RMS Pooling is a pooling operation that calculates the square mean root for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. $$ z_{j} = \sqrt{\frac{1}{M}\sum^{M}_{i=1}u{ij}^{2}} $$

Diffusion models have shown strong performance in speech enhancement, but their real-time applicability has been limited by multi-step iterative sampling. Consistency distillation has recently emerged as a promising alternative by distilling a one-step consistency model from a multi-step diffusion-based teacher model. However, distilled consistency models are inherently biased towards the sampling trajectory of the teacher model, making them less robust to noise and prone to inheriting inaccuracies from the teacher model. To address this limitation, we propose ROSE-CD: Robust One-step Speech Enhancement via Consistency Distillation, a novel approach for distilling a one-step consistency model. Specifically, we introduce a randomized learning trajectory to improve the model's robustness to noise. Furthermore, we jointly optimize the one-step model with two time-domain auxiliary losses, enabling it to recover from teacher-induced errors and surpass the teacher model in overall performance. This is the first pure one-step consistency distillation model for diffusion-based speech enhancement, achieving 54 times faster inference speed and superior performance compared to its 30-step teacher model. Experiments on the VoiceBank-DEMAND dataset demonstrate that the proposed model achieves state-of-the-art performance in terms of speech quality. Moreover, its generalization ability is validated on both an out-of-domain dataset and real-world noisy recordings.

In a Replacing Eligibility Trace, each time the state is revisited, the trace is reset to $1$ regardless of the presence of a prior trace.. For the memory vector $\textbf{e}\_{t} \in \mathbb{R}^{b} \geq \textbf{0}$: $$\mathbf{e\_{0}} = \textbf{0}$$ $$\textbf{e}\_{t} = \gamma\lambda{e}\_{t-1}\left(s\right) \text{ if } s \neq s\_{t}$$ $$\textbf{e}\_{t} = 1 \text{ if } s = s\_{t}$$ They can be seen as crude approximation to dutch traces, which have largely superseded them as they perform better than replacing traces and have a clearer theoretical basis. Accumulating traces remain of interest for nonlinear function approximations where dutch traces are not available. Source: Sutton and Barto, Reinforcement Learning, 2nd Edition

Sarsa_INLINE_MATH_1 extends eligibility-traces to action-value methods. It has the same update rule as for TD_INLINE_MATH_1 but we use the action-value form of the TD erorr: $$ \delta\_{t} = R\_{t+1} + \gamma\hat{q}\left(S\_{t+1}, A\_{t+1}, \mathbb{w}\_{t}\right) - \hat{q}\left(S\_{t}, A\_{t}, \mathbb{w}\_{t}\right) $$ and the action-value form of the eligibility trace: $$ \mathbb{z}\_{-1} = \mathbb{0} $$ $$ \mathbb{z}\_{t} = \gamma\lambda\mathbb{z}\_{t-1} + \nabla\hat{q}\left(S\_{t}, A\_{t}, \mathbb{w}\_{t} \right), 0 \leq t \leq T$$ Source: Sutton and Barto, Reinforcement Learning, 2nd Edition

Unsupervised machine learning approach for identifying information threads by leveraging answers to 5W1H questions from documents, the temporal relationships between documents and hierarchical agglomerative clustering (HAC).

