Dominik Engel

Example Talk

Sat, 01 Jun 2030 13:00:00 +0000

Click on the Slides button above to view the built-in slides feature.

Slides can be added in a few ways:

Create slides using Hugo Blox Builder’s Slides feature and link using slides parameter in the front matter of the talk file
Upload an existing slide deck to static/ and link using url_slides parameter in the front matter of the talk file
Embed your slides (e.g. Google Slides) or presentation video on this page using shortcodes.

Further event details, including page elements such as image galleries, can be added to the body of this page.

Worker Harness

Thu, 16 Jul 2026 00:00:00 +0000

Worker Harness is an open-source control plane for running containerized GPU workers that self-register with an orchestrator over Tailscale or Headscale. It gives agents typed tools to discover workers, dispatch and monitor jobs, stream logs, and expose services through tunnels.

The project focuses on reproducible, observable compute orchestration across a private worker fleet.

ARCOL: Aspect Ratio Constrained Orthogonal Layout

Mon, 01 Jun 2026 00:00:00 +0000

Motivation

Orthogonal layouts make graphs easier to read in domains such as software, process, and biological-network visualization. Existing algorithms optimize local layout quality, but offer little control over the overall shape—a problem when the same graph must fit a wide display, a tall screen, or a fixed interface region.

Method

ARCOL extends the Human-like Orthogonal Layout Algorithm (HOLA) with a soft aspect-ratio objective. It introduces aspect-ratio awareness during core stress minimization and the subsequent placement of tree components, encouraging a requested shape while retaining the geometric constraints and readability conventions of orthogonal drawing.

Evaluation

The paper compares layouts across target aspect ratios and studies the resulting trade-offs in area, bends, crossings, and edge length. The evaluation shows that ARCOL can steer the global layout shape without treating aspect ratio as a purely post-processing operation.

Limitations

Aspect ratio is an additional layout objective, not a guarantee that every graph can meet an arbitrary target without compromises. Highly constrained graphs may still require trade-offs against compactness or other conventional quality measures.

EfficientMonoHair: Fast Strand-Level Reconstruction from Monocular Video via Multi-View Direction Fusion

Mon, 01 Jun 2026 00:00:00 +0000

Motivation

Strand-level hair reconstruction must balance global shape, fine directional detail, and runtime. Implicit methods can capture a coherent hair mass but lose individual strands, while explicit optimization can be accurate but expensive and difficult to scale.

Method

EfficientMonoHair combines an implicit neural representation with multi-view geometric fusion. It samples hair directions across views, aggregates them with a fusion-patch optimization, and enforces patch-level consistency to create a direction-aware outer-layer point cloud from which strands can be reconstructed.

Evaluation

The work compares reconstruction quality and efficiency with existing monocular-video approaches across varied hairstyles. Its evaluation emphasizes the benefit of using multi-view direction evidence without the sequential optimization burden of earlier strand-growing pipelines.

Limitations

Very complex, heavily occluded, or highly curled hairstyles remain difficult because visible image evidence can be sparse or ambiguous. As with other monocular methods, reconstruction quality is coupled to capture coverage and input-image quality.

ClickAIXR: On-Device Multimodal Vision-Language Interaction with Real-World Objects in Extended Reality

Wed, 01 Apr 2026 00:00:00 +0000

Motivation

Vision-language assistants can answer questions about the physical world, but cloud inference introduces privacy, connectivity, and latency concerns. In XR, they must also know precisely which nearby object the user intends to ask about.

Method

ClickAIXR combines local vision-language inference with a controller-driven object-selection interface on an XR headset. Users place and adjust a 3D crop around a physical object using depth, width, and height controls; the selected visual context is then passed to an on-device VLM with the spoken or typed question.

Evaluation

The paper evaluates the interaction on real-world objects, considering the accuracy of the selected context as well as the practical responsiveness of the on-device workflow. The system is positioned against cloud-based and gaze-based alternatives that leave privacy or referential ambiguity unresolved.

Limitations

The approach depends on the capabilities and compute budget of the headset-resident model, and careful selection remains necessary when objects are occluded, small, or spatially close together.

