Consider: we came fairly close to extincting the various species crucial to this cycle *AND STILL COULD.*
The problem today isn’t the ongoing symbolic & subsistence whaling, it is that we have ravaged the ocean ecosystem in ways we don’t understand, such as noise pollution.
We really don’t understand enough about the subsystems of the oceans to be fucking with them in the ways we have been during the past century.
Sleep and Activity Patterns as Transdiagnostic Behavioral Biomarkers in Psychiatry: Initial Insights from the DeeP-DD study
Dylan Hamitouche, Tihare Zamorano, Youcef Barkat, Deven Parekh, Lena Palaniyappan, David Benrimoh
https://arxiv.org/abs/2507.22088
Yo no soy capaz de identificar una canción de Kidd Voodoo, pero el muchacho llenó 7 Movistar Arena y eso tiene mucho mérito. Hallo que no queda mšs que aplaudir. 👏🏻
https://www.latercera.com/culto/noticia/kidd-voodoo-recibe-…
DD-JSCC: Dynamic Deep Joint Source-Channel Coding for Semantic Communications
Avi Deb Raha, Apurba Adhikary, Mrityunjoy Gain, Yumin Park, Walid Saad, Choong Seon Hong
https://arxiv.org/abs/2507.20467
Flexible Intelligent Metasurfaces in High-Mobility MIMO Integrated Sensing and Communications
Kuranage Roche Rayan Ranasinghe, Jiancheng An, Iv\'an Alexander Morales Sandoval, Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu, Chau Yuen, M\'erouane Debbah
https://arxiv.org/abs/2507.18793
Lattice study of scattering phase shifts for $DD^*$ and $BB^*$ systems using twisted boundary conditions: search for bound state formation
Masato Nagatsuka, Shoichi Sasaki
https://arxiv.org/abs/2507.20712
Envelope Control Enabled Probabilistic Shaping for Peak Power Constrained IM DD Systems
Dongdong Zou, Wei Wang, Jiawen Yao, Zhongxing Tian, Zeyu Feng, Huan Huang, Fan Li, Gordon Ning Liu, Gangxiang Shen, Yi Cai
https://arxiv.org/abs/2507.18149
Properties and microscopic structures of dense stellar matter in RMF models
Jia-Xing Niu, Hao Sun, Cheng-Jun Xia, Toshiki Maruyama
https://arxiv.org/abs/2506.11492

Properties and microscopic structures of dense stellar matter in RMF models
Data tables on the equation of state (EOS) and microscopic structures for cold dense stellar matter with proton fractions $Y_p =0.01$-$0.65$ and baryon number densities $n_\text{b}=10^{-8}$-$2 \ \mathrm{fm}^{-3}$ are obtained adopting 13 different relativistic density functionals, i.e., NL3, PK1, PK1r, GM1, MTVTC, DD-LZ1, PKDD, DD-ME2, TW99, DD-MEX, DD-MEX1, DD-MEX2, and DD-MEY. The EOSs of dense stellar matter inside neutron stars with baryon number densities $n_\text{b}=7.6\times 10^{-11}$-$2…
🌐 GÉANT sets a new benchmark for long-haul optical networking: 3,403 km of terrestrial transmission at 400G without transponders or regeneration!
This field trial, conducted in June on the GÉANT live production network using Cisco / Acacia 400G ULH QSFP-DD coherent pluggable optics, demonstrates the capabilities of new technologies to bridge extended distances without the need of traditional stand-alone DWDM transponders.
Read more:
Experimental End-to-End Optimization of Directly Modulated Laser-based IM/DD Transmission
Sergio Hernandez, Christophe Peucheret, Francesco Da Ros, Darko Zibar
https://arxiv.org/abs/2508.19910
Encoding Optimization for Low-Complexity Spiking Neural Network Equalizers in IM/DD Systems
Eike-Manuel Edelmann, Alexander von Bank, Laurent Schmalen
https://arxiv.org/abs/2508.13783
Dept Q episode 1 (set in Scotland) opens with Axon-style bodycam footage, but with the on-screen date formatted as YYYY-DD-MM
A double-degenerate scenario with a merger to explosion delay time to explain type Ia supernova SN 2020aeuh
Noam Soker (Technion, Israel)
https://arxiv.org/abs/2507.16757
Recurrent Optical Spectrum Slicers as multi-{\lambda} processors for WDM optical equalization of IM/DD channels
Kostas Sozos, Francesco Da Ros, George Sarantoglou, Charis Mesaritakis, Adonis Bogris
https://arxiv.org/abs/2507.11659
Beyond 200 Gb/s/lane: An Analytical Approach to Optimal Detection in Shaped IM-DD Optical Links with Relative Intensity Noise
Felipe Villenas, Kaiquan Wu, Yunus Can G\"ultekin, Jamal Riani, Alex Alvarado
https://arxiv.org/abs/2506.19684
fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…
fly_larva — Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`, `'ad'`, `'da'`, `'dd'`), and synapse count.