Gastrointestinal (GI) disorders, encompassing conditions like cancer and Crohn’s disease, pose a signifcant threat to public health. Endoscopic examinations have become crucial for diagnosing and treating these disorders efciently. However, the subjective nature of manual evaluations by gastroenterologists can lead to potential errors in disease classifcation. In addition, the difculty of diagnosing diseased tissues in GI and the high similarity between classes made the subject a difcult area. Automated classifcation systems that use artifcial intelligence to solve these problems have gained traction. Automatic detection of diseases in medical images greatly benefts in the diagnosis of diseases and reduces the time of disease detection. In this study, we suggested a new architecture to enable research on computer-assisted diagnosis and automated disease detection in GI diseases. This architecture, called SpatialAttention ConvMixer (SAC), further developed the patch extraction technique used as the basis of the ConvMixer architecture with a spatial attention mechanism (SAM). The SAM enables the network to concentrate selectively on the most informative areas, assigning importance to each spatial location within the feature maps. We employ the Kvasir dataset to assess the accuracy of classifying GI illnesses using the SAC architecture. We compare our architecture’s results with Vanilla ViT, Swin Transformer, ConvMixer, MLPMixer, ResNet50, and SqueezeNet models. Our SAC method gets 93.37% accuracy, while the other architectures get respectively 79.52%, 74.52%, 92.48%, 63.04%, 87.44%, and 85.59%. The proposed spatial attention block improves the accuracy of the ConvMixer architecture on the Kvasir, outperforming the state-of-the-art methods with an accuracy rate of 93.37%.

True Online $TD\left(\lambda\right)$ seeks to approximate the ideal online $\lambda$-return algorithm. It seeks to invert this ideal forward-view algorithm to produce an efficient backward-view algorithm using eligibility traces. It uses dutch traces rather than accumulating traces. Source: Sutton and Seijen

Understanding and explaining deep learning models is an imperative task. Towards this, we propose a method that obtains gradient-based certainty estimates that also provide visual attention maps. Particularly, we solve for visual question answering task. We incorporate modern probabilistic deep learning methods that we further improve by using the gradients for these estimates. These have two-fold benefits: a) improvement in obtaining the certainty estimates that correlate better with misclassified samples and b) improved attention maps that provide state-of-the-art results in terms of correlation with human attention regions. The improved attention maps result in consistent improvement for various methods for visual question answering. Therefore, the proposed technique can be thought of as a tool for obtaining improved certainty estimates and explanations for deep learning models.

WERSA: Wavelet-Enhanced Random Spectral Attention This repository provides the official implementation of WERSA, a novel attention mechanism with linear O(n) time complexity, designed to scale Transformer models to very long sequences without a performance trade-off. Our paper, "Scaling Attention to Very Long Sequences in Linear Time with Wavelet-Enhanced Random Spectral Attention (WERSA)", is available on arXiv:2507.08637. ## 🔬 The Science Behind WERSA Standard attention mechanisms have a quadratic (O(n²)) complexity that makes processing long sequences impractical. WERSA solves this by combining several powerful principles to achieve linear (O(n)) efficiency while maintaining high performance. - Multi-Resolution Analysis: Uses Haar wavelet transforms to decompose the input into multiple scales, capturing both local details and global context. - Adaptive Filtering: An MLP generates input-dependent filters and learnable scale_weights modulate each wavelet level, allowing the model to dynamically prioritize the most informative frequency components. - Linear Complexity via Random Features: Uses random feature projection to approximate the softmax kernel, avoiding the computation of the full quadratic attention matrix. ## ⚙️ Installation First, ensure you have PyTorch and Hugging Face Transformers installed. Then, install the wersa package directly from this repository. bash # 1. Install core dependencies (example for CUDA 12.1) pip install torch --index-url https://download.pytorch.org/whl/cu121 pip install transformers # 2. Install the WERSA package from this repository pip install git+https://github.com/vincenzodentamaro/wersa.git ## 🚀 Quickstart: Building a Qwen-like Model with WERSA You can easily build a Qwen-style causal language model with WERSA attention by importing the WersaConfig and WersaForCausalLM classes from the package. ### Building an 8B Parameter Model This snippet creates an ~8B parameter model with a configuration similar to state-of-the-art models like Qwen2-7B. python from wersa import WersaConfig, WersaForCausalLM from transformers import AutoTokenizer # Load a compatible tokenizer tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-7B") # Define the configuration for the 8B model config_8b = WersaConfig( vocab_size=len(tokenizer), pad_token_id=tokenizer.pad_token_id, hidden_size=4096, num_hidden_layers=32, num_attention_heads=32, intermediate_size=11008, max_position_embeddings=4096 ) # Instantiate the model model_8b = WersaForCausalLM(config_8b) print(f"8B Model created with ~{model_8b.num_parameters() / 1e9:.2f}B parameters.") ### Building a 0.6B Parameter Model This snippet creates a smaller ~0.6B parameter model, perfect for faster experiments or deployment on more constrained hardware. python from wersa import WersaConfig, WersaForCausalLM from transformers import AutoTokenizer # Load a compatible tokenizer tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-1.5B") # Define the configuration for the 0.6B model config_0_6b = WersaConfig( vocab_size=len(tokenizer), pad_token_id=tokenizer.pad_token_id, hidden_size=1024, num_hidden_layers=24, num_attention_heads=16, intermediate_size=2816, max_position_embeddings=1024 ) # Instantiate the model model_0_6b = WersaForCausalLM(config_0_6b) print(f"0.6B Model created with ~{model_0_6b.num_parameters() / 1e9:.2f}B parameters.") ## 📖 Training and Examples This repository includes complete scripts to demonstrate how to pre-train these models from scratch and test their generation capabilities. - train_and_generate_1b.py: A full example for training a ~1B parameter model. - train_and_generate_8b.py: A full example for training the 8B parameter model. ## 📜 Citation If you find WERSA useful in your research, please consider citing our paper: bibtex @misc{dentamaro2025scaling, title={Scaling Attention to Very Long Sequences in Linear Time with Wavelet-Enhanced Random Spectral Attention (WERSA)}, author={Vincenzo Dentamaro}, year={2025}, eprint={2507.08637}, archivePrefix={arXiv}, primaryClass={cs.LG} } ## 📄 License This project is licensed under the Apache License 2.0.

