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Metronom will Hansenetz auch nach 2026 betreiben: Ende Januar wurde bekannt, dass der Vertrag für das zurzeit von der Metronom Eisenbahngesellschaft betriebene Hansenetz im Sommer 2026 vorzeitig endet und neu ausgeschrieben werden soll. Die Metronom Eisenbahngesellschaft hat jetzt klargestellt, dass sie sich bei der Ausschreibung wieder bewerben möchte, und sich keinesfalls aus dem Nahverkehr in Niedersachsen zurückziehen wird. Der Fahrgastverband PRO BAHN hat gefordert,…

Metronom will Hansenetz auch nach 2026 betreiben
Metronom will Hansenetz auch nach 2026 betreiben

Sehr spannend gerade, mit Stream:
„This panel brings together three different stories from Gaza, Ukraine and Azerbaijan, documenting the monopoly of communication channels during wartime and how strategies of digital authoritarianism can be used to maintain power, target people and distort reality.“
Beyond Control — Disruption Network Lab

LLAMP: Assessing Network Latency Tolerance of HPC Applications with Linear Programming
Siyuan Shen, Langwen Huang, Marcin Chrapek, Timo Schneider, Jai Dayal, Manisha Gajbe, Robert Wisniewski, Torsten Hoefler
https://arxiv.org/abs/2404.14193

LLAMP: Assessing Network Latency Tolerance of HPC Applications with Linear Programming
The shift towards high-bandwidth networks driven by AI workloads in data centers and HPC clusters has unintentionally aggravated network latency, adversely affecting the performance of communication-intensive HPC applications. As large-scale MPI applications often exhibit significant differences in their network latency tolerance, it is crucial to accurately determine the extent of network latency an application can withstand without significant performance degradation. Current approaches to as…

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Physics-Informed Graph Neural Networks for Water Distribution Systems
Inaam Ashraf, Janine Strotherm, Luca Hermes, Barbara Hammer
https://arxiv.org/abs/2403.18570

Physics-Informed Graph Neural Networks for Water Distribution Systems
Water distribution systems (WDS) are an integral part of critical infrastructure which is pivotal to urban development. As 70% of the world's population will likely live in urban environments in 2050, efficient simulation and planning tools for WDS play a crucial role in reaching UN's sustainable developmental goal (SDG) 6 - "Clean water and sanitation for all". In this realm, we propose a novel and efficient machine learning emulator, more precisely, a physics-informed deep learning (DL) model…

Exploring Gene Regulatory Interaction Networks and predicting therapeutic molecules for Hypopharyngeal Cancer and EGFR-mutated lung adenocarcinoma
Abanti Bhattacharjya, Md Manowarul Islam, Md Ashraf Uddin, Md. Alamin Talukder, AKM Azad, Sunil Aryal, Bikash Kumar Paul, Wahia Tasnim, Muhammad Ali Abdulllah Almoyad, Mohammad Ali Moni
https://

Exploring Gene Regulatory Interaction Networks and predicting therapeutic molecules for Hypopharyngeal Cancer and EGFR-mutated lung adenocarcinoma
With the advent of Information technology, the Bioinformatics research field is becoming increasingly attractive to researchers and academicians. The recent development of various Bioinformatics toolkits has facilitated the rapid processing and analysis of vast quantities of biological data for human perception. Most studies focus on locating two connected diseases and making some observations to construct diverse gene regulatory interaction networks, a forerunner to general drug design for cur…

Impact of the Russia-Ukraine conflict on the international staple agrifood trade networks
Yin-Ting Zhang, Mu-Yao Li, Wei-Xing Zhou
https://arxiv.org/abs/2403.12496

Impact of the Russia-Ukraine conflict on the international staple agrifood trade networks
The Russia-Ukraine conflict is a growing concern worldwide and poses serious threats to regional and global food security. Using monthly trade data for maize, rice, and wheat from 2016/1 to 2022/12, this paper constructs three international crop trade networks (iCTNs) and an aggregate international food trade network (iFTN). We aim to examine the structural changes following the occurrence of the Russia-Ukraine conflict. We find significant shifts in the number of edges, average degree, density…

AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations
Wei Wu, Chao Wang, Dazhong Shen, Chuan Qin, Liyi Chen, Hui Xiong
https://arxiv.org/abs/2403.17416

AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations
Collaborative filtering methods based on graph neural networks (GNNs) have witnessed significant success in recommender systems (RS), capitalizing on their ability to capture collaborative signals within intricate user-item relationships via message-passing mechanisms. However, these GNN-based RS inadvertently introduce excess linear correlation between user and item embeddings, contradicting the goal of providing personalized recommendations. While existing research predominantly ascribes this…

This https://arxiv.org/abs/2206.06112 has been replaced.
link: https://scholar.google.com/scholar?q=a

Vision-State Fusion: Improving Deep Neural Networks for Autonomous Robotics
Vision-based deep learning perception fulfills a paramount role in robotics, facilitating solutions to many challenging scenarios, such as acrobatic maneuvers of autonomous unmanned aerial vehicles (UAVs) and robot-assisted high-precision surgery. Control-oriented end-to-end perception approaches, which directly output control variables for the robot, commonly take advantage of the robot's state estimation as an auxiliary input. When intermediate outputs are estimated and fed to a lower-level c…

Hybridized Convolutional Neural Networks and Long Short-Term Memory for Improved Alzheimer's Disease Diagnosis from MRI Scans
Maleka Khatun, Md Manowarul Islam, Habibur Rahman Rifat, Md. Shamim Bin Shahid, Md. Alamin Talukder, Md Ashraf Uddin
https://arxiv.org/abs/2403.05353

Hybridized Convolutional Neural Networks and Long Short-Term Memory for Improved Alzheimer's Disease Diagnosis from MRI Scans
Brain-related diseases are more sensitive than other diseases due to several factors, including the complexity of surgical procedures, high costs, and other challenges. Alzheimer's disease is a common brain disorder that causes memory loss and the shrinking of brain cells. Early detection is critical for providing proper treatment to patients. However, identifying Alzheimer's at an early stage using manual scanning of CT or MRI scans is challenging. Therefore, researchers have delved into the e…