Today's article comes from the JASA Express Letters journal. The authors are Ragland et al., from Woods Hole Oceanographic Institution, in Massachusetts. In this paper, the authors take data collected from a set of OOI's hydrophones, and use it to figure out how much low-frequency underwater-sound is controlled by wind speed.

Today's article comes from the journal of Machine Learning: Science and Technology. The authors are Chen et al., from Pacific Northwest National Laboratory (PNNL), in Washington. In this paper they make the argument that many PINN failures are simultaneously optimization problems and sampling problems. Their solution? Instead of treating adaptive weighting and adaptive sampling as competing alternatives, just combine them into a single unified framework.

Today's article comes from the MGE Advances journal. The authors are Nie et al., from Northwestern Polytechnical University, in China. In this paper they're showcasing a pipeline that can hunt for new metals with specific physical properties. It screens through a massive set of candidate compositions, and narrows those down to a handful of promising alloys.

Today's article comes from the International Journal of Computational Intelligence Systems. The authors are Chilukuri et al., from St. Jude Childrens Cancer Research Hospital, in Tennessee. In this paper they're proposing a two-stage deep learning system for group recommendations.

Today's article comes from the Journal of Universal Computer Science. The authors are Altherwi et al., from Jazan University, in Saudi Arabia. In this paper they're combining a Deep Belief Network (DBN) with Grey Wolf Optimization (GWO) to create a pipeline that can better predict the output of Hybrid Renewable Energy Systems (HRES).

Today's article comes from the Frontiers in Computer Science journal. The authors are Xiao et al., from Hunan Institute of Engineering, in China. In this paper, they're taking the signals that fault localization normally uses and augmenting them with static features, derived from the repo.

Today's article comes from the CAAI Transactions on Intelligence Technology journal. The authors are He et al., from Yanshan University, in China. In this paper they're showcasing a new object detector for roadside cameras. Their framework augments a standard YOLO pipeline with a second branch that extracts boundary and texture features, then fuses them with RGB features to better distinguish between objects.

Today's article comes from the journal of Machine Learning and Knowledge Extraction. The authors are Kamal et al., from the German University in Cairo (GUC). In this paper they build can intrusion detection system (IDS) that can operate at multiple network-layers at once. How? It uses a shared autoencoder with projection layers that map each level of data into a common latent space.

Today's article comes from the journal of Machine Learning and Knowledge Extraction. The authors are Nassar et al., from Hamad Bin Khalifa University, in Qatar. In this paper they're evaluating two replacements for KL-divergence within t-SNE. Max-Flipped KL Divergence (KLmax) and KL-Wasserstein Loss.

Today's article comes from the Advances in Fuzzy Systems journal. The authors are Zhang et al., from Anqing Normal University, in China. In this paper they're exploring a new strategy for solving MCDMs with fuzzy inputs. Their approach works by representing uncertain evaluations as sets of possible values, measuring the distance between those sets without distorting the data, and then deriving the importance of each criterion from the level of disagreement it creates among the alternatives.