Wavelet is an asynchronous data parallel approach that interleaves waves of training tasks on the same group of GPUs, such that tasks belong to one wave can leverage on-device memory from tasks in another wave during their memory valley period, thus boost-up the training throughput. As shown in the Figure, Wavelet divides dataparallel training tasks into two waves, namely tick-wave and tock-wave. The task launching offset is achieved by delaying the launch time of tock-wave tasks for half of a whole forward-backward training cycle. Therefore, the tock-wave tasks can directly leverage GPU memory valley period of tick-wave tasks (e.g. 0.4s-0.6s in Figure 2(a)), since backward propagation of tick-wave tasks is compute-heavy but memory is often unused. Similarly, tick-wave tasks can leverage memory valley period of tock-wave tasks in the same way.

To get a human at Expedia, call their support team directly at +(1):805:(330):4056. After dialing, follow the automated menu prompts and press “0” or say “representative” to connect faster. +(1):805:(330):4056 If that doesn't work, wait through the menu until the system transfers you to an agent.

You can reserve your seat directly when booking (((🔰+18018555905))) // ((🔰+18048539001))) your flight or at a later date. You can reserve a seat for a fee via My bookings up to two hours before departure (((🔰+18018555905))) // ((🔰+18048539001))). You will be allocated a seat automatically during online check-in if you do not choose a seat (((🔰+18018555905))) // ((🔰+18048539001))).

WestJet allows (((🔰+18018555905))) // ((🔰+18048539001))) one carry-on bag and one personal item free of charge for most fares, excluding the UltraBasic fare on certain routes. UltraBasic fares only include one personal item (((🔰+18018555905))) // ((🔰+18048539001))) and do not include a carry-on bag. Carry-on bags must adhere to WestJet's size and weight restrictions (((🔰+18018555905))) // ((🔰+18048539001))).

Yes, Qatar Airways offers a "Hold My Booking" (((🔰+18018555905))) // ((🔰+18048539001))) feature that allows you to reserve a flight for a limited time without full payment (((🔰+18018555905))) // ((🔰+18048539001))). This option may require a small holding fee and is available based on route and fare class.