RaRa Clipper: A Clipper for Gaussian Splatting Based on Ray Tracer and Rasterizer

Mon, 01 Dec 2025 00:00:00 +0000

Motivation

Clipping is essential for inspecting interior structure in 3D scenes. For Gaussian Splatting, however, a primitive can contribute to many pixels across a volume, so simply discarding primitives at a clipping plane can produce imprecise boundaries and distracting artifacts.

Method

RaRa Clipper uses a hybrid renderer: rasterization provides efficient visibility handling, while ray tracing resolves the volumetric contributions needed for accurate clipping. The resulting pipeline is designed to preserve both the speed expected from splatting and the spatial fidelity needed at a cut boundary.

Evaluation

The paper compares RaRa Clipper with hard clipping across a set of clipping scenarios, combining image-based measures with participant feedback. The evaluation focuses on the visual clarity of the cut, preservation of surrounding context, and dataset-dependent preferences.

Limitations

Clipping quality remains dependent on the underlying Gaussian representation and viewing configuration. The work also identifies cases where retaining contextual background or choosing the right clipping configuration requires further user control.

S2D: Sparse-To-Dense Keymask Distillation for Unsupervised Video Instance Segmentation

Mon, 01 Dec 2025 00:00:00 +0000

A Survey on Quality Metrics for Text-to-Image Generation

Wed, 01 Oct 2025 00:00:00 +0000

Motivation

Text-to-image systems must be judged on more than photorealism: an image can look plausible while failing to depict the requested objects, relationships, or style. Classical image-quality measures alone do not capture this joint visual-and-linguistic requirement.

Method

This survey organizes text-to-image evaluation metrics around two main criteria: compositional image quality and semantic consistency with the prompt. It reviews metric families, the datasets used to validate them, and how evaluation choices interact with the intended generation task.

Evaluation

Beyond cataloguing metrics, the paper discusses the evidence behind them, surveys available evaluation datasets, and examines a selection of human-preference metrics in supplementary experiments. The result is a practical map of what a reported score can—and cannot—support.

Limitations

No single automatic metric captures all of human judgment. Metric selection remains task-dependent, and the survey emphasizes the need to combine appropriate automatic measures with carefully designed human evaluation when stakes are high.

CutS3D: Cutting Semantics in 3D for 2D Unsupervised Instance Segmentation

Wed, 01 Oct 2025 00:00:00 +0000

Attention-Guided Masked Autoencoders for Learning Image Representations

Sat, 01 Feb 2025 00:00:00 +0000

HPSCAN: Human Perception-Based Scattered Data Clustering

Sat, 01 Feb 2025 00:00:00 +0000

Motivation

Conventional clustering algorithms optimize mathematical criteria that do not necessarily match how people perceive groups in a scatterplot. HPSCAN asks a different question: which separations would human viewers recognize as clusters?

Method

The authors crowdsourced cluster annotations for 7,320 bivariate datasets from 384 participants. HPSCAN learns from these labels using a modified PointNet++ architecture that receives the scattered point coordinates directly, avoiding the resolution and rendering choices of image-based scatterplot analysis.

Evaluation

The work reports annotation statistics and compares HPSCAN’s separations with human judgments on held-out and real-world data. This makes the target of evaluation perceptual agreement, rather than only agreement with a geometric clustering heuristic.

Limitations

The model is trained on bivariate scatterplots and reflects the judgments represented in the collected dataset. Extending this notion of perceptual clustering to other visual encodings or higher-dimensional interaction contexts remains an open direction.

Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling

Tue, 27 Feb 2024 00:00:00 +0000

Deep Learning in Volume Rendering

Mon, 01 Jan 2024 00:00:00 +0000

Blog with Jupyter Notebooks!

Sat, 04 Nov 2023 00:00:00 +0000

from IPython.core.display import Image
Image('https://www.python.org/static/community_logos/python-logo-master-v3-TM-flattened.png')

print("Welcome to Academic!")

Welcome to Academic!

Organize your notebooks

Place the notebooks that you would like to publish in a notebooks folder at the root of your website.

Import the notebooks into your site

pipx install academic
academic import 'notebooks/**.ipynb' content/post/ --verbose

The notebooks will be published to the folder you specify above. In this case, they will be published to your content/post/ folder.