Miroir d'Hypnos - Episode 74 - ⛈️ TrembleLance ⛈️ - YouTube
#ReveDeDragon
Cross-Subject DD: A Cross-Subject Brain-Computer Interface Algorithm
Xiaoyuan Li, Xinru Xue, Bohan Zhang, Ye Sun, Shoushuo Xi, Gang Liu
https://arxiv.org/abs/2507.05268
DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios
Bo Yang, Xingquan Li, Jie Zhao, Ying Jiang
https://arxiv.org/abs/2508.02717
»The start date and end date should be in the format mm/dd/yyyy hh:ss Z«
Und tschüss. ISO 8601 oder gar nicht.
Ich suchte nach etwas, das mir aus einem CSV mit vielen Terminen ein ICS macht, das ich in eine Kalender-Software importieren kann. Und nein, ich brauche keine Hinweise mehr, ich wurde bereits ausführlich und hinreichend fündig. #iso8601
Discrete Radar based on Modulo Arithmetic
Nishant Mehrotra, Sandesh Rao Mattu, Saif Khan Mohammed, Ronny Hadani, Robert Calderbank
https://arxiv.org/abs/2508.15671 https://
Melting down a tetraquark: $D^{\ast}D^{(\ast)}$ interactions and $T_{cc}(3875)^ $ in a hot environment
Victor Montesinos, Gloria Montana, Miguel Albaladejo, Juan Nieves, Laura Tolos
https://arxiv.org/abs/2507.13319
Charged Particle Scattering in Renormalizable Pionless Effective Field Theory at Next-to-Leading Order: The $pd$, $dd$, and $p^3\mathrm{He}$ Case
Mat\'u\v{s} Rojik, Martin Sch\"afer, Mirko Bagnarol, Nir Barnea
https://arxiv.org/abs/2507.16250
Integrated recurrent optical spectral slicer for equalization of 100-km C-band IM/DD transmission
I. Teofilovic, K. Sozos, H. Liu, S. Malhouitre, S. Garcia, G. Sarantoglou, P. Bienstman, B. Charbonnier, C. Mesaritakis, C. Vigliar, P. Petropoulos, A. Bogris, F. Da Ros
https://arxiv.org/abs/2507.11654
Homogeneous Stellar Atmospheric Parameters and 22 Elemental Abundances for FGK Stars Derived From LAMOST Low-resolution Spectra with DD-Payne
Meng Zhang, Maosheng Xiang, Yuan-Sen Ting, Anish Maynur Amarsi, Hua-Wei Zhang, Jianrong Shi, Haibo Yuan, Haining Li, Jiahui Wang, Yaqian Wu, Tianmin Wu, Lanya Mou, Hong-liang Yan, Jifeng Liu
https://
Classification non supervis{\'e}es d'acquisitions hyperspectrales cod{\'e}es : quelles v{\'e}rit{\'e}s terrain ?
Trung-tin Dinh (IRAP, LAAS-PHOTO, UT3, LAAS), Herv\'e Carfantan (IRAP), Antoine Monmayrant (LAAS-PHOTO), Simon Lacroix (LAAS-RIS)
https://arxiv.org/abs/2508.03753…
fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…
fly_larva — Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`, `'ad'`, `'da'`, `'dd'`), and synapse count.
An overlapping domain decomposition method for parametric Stokes and Stokes-Darcy problems via proper generalized decomposition
Marco Discacciati, Ben J. Evans, Matteo Giacomini
https://arxiv.org/abs/2507.06861
Differential Communication in Channels with Mobility and Delay Spread using Zak-OTFS
Sandesh Rao Mattu, Nishant Mehrotra, Robert Calderbank
https://arxiv.org/abs/2507.12593
Numerical Errors in Quantitative System Analysis With Decision Diagrams
Sebastiaan Brand, Arend-Jan Quist, Richard M. K. van Dijk, Alfons Laarman
https://arxiv.org/abs/2508.02673
Es gibt auch dieses Helferlein, um die #Synology zu "befreien"
https://github.com/007revad/Synology_HDD_db
Metasurfaces-Integrated Doubly-Dispersive MIMO: Channel Modeling and Optimization
Kuranage Roche Rayan Ranasinghe, Hyeon Seok Rou, Iv\'an Alexander Morales Sandoval, Giuseppe Thadeu Freitas de Abreu, George C. Alexandropoulos
https://arxiv.org/abs/2506.14985
Towards Machine Unlearning for Paralinguistic Speech Processing
Orchid Chetia Phukan, Girish, Mohd Mujtaba Akhtar, Shubham Singh, Swarup Ranjan Behera, Vandana Rajan, Muskaan Singh, Arun Balaji Buduru, Rajesh Sharma
https://arxiv.org/abs/2506.02230
fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…
fly_larva — Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`, `'ad'`, `'da'`, `'dd'`), and synapse count.