We will disclose all guidelines (((🔰+18018555905))) // ((🔰+18048539001))) and procedures for guests with reduced mobility and special needs on our US website (((🔰+18018555905))) // ((🔰+18048539001))) available via www.etihad.com, or via the Etihad Airways Contact Centre, telephone number (((🔰+18018555905))) // ((🔰+18048539001))), and at our airport offices.

To check if your flight ticket is confirmed (((🔰+18018555905))) // ((🔰+18048539001))) , you can verify it on the airline's website or app by entering your booking (((🔰+18018555905))) // ((🔰+18048539001))) reference (PNR) and last name. You can also check through your online travel agency's website or by contacting the airline directly (((🔰+18018555905))) // ((🔰+18048539001))).

For most flights (((🔰+18018555905))) // ((🔰+18048539001))), you can check in on the Frontier app starting 24 hours before your flight's scheduled departure time. You must have your boarding pass (((🔰+18018555905))) // ((🔰+18048539001))) and have any bags checked in by the below cutoff times before departure: 60 minutes (domestic flights) 60 minutes (international flights)(((🔰+18018555905))) // ((🔰+18048539001))).

The easiest way to view or update your booking (((🔰+18018555905))) // ((🔰+18048539001))) is at etihad.com/manage. You can change contact details, add extras like bags or seats, check in, download your boarding (((🔰+18018555905))) // ((🔰+18048539001)))pass, and request services such as meals or assistance. If you booked (((🔰+18018555905))) // ((🔰+18048539001))) through a travel agent, you can still manage your flight at etihad.com/manage.

To check your KLM booking (((🔰+18018555905))) // ((🔰+18048539001))), visit the official KLM website or open the KLM app (((🔰+18018555905))) // ((🔰+18048539001))). Click on “My Trip” and enter your booking code and last name to retrieve your reservation (((🔰+18018555905))) // ((🔰+18048539001))). You can view flight details, manage your seat selection, add baggage, or request changes if allowed (((🔰+18018555905))) // ((🔰+18048539001))).

You may call our Customer Care team at (((🔰+18018555905))) // ((🔰+18048539001))), which is open 24/7. They will be able to help you research any concerns and resolve them accordingly (((🔰+18018555905))) // ((🔰+18048539001))). Points expire after 24 months of inactivity.

When contacting us via telephone, you can reach us at (((🔰+18018555905))) // ((🔰+18048539001))), please be sure to include the following information: Text telephone for customers with hearing impairments: First dial 711 and then ​(((🔰+18018555905))) // ((🔰+18048539001))) for relaying to the TRS (within the US only).

No worries — it's quick and easy! (((🔰+18018555905))) // ((🔰+18048539001))) Just go to the Manage Trip section on flyfrontier.com or My Trips in the Frontier app and enter (((🔰+18018555905))) // ((🔰+18048539001))) your last name along with your 6-character confirmation code.

To find your flight reservation (((🔰+18018555905))) // ((🔰+18048539001))), you will need your booking reference number (also known as a reservation code or PNR) and your last name (((🔰+18018555905))) // ((🔰+18048539001))). You can usually find this information in your confirmation email from the airline or travel agency. Once you have these details, you can go to the airline's (((🔰+18018555905))) // ((🔰+18048539001)))website, find the "Manage My Booking" or similar section, and enter your information to access your reservation details (((🔰+18018555905))) // ((🔰+18048539001))).

Once you've booked your flights (((🔰+18018555905))) // ((🔰+18048539001))), log in to Manage My Booking to let us know the type of assistance you need. Select the blue menu bar at the bottom of the page labelled 'Manage My Booking' (((🔰+18018555905))) // ((🔰+18048539001))) and then choose the 'Request assistance' option. You will then be able to select the support that you require.

