MCFGes

<h3>Dataset Description</h3> <p>The dataset consists of 105 classes and 2264 samples, collected using an RGB camera and two IWR6843 ISK mmWave radars. The data is stored in a structured format designed to facilitate multimodal and cross-domain few-shot gesture recognition research.</p> <h3>Data Organization</h3> <p>The dataset file <em>pickle.pkl</em> is organized into a dictionary where each key corresponds to a <code>class_idx</code>. The value associated with each key is a list containing samples from that class.</p> <p>dataset/<br>│<br>├── class_001/<br>│   ├── sample_0001<br>│   ├── sample_0002<br>│   ...<br>│<br>├── class_002/<br>│   ├── sample_0001<br>│   ├── sample_0002<br>│   ...<br>│<br>...</p> <p>Each sample is a dictionary with the following keys:</p> <ul> <li><code>mm_cloud1</code>: Numpy array data from Radar 1, as described in the associated paper.</li> <li><code>mm_cloud1_mask</code>: Mask data indicating real gesture data versus padding (zeros) for Radar 1.</li> <li><code>label</code>: The label identifying the gesture class.</li> <li><code>mm_cloud2</code>: Numpy array data from Radar 2.</li> <li><code>mm_cloud2_mask</code>: Mask data indicating real gesture data versus padding (zeros) for Radar 2.</li> <li><code>vis_rgb</code>: Video data from the RGB camera.</li> <li><code>vis_rgb_mask</code>: Mask data indicating real gesture data versus padding (zeros) for the RGB video.</li> </ul> <h3>Dataset loading</h3> <p>All data undergoes preprocessing and is loaded into batches suitable for model input via the <code>fs_dataset.py</code> script, ensuring that the data is ready for use in machine learning models.</p> <p><strong>Spilit Table</strong></p> <p>The six cross-domain split tables are:</p> <p>split_table_micro.json for MicroGesture(MG)</p> <p>spilt_table_meeting.json for MeetingRoom(MR)</p> <p>split_table_out_door.json for Outdoor(OD)</p> <p>split_table_home.json for Home(H)</p> <p>split_table_static.json for VR(V)</p> <p>split_table_multi_peope.json (MP)</p> <h3><strong>Design Patterns in the Project</strong></h3> <p><strong>Singleton Pattern:</strong> Utilized to manage parameter modules consistently across the project. Readers can adjust settings or introduce new configurations in the <code>config</code> directory to initiate different experiments.</p> <p><strong>Builder Pattern:</strong> This pattern is used to enable easy expansion of components such as the training engine, dataset, and model. By adding new build methods, users can enhance the project's capabilities and adapt it to new requirements.</p> <p><strong>Factory Pattern:</strong> Supports the registration of new models through decorators in various factories, allowing for the broadening of experimental scope and flexibility.</p> <p><strong>Overall Benefits:</strong> These design patterns are implemented to streamline the construction and expansion of experiments, promoting efficient development and scalable architecture.</p> <p><strong>Run:</strong></p> <p>You can edit the script.py and add config file to run your own experiment.</p> <p><strong>Others:</strong></p> <p>There are some git configs and pycache i forget to delete before uploading. Just delete or ignore them.</p> <p>Some __init__.py files contains some import will not be used, just delete them either.</p&gt

Using Predation Rates to Assess the Function of Restored Spartina alterniflora marshe

The worldwide decline of salt marshes has resulted in the loss of critical ecosystem services. Coastal resilience efforts seek to mitigate marsh loss by restoring these coastal wetlands. It is often unclear whether restored marshes perform equivalent functions as natural marshes. To determine if trophic interactions differ in restored and natural marshes, field tethering experiments were executed in restored and natural Spartina alterniflora marshes along the Connecticut coastline in June and July 2017. Mud snails and Asian shore crabs were tethered within six locations: 1) a restored marsh planted in 2015 at Stratford Point, 2) an adjacent restored marsh planted in April 2017 at Stratford Point, 3) a natural remnant marsh at Stratford Point, 4) a recolonizing marsh at Stratford Point, 5) a natural marsh situated at Milford Point, across the Housatonic River from the Stratford Point restoration site, and 6) a natural marsh in Guilford. The number of prey consumed in each location for the Snail survival was assessed after 10 days; crab survival was assessed after 48 hours, and Chi- square test was employed to determine if consumption differs across locations. We also measured the Spartina alterniflora densities and stem height at each of the locations. The snails survived well in both restored marshes in Stratford where Spartina densities were low. Crab survival was highest in Guilford where Spartina densities were highest. Our results suggest that marshes with taller but less dense vegetation than is present in our restored marshes are better foraging grounds for species that feed on small crab

Carte du Lac de Mexico et de ses Environs Lors de la Conquete des Espagnols. Serie Zonas Regionales

Contiene viñeta para el título

Plan du port d' Acapulco

La carta forma parte de la publicación: Le historie generale de vouyages.</p