Leveraging Self-Supervised Vision Transformers for Segmentation-based Transfer Function Design

Sun, 03 Sep 2023 00:00:00 +0000

Motivation

Transfer functions determine which structures are visible in a volume rendering, yet designing them from intensity and gradient values is often tedious and unintuitive. The goal is to let users specify structures directly rather than manually engineer a mapping in feature space.

Method

The workflow exposes a slice viewer in which a user marks a structure of interest. A self-supervised, pre-trained vision transformer supplies high-level features; similar features are selected across the volume to form a segmentation that drives the transfer function. Because the method relies on pre-trained features rather than per-volume training, edits can be reflected immediately.

Evaluation

The paper demonstrates annotation-guided transfer-function design on volume data and evaluates whether feature-based selections isolate target structures from only a small amount of user input. It also contrasts the interactive workflow with previous learning-based approaches that require costly optimization for each volume.

Limitations

The result depends on the discriminative power of the pre-trained features and on the ambiguity of the selected structure. Sparse annotations are powerful, but may need refinement when visually similar structures should receive different optical properties.

Monocular Depth Decomposition of Semi-Transparent Volume Renderings

Mon, 27 Jun 2022 00:00:00 +0000

Motivation

Depth is ambiguous in semi-transparent volume rendering: several structures can contribute to one pixel, and there may be no single visible surface. A useful depth estimate nevertheless enables labels, geometry, and additional shading to be composed with a rendering.

Method

The work studies depth definitions for volume-rendered images and adapts monocular depth networks to predict a layered decomposition. From one RGB rendering, the model estimates front semi-transparent content, the depth of a surface of interest, and structures behind it; the layers can be recomposited with new scene elements.

Evaluation

The evaluation compares depth-estimation approaches across different opacity and illumination settings, and assesses the additional color and opacity predictions needed for layered reconstruction. The resulting layers support downstream re-composition tasks rather than serving only as a scalar depth map.

Limitations

The decomposition is learned from rendered imagery and inherits ambiguities from the rendering setup. It is therefore most useful as an image-space representation for interactive composition, not as a replacement for the original volumetric scene model.

Differentiable Electron Microscopy Simulation: Methods and Applications for Visualization

Sun, 26 Jun 2022 00:00:00 +0000

Motivation

Electron-microscopy data are expensive to acquire, noisy, and difficult to annotate. Faithful simulation can supply controlled ground truth, but conventional simulators are often too slow or too rigid to support optimization and learning.

Method

DiffTEM simulates micrographs from atomistic models at a scale suitable for many viral particles. Both the deterministic image-formation stages and the stochastic signal-and-noise stages are differentiable, allowing gradients to flow from an image-space objective back to simulation and detector parameters.

Evaluation

The paper demonstrates two uses of differentiability: estimating detector parameters automatically and using simulated data to support denoising of real microscopy observations. It also evaluates the simulator’s visual plausibility and scalability relative to previous simulation approaches.

Limitations

A simulator remains an approximation of a complex physical acquisition process. Its usefulness depends on how well the modeled specimen, microscope, and noise assumptions match the data to which it is applied.

Finding Nano-Ötzi: Cryo-Electron Tomography Visualization Guided by Learned Segmentation

Tue, 06 Apr 2021 00:00:00 +0000

Motivation

Cryo-electron tomography contains valuable nanoscale structure but suffers from severe noise, making direct volume rendering and transfer-function design difficult. Experts need a way to expose meaningful structure without manually tuning a complex visual mapping for every dataset.

Method

Nano-Ötzi decomposes visual mapping into two tasks. A semi-supervised pipeline turns sparse user input into soft segmentation through pseudo labels and a stronger classifier; an iterative thresholding method then combines raw-data detail with soft labels to estimate opacity. The result is a volume-rendering workflow tailored to noisy cryo-ET data.

Evaluation

The paper demonstrates the pipeline on challenging cryo-ET data and evaluates its individual components. Feedback from two domain experts complements the technical assessment by examining whether the resulting visualizations support real scientific interpretation.

Limitations

The learned segmentation stage benefits from substantial pre-training data, and sparse labels may not resolve every ambiguous structure. The system is designed to support expert exploration rather than remove the need for domain judgment.

Property-Based Testing for Visualization Development

Mon, 15 Mar 2021 00:00:00 +0000

Motivation

Visualization software can expose many rendering parameters, while conventional regression tests usually cover only a small set of hand-picked configurations. Bugs may therefore remain hidden until a particular combination of values is encountered.