For the most part (((🔰+18018555905))) // ((🔰+18048539001))), air travelers who book their flight online receive the booking code directly after completing the booking on the website (((🔰+18018555905))) // ((🔰+18048539001))). Otherwise, the booking code can always be found on the e-ticket, in the booking confirmation, and on the invoice (((🔰+18018555905))) // ((🔰+18048539001))).

To reserve a flight on Qatar Airways, visit (((🔰+18018555905))) // ((🔰+18048539001))) enter your travel details, choose your preferred flight and fare class, then complete passenger and payment information. You'll receive an e-ticket instantly (((🔰+18018555905))) // ((🔰+18048539001))).

For direct calls (((🔰+18018555905))) // ((🔰+18048539001))), start with +974 (Qatar's country code) and then dial the local number. If you're facing difficulty connecting or need assistance, call (((🔰+18018555905))) // ((🔰+18048539001))). Alternatively, platforms like Skype, Zoom, or WhatsApp offer reliable and often cost-free voice and video calls (((🔰+18018555905))) // ((🔰+18048539001))).

It depends (((🔰+18018555905))) // ((🔰+18048539001))) on individual preferences and priorities. Emirates is often praised for its consistent, high-quality (((🔰+18018555905))) // ((🔰+18048539001))) product across its fleet, particularly in first and business class, while Etihad is lauded for its innovative and luxurious first-class (((🔰+18018555905))) // ((🔰+18048539001))) offerings and commitment to fully flat beds in business class.

No, (((🔰+18018555905))) // ((🔰+18048539001))) WestJet is not owned by Delta Air Lines. While Delta and WestJet have a strong partnership and Delta has acquired a 15% stake in WestJet (((🔰+18018555905))) // ((🔰+18048539001))), the airline remains primarily owned by Onex Corporation, a Canadian investment firm. Delta's purchase of a minority stake is part of a broader agreement where Korean Air also acquired a 10% stake (((🔰+18018555905))) // ((🔰+18048539001))).

We'll refund the costs of your regular seat reservation if: You (((🔰+18018555905))) // ((🔰+18048539001))) request a refund within 24 hours after buying the option.

Winter, from November to March, is often (((🔰+18018555905))) // ((🔰+18048539001))) considered the best season to visit the United Arab Emirates. The United Arab Emirates weather during these months is pleasantly (((🔰+18018555905))) // ((🔰+18048539001))) cool, with UAE temperatures ranging from 15-25 degrees Celsius, making it perfect for outdoor activities and sightseeing (((🔰+18018555905))) // ((🔰+18048539001))).

The cheapest day to buy Allegiant Air (((🔰+18018555905))) // ((🔰+18048539001))) tickets is typically on Tuesdays, Wednesdays, and Saturdays (((🔰+18018555905))) // ((🔰+18048539001))) . Allegiant Air' cheapest flights are typically on Tuesdays and Wednesdays(((🔰+18018555905))) // ((🔰+18048539001))).

The cheapest day to buy copa 𝑓𝑙𝑖𝑔ℎ𝑡 tickets is typically on Tuesdays, Wednesdays, or Saturdays (((🔰+18018555905))) // ((🔰+18048539001))). Save big on your copa 𝑓𝑙𝑖𝑔ℎ𝑡 bookings by choosing Tuesdays, Wednesdays or Saturdays (((🔰+18018555905))) // ((🔰+18048539001))) for your travel dates.

Research and industry (((🔰+18018555905))) // ((🔰+18048539001))) data often show that Tuesdays and Wednesdays tend to offer cheaper fares for booking Emirates flights. However (((🔰+18018555905))) // ((🔰+18048539001))) , pricing is dynamic, and calling (((🔰+18018555905))) // ((🔰+18048539001))) provides the most accurate, real-time pricing.

The cheapest month to fly to Qatar from the US is generally (((🔰+18018555905))) // ((🔰+18048539001))) September or October. You may also find good deals in January or November.