensor recovery 6章 Tensor principal component analysis 7章 Tensors for deep learning theory 8章 Tensor network algorithms for image classification 9章 High-performance TD for compressing and accelerating DNN 10章 Coupled tensor decomposition for data fusion 11章 Tensor methods for low-level vision T. Yokota, CF.

Two major causes of death in the United States and worldwide are stroke and myocardial infarction. The underlying cause of both is thrombi released from ruptured or eroded unstable atherosclerotic plaques that occlude vessels in the heart (myocardial infarction) or the brain (stroke). Clinical studies show that plaque composition plays a more important role than lesion size in plaque rupture or erosion events. To determine the plaque composition, various cell types in 3D cardiovascular immunofluorescent images of plaque lesions are counted. However, counting these cells manually is expensive, time-consuming, and prone to human error. These challenges of manual counting motivate the need for an automated approach to localize and count the cells in images. The purpose of this study is to develop an automatic approach to accurately detect and count cells in 3D immunofluorescent images with minimal annotation effort. In this study, we used a weakly supervised learning approach to train the HoVer-Net segmentation model using point annotations to detect nuclei in fluorescent images. The advantage of using point annotations is that they require less effort as opposed to pixel-wise annotation. To train the HoVer-Net model using point annotations, we adopted a popularly used cluster labeling approach to transform point annotations into accurate binary masks of cell nuclei. Traditionally, these approaches have generated binary masks from point annotations, leaving a region around the object unlabeled (which is typically ignored during model training). However, these areas may contain important information that helps determine the boundary between cells. Therefore, we used the entropy minimization loss function in these areas to encourage the model to output more confident predictions on the unlabeled areas. Our comparison studies indicate that the HoVer-Net model trained using our weakly ...

While communicating with a user, a task-oriented dialogue system has to track the user's needs at each turn according to the conversation history. This process called dialogue state tracking (DST) is crucial because it directly informs the downstream dialogue policy. DST has received a lot of interest in recent years with the text-to-text paradigm emerging as the favored approach. In this review paper, we first present the task and its associated datasets. Then, considering a large number of recent publications, we identify highlights and advances of research in 2021-2022. Although neural approaches have enabled significant progress, we argue that some critical aspects of dialogue systems such as generalizability are still underexplored. To motivate future studies, we propose several research avenues.

Bilingual terminologies are important machine translation resources in the field of e-commerce, which are usually either manually translated or automatically extracted from parallel data. The human translation is costly and e-commerce parallel corpora is very scarce. However, the comparable data in different languages in the same commodity field is abundant. In this paper, we propose a novel framework of extracting e-commercial bilingual terminologies from comparable data. Benefiting from the cross-lingual pre-training in e-commerce, our framework can make full use of the deep semantic relationship between source-side terminology and target-side sentence to extract corresponding target terminology. Experimental results on various language pairs show that our approaches achieve significantly better performance than various strong baselines.

Federated learning (FL) is an emerging technique used to collaboratively train a global machine learning model while keeping the data localized on the user devices. The main obstacle to FL's practical implementation is the Non-Independent and Identical (Non-IID) data distribution across users, which slows convergence and degrades performance. To tackle this fundamental issue, we propose a method (ComFed) that enhances the whole training process on both the client and server sides. The key idea of ComFed is to simultaneously utilize client-variance reduction techniques to facilitate server aggregation and global adaptive update techniques to accelerate learning. Our experiments on the Cifar-10 classification task show that ComFed can improve state-of-the-art algorithms dedicated to Non-IID data.

This study proposes the use of generative models (GANs) for augmenting the EuroSAT dataset for the Land Use and Land Cover (LULC) Classification task. We used DCGAN and WGAN-GP to generate images for each class in the dataset. We then explored the effect of augmenting the original dataset by about 10% in each case on model performance. The choice of GAN architecture seems to have no apparent effect on the model performance. However, a combination of geometric augmentation and GAN-generated images improved baseline results. Our study shows that GANs augmentation can improve the generalizability of deep classification models on satellite images.