Method

The work applies property-based testing to visualization development. It generates many parameter settings automatically, renders paired configurations, and checks whether the expected visual effect of a controlled parameter change is present. These properties can be defined alongside an algorithm, making the approach compatible with test-driven development.

Evaluation

The approach is integrated into Inviwo’s regression-testing infrastructure and exercised through visualization use cases. The examples show how automatically generated parameter combinations can expose errors that are unlikely to be covered by fixed image baselines alone.

Limitations

A property-based test is only as useful as the property it encodes. Designing robust visual assertions and diagnosing a failed generated case still require developer insight, especially when rendering differences are subtle or data-dependent.

Deep Volumetric Ambient Occlusion

Fri, 01 Jan 2021 00:00:00 +0000

Motivation

Ambient occlusion improves depth perception in direct volume rendering, but high-quality volumetric illumination can be expensive to compute. DVAO investigates whether a learned model can precompute an ambient-occlusion volume for the current transfer function, leaving view changes interactive.

Method

DVAO is a 3D convolutional encoder–decoder that takes a scalar volume and transfer-function context and predicts one ambient-occlusion value per voxel. The strongest variant preclassifies the volume with transfer-function opacity, giving the network a spatially aligned, implicit representation of the global transfer function from its first layer.

The selected implicit/preclassified strategy combines opacity and intensity volumes before the encoder and predicts a 128³ ambient-occlusion volume. Adapted from Fig. 6 of the arXiv version.

Evaluation

Across 30 training runs covering six transfer-function injection strategies and five loss functions, the implicit preclassified input performed best; the selected DSSIM-2D + MSE model reached SSIM 0.875 and MSE 0.008 on held-out data.
The default 16-filter model required 411 ms per transfer-function update on an RTX 2070. It is not evaluated every frame, so camera interaction remains responsive after the occlusion volume is produced.
Although trained only on human head CT data, it obtained SSIM scores of 0.786 on mouse micro-CT, 0.866 on chameleon CT, 0.751 on MRI, and 0.869 on electron microscopy data.

Limitations

The prototype operates on 128³ volumes and needs substantial memory for 3D CNN inference. It was a research exploration of learned volumetric illumination rather than a replacement for optimized algorithmic ambient-occlusion methods.

Welcome to Hugo Blox Builder, the website builder for Hugo

Sun, 13 Dec 2020 00:00:00 +0000

import libr
print('hello')

Overview

The Wowchemy website builder for Hugo, along with its starter templates, is designed for professional creators, educators, and teams/organizations - although it can be used to create any kind of site
The template can be modified and customised to suit your needs. It’s a good platform for anyone looking to take control of their data and online identity whilst having the convenience to start off with a no-code solution (write in Markdown and customize with YAML parameters) and having flexibility to later add even deeper personalization with HTML and CSS
You can work with all your favourite tools and apps with hundreds of plugins and integrations to speed up your workflows, interact with your readers, and much more

Get Started

👉 Create a new site
📚 Personalize your site
💬 Chat with the Wowchemy community or Hugo community
🐦 Twitter: @wowchemy @GeorgeCushen #MadeWithWowchemy
💡 Request a feature or report a bug for Wowchemy
⬆️ Updating Wowchemy? View the Update Tutorial and Release Notes

Crowd-funded open-source software

To help us develop this template and software sustainably under the MIT license, we ask all individuals and businesses that use it to help support its ongoing maintenance and development via sponsorship.

As a token of appreciation for sponsoring, you can unlock these awesome rewards and extra features 🦄✨

Ecosystem

Hugo Academic CLI: Automatically import publications from BibTeX

Inspiration

Check out the latest demo of what you’ll get in less than 10 minutes, or view the showcase of personal, project, and business sites.