Replaced article(s) found for hep-lat. https://arxiv.org/list/hep-lat/new
[1/1]:
- Extending DD-$\alpha$AMG on heterogeneous machines
Gustavo Ramirez-Hidalgo, Lianhua He, Ke-Long Zhang
Low-Complexity Receiver Design for Affine Filter Bank Modulation
Kuranage Roche Rayan Ranasinghe, Bruno S. Chang, Giuseppe Thadeu Freitas de Abreu
https://arxiv.org/abs/2506.17010
Reaction processes of muon-catalyzed fusion in the muonic molecule $dd\mu$ studied with the tractable $T$-matrix model
Qian Wu, Zhu-Fang Cui, Masayasu Kamimura
https://arxiv.org/abs/2508.12783
fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…
fly_larva — Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`, `'ad'`, `'da'`, `'dd'`), and synapse count.
Modeling Optical Key Distribution over a Satellite-to-Ground Link Under Weak Atmospheric Turbulence
Artur Czerwinski, Miko{\l}aj Lasota, Marcin Jarzyna, Mateusz Kucharczyk, Micha{\l} Jachura, Konrad Banaszek
https://arxiv.org/abs/2508.05807
A New 5 bit/2D-symbol Modulation Format for Relative Intensity Noise-dominated IM-DD Systems
Felipe Villenas, Kaiquan Wu, Yunus Can G\"ultekin, Jamal Riani, Alex Alvarado
https://arxiv.org/abs/2506.01761
Entropy-Based Methods to Address Sampling Bias in Archaeological Predictive Modeling
Mehmet S{\i}dd{\i}k \c{C}ad{\i}rc{\i}, Golnaz Shahtahmassebi
https://arxiv.org/abs/2508.02272
PGD-based local surrogate models via overlapping domain decomposition: a computational comparison
Marco Discacciati, Ben J. Evans, Matteo Giacomini
https://arxiv.org/abs/2508.01313
Charge symmetry breaking effects of $\omega$-$\rho^0$ mixing in relativistic mean-field model
Yusuke Tanimura, Tomoya Naito, Hiroyuki Sagawa, Myung-Ki Cheoun
https://arxiv.org/abs/2506.06629
fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…
fly_larva — Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`, `'ad'`, `'da'`, `'dd'`), and synapse count.
Delay-Doppler Domain Signal Processing Aided OFDM (DD-a-OFDM) for 6G and Beyond
Yiyan Ma, Bo Ai, Jinhong Yuan, Shuangyang Li, Qingqing Cheng, Zhenguo Shi, Weijie Yuan, Zhiqiang Wei, Akram Shafie, Guoyu Ma, Yunlong Lu, Mi Yang, Zhangdui Zhong
https://arxiv.org/abs/2508.04253
Replaced article(s) found for hep-lat. https://arxiv.org/list/hep-lat/new
[1/1]:
- Extending DD-$\alpha$AMG on heterogeneous machines
Gustavo Ramirez-Hidalgo, Lianhua He, Ke-Long Zhang
Photoabsorption Cross Sections studied within the axially deformed Relativistic Quasiparticle Finite Amplitude Framework
C. Chen (Frontiers Science Center for Rare isotope, Lanzhou University, Lanzhou, China, School of Nuclear Science and Technology, Lanzhou University, Lanzhou, China), Y. F. Niu (Frontiers Science Center for Rare isotope, Lanzhou University, Lanzhou, China, School of Nuclear Science and Technology, Lanzhou University, Lanzhou, China), R. Xu (China Nuclear Data Center,…
fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…
fly_larva — Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`, `'ad'`, `'da'`, `'dd'`), and synapse count.
Exploring O-RAN Compression Techniques in Decentralized Distributed MIMO Systems: Reducing Fronthaul Load
Mostafa Rahmani, Junbo Zhao, Vida Ranjbar, Ahmed Al-Tahmeesschi, Hamed Ahmadi, Sofie Pollin, Alister G. Burr
https://arxiv.org/abs/2507.04997
Zak-OTFS over CP-OFDM
Saif Khan Mohammed, Saurabh Prakash, Muhammad Ubadah, Imran Ali Khan, Ronny Hadani, Shlomo Rakib, Shachar Kons, Yoav Hebron, Ananthanarayanan Chockalingam, Robert Calderbank
https://arxiv.org/abs/2508.03906
Zak-OFDM: Low Complexity Joint Equalization of OFDM Carriers in Doubly-Spread Channels
Saif Khan Mohammed, Sandesh Rao Mattu, Nishant Mehrotra, Venkatesh Khammammetti, Robert Calderbank
https://arxiv.org/abs/2506.23045
SDR-Empowered Environment Sensing Design and Experimental Validation Using OTFS-ISAC Signals
Jun Wu, Yuye Shi, Weijie Yuan, Qingqing Cheng, Buyi Li, Xinyuan Wei
https://arxiv.org/abs/2507.01427