A search for "arxiv ddos" produces hundreds of papers on DDOS and DDOS mitigation.
How many of these are practical and useful and how many of these are implemented by ISPs?

https://www.google.com/search?q=arxiv+ddos&sca_esv=564661802&hl=en&sxsrf=AB5stBjwJWXXDQjeAjmKos23xxcq7yRnbQ%3A1694518049914&source=hp&ei=IUsAZdL7NOagur8Ph8W6qA8&iflsig=AD69kcEAAAAAZQBZMZ5TbNkRRmU2K_5GeS3GkTRWgsCo&ved=0ahUKEwiSkLbT-6SBAxVmkO4BHYeiDvUQ4dUDCAo&uact=5&oq=arxiv+ddos&gs_lp=Egdnd3Mtd2l6IgphcnhpdiBkZG9zMgQQIxgnMggQABiKBRiGAzIIEAAYigUYhgMyCBAAGIoFGIYDMggQABiKBRiGA0jcEFAAWL0PcAB4AJABAJgBsgGgAf4LqgEEMC4xMLgBA8gBAPgBAcICBxAjGIoFGCfCAhQQLhiKBRixAxiDARjHARjRAxiRAsICCBAAGIoFGJECwgILEC4YigUYsQMYgwHCAgsQABiABBixAxiDAcICCxAAGIoFGLEDGIMBwgIREC4YgAQYsQMYgwEYxwEY0QPCAgcQABiKBRhDwgINEC4YigUYxwEY0QMYQ8ICCBAuGIAEGLEDwgINEAAYigUYsQMYgwEYQ8ICDhAAGIoFGLEDGIMBGJECwgIKEAAYgAQYFBiHAsICCBAAGIAEGLEDwgIREC4YigUYsQMYgwEYxwEYrwHCAgUQABiABMICBhAAGBYYHg&sclient=gws-wiz

#ddos #arxiv

📝 Weak-Pde-Learn: A Weak Form Based Approach to Discovering PDEs From Noisy, Limited Data 🧠

"Weak-PDE-LEARN uses an adaptive loss function based on weak forms to train a neural network to approximate the PDE solution while simultaneously identifying the governing PDE (see Figure )." [gal30b+] 🤖 #LG

⚙️ https://github.com/punkduckable/Weak_PDE_LEARN
🔗 https://arxiv.org/abs/2309.04699v1 #arxiv

📝 Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf 📚

"Keeps LLMs frozen, and relies on retrieval and reflection on past communications and experiences for improvement, which is inspired by the human learning process of "reflection-in-action"." [gal30b+] 🤖 #CL

🔗 https://arxiv.org/abs/2309.04658v1 #arxiv

📝 Redundancy-Free Self-Supervised Relational Learning for Graph Clustering 🧠

"A novel self-supervised deep graph clustering method named Relational Redundancy-Free Graph Clustering (R$^2$FGC) is proposed to tackle the problem." [gal30b+] 🤖 #LG

⚙️ https://github.com/yisiyu95/R2FGC
🔗 https://arxiv.org/abs/2309.04694v1 #arxiv

📝 Can NLP Models 'Identify', 'Distinguish', and 'Justify' Questions That Don't Have a Definitive Answer? 📚

"QnotA Dataset is constructed with the aim to test the ability of QA models in identifying, distinguishing and justifying questions without answers (QnotA)." [gal30b+] 🤖 #CL

🔗 https://arxiv.org/abs/2309.04635v1 #arxiv

📝 Learning Task Skills and Goals Simultaneously From Physical Interaction 🦾

"Develops a framework that enables a robot to learn functional modules and long-term task objectives simultaneously from human demonstrations using a novel functional module identification algorithm and an objective alignment algorithm." [gal30b+] 🤖 #RO

🔗 https://arxiv.org/abs/2309.04596v1 #arxiv

📝 Examining Autoexposure for Challenging Scenes 🔭

"Presents a new dataset of images and video sequences captured using a DSLR camera with a large solution space (i,e shutter speed from 1/500 to 15 seconds)." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.04542v1 #arxiv

📝 Towards Understanding Neural Collapse: The Effects of Batch Normalization and Weight Decay 🧠

"Provides theoretical guarantees and empirical evidence that neural networks with batch normalization and a high weight decay will exhibit Neural Collapse, whereas neural networks without batch normalization or low weight decay will not." [gal30b+] 🤖 #LG