Features

Page builder - Create anything with widgets and elements
Edit any type of content - Blog posts, publications, talks, slides, projects, and more!
Create content in Markdown, Jupyter, or RStudio
Plugin System - Fully customizable color and font themes
Display Code and Math - Code highlighting and LaTeX math supported
Integrations - Google Analytics, Disqus commenting, Maps, Contact Forms, and more!
Beautiful Site - Simple and refreshing one page design
Industry-Leading SEO - Help get your website found on search engines and social media
Media Galleries - Display your images and videos with captions in a customizable gallery
Mobile Friendly - Look amazing on every screen with a mobile friendly version of your site
Multi-language - 34+ language packs including English, 中文, and Português
Multi-user - Each author gets their own profile page
Privacy Pack - Assists with GDPR
Stand Out - Bring your site to life with animation, parallax backgrounds, and scroll effects
One-Click Deployment - No servers. No databases. Only files.

Themes

Wowchemy and its templates come with automatic day (light) and night (dark) mode built-in. Alternatively, visitors can choose their preferred mode - click the moon icon in the top right of the Demo to see it in action! Day/night mode can also be disabled by the site admin in params.toml.

Choose a stunning theme and font for your site. Themes are fully customizable.

License

Released under the MIT license.

Inviwo Visualization Framework

Thu, 27 Aug 2020 00:00:00 +0000

Inviwo is an open-source framework for interactive visualization of scientific data written in C++ and OpenGL. Inviwo enables visualization of geometry, scalar fields, vector fields, molecules and more. I have contributed to the core framework, text and video documentation, testability, CI, Python integration, as well as several modules to introduce deep neural networks to the framework.

torchvtk

Sat, 01 Aug 2020 00:00:00 +0000

torchvtk is a PyTorch-based framework for efficient loading, caching and transformation of volumetric data, addressing common challenges like IO bottlenecks. I started this project with a colleague and used it for my research. The project is no longer in development as many alternatives emerged in the recent years.

Writing technical content in Markdown

Fri, 12 Jul 2019 00:00:00 +0000

Hugo Blox Builder is designed to give technical content creators a seamless experience. You can focus on the content and Wowchemy handles the rest.

Highlight your code snippets, take notes on math classes, and draw diagrams from textual representation.

On this page, you’ll find some examples of the types of technical content that can be rendered with Wowchemy.

Examples

Code

Wowchemy supports a Markdown extension for highlighting code syntax. You can customize the styles under the syntax_highlighter option in your config/_default/params.yaml file.

```python
import pandas as pd
data = pd.read_csv("data.csv")
data.head()
```

renders as

import pandas as pd
data = pd.read_csv("data.csv")
data.head()

Mindmaps

Wowchemy supports a Markdown extension for mindmaps.

Simply insert a Markdown markmap code block and optionally set the height of the mindmap as shown in the example below.

A simple mindmap defined as a Markdown list:


```markmap {height="200px"}
- Hugo Modules
- wowchemy
- blox-plugins-netlify
- blox-plugins-netlify-cms
- blox-plugins-reveal
```

renders as

- Hugo Modules
- wowchemy
- blox-plugins-netlify
- blox-plugins-netlify-cms
- blox-plugins-reveal

A more advanced mindmap with formatting, code blocks, and math:


```markmap
- Mindmaps
- Links
- [Wowchemy Docs](https://docs.hugoblox.com/)
- [Discord Community](https://discord.gg/z8wNYzb)
- [GitHub](https://github.com/HugoBlox/hugo-blox-builder)
- Features
- Markdown formatting
- **inline** ~~text~~ *styles*
- multiline
text
- `inline code`
-
```js
console.log('hello');
console.log('code block');
```
- Math: $x = {-b \pm \sqrt{b^2-4ac} \over 2a}$
```

renders as

- Mindmaps
- Links
- [Wowchemy Docs](https://docs.hugoblox.com/)
- [Discord Community](https://discord.gg/z8wNYzb)
- [GitHub](https://github.com/HugoBlox/hugo-blox-builder)
- Features
- Markdown formatting
- **inline** ~~text~~ *styles*
- multiline
text
- `inline code`
-
```js
console.log('hello');
console.log('code block');
```
- Math: $x = {-b \pm \sqrt{b^2-4ac} \over 2a}$

Charts

Wowchemy supports the popular Plotly format for interactive charts.

Save your Plotly JSON in your page folder, for example line-chart.json, and then add the {{< chart data="line-chart" >}} shortcode where you would like the chart to appear.

Demo:

You might also find the Plotly JSON Editor useful.

Math

Wowchemy supports a Markdown extension for $\