🔗 https://arxiv.org/abs/2309.04644v1 #arxiv

📝 When Less Is More: Investigating Data Pruning for Pretraining LLMs at Scale 📚🧠

"Performs a rigorous comparison at scale of the simple data quality estimator of perplexity, as well as more sophisticated and computationally intensive estimates of the Error L2-Norm and memorization." [gal30b+] 🤖 #CL #LG

🔗 https://arxiv.org/abs/2309.04564v1 #arxiv

📝 On the Efficacy of Multi-Scale Data Samplers for Vision Applications 🔭

"Shows that variable-batch-size multi-scale data sampling acts as an implicit regularizer which improves performance and model calibration, and makes models more robust to scaling and data distribution shifts." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.04502v1 #arxiv

arXiv, the open-access repository of electronic preprints and postprints, is experiencing a DDOS attack. Over a million email change requests were received from 200 IP addresses in China, overwhelming the arXiv email service.

Browsing and submitting papers on arXiv are not affected. Daily emails, new account registrations, and legitimate email address changes are all impacted by this disruption.

What would be the motivation?

https://blog.arxiv.org/2023/09/11/arxiv-org-is-experiencing-a-ddos-attack/
#arxiv #ddos

📝 MoEController: Instruction-Based Arbitrary Image Manipulation with Mixture-of-Expert Controllers 🔭📚

"Leverages large language models (ChatGPT) and image synthesis models (ControlNet) to generate a large number of image-text pairs that can be used for global and local image manipulation datasets." [gal30b+] 🤖 #CV #CL

🔗 https://arxiv.org/abs/2309.04372v1 #arxiv

📝 MaskDiffusion: Boosting Text-to-Image Consistency with Conditional Mask 🔭

"We advance the diffusion model with an adaptive mask, which is conditioned on the attention maps and the prompt embeddings, to dynamically adjust the contribution of each text token for the image features." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.04399v1 #arxiv

📝 CNN Injected Transformer for Image Exposure Correction 🔭

"A CNN Injected Transformer (CIT) is proposed to harness the individual strengths of CNN and Transformer simultaneously to perform exposure correction on images by incorporating a channel attention block (CAB) and a half-instance normalization block (HINB) into each window-based Transformer block." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.04366v1 #arxiv

📝 Evaluation and Mitigation of Agnosia in Multimodal Large Language Models 🔭📚

"Proposes EMMA, an evaluation-mitigation framework that automatically creates fine-grained and diverse visual question answering examples to assess the extent of agnosia in Multimodal Pre-trained Language Models (MLLMs) comprehensively." [gal30b+] 🤖 #CV #CL

🔗 https://arxiv.org/abs/2309.04041v1 #arxiv

📝 Mobile v-MoEs: Scaling Down Vision Transformers via Sparse Mixture-of-Experts 🔭🧠

"Proposes a simplified and mobile-friendly MoE design where entire images rather than individual patches are routed to the experts to achieve better accuracy and efficiency trade-off on vision tasks." [gal30b+] 🤖 #CV #LG

🔗 https://arxiv.org/abs/2309.04354v1 #arxiv

📝 Unsupervised Object Localization with Representer Point Selection 🔭

"Proposes a novel unsupervised object localization method based on representer point selection, where the predictions of the model can be represented as a linear combination of representer values of training points by using the self-supervised pre-trained model." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.04172v1 #arxiv

📝 Multi-Contact Stochastic Predictive Control for Legged Robots with Contact Locations Uncertainty 🦾

"Addresses the problem of robust kino-dynamic trajectory optimization using stochastic nonlinear model predictive control (SNMPC) by considering additive uncertainties on the model dynamics subject to contact location chance-constraints as a function of robot's full kinematics." [gal30b+] 🤖 #RO #SY

🔗 https://arxiv.org/abs/2309.04469v1 #arxiv

📝 Learning From Power Signals: An Automated Approach to Electrical Disturbance Identification Within a Power Transmission System 🧠

"Power disturbance events are recorded as a voltage/current waveform over a time period ranging from a few milliseconds to several minutes depending on the event type and the type of recording device." [gal30b+] 🤖 #LG

🔗 https://arxiv.org/abs/2309.04361v1 #arxiv

📝 Grouping Boundary Proposals for Fast Interactive Image Segmentation 🔭

"The adaptive cut can disconnect the image domain such that the target contours are imposed to pass through this cut only once, and the selected boundary proposals and corresponding minimal paths are used to delineate the target contours." [gal30b+] 🤖 #CV

⚙️ https://github.com/Mirebeau/HamiltonFastMarching
🔗 https://arxiv.org/abs/2309.04169v1 #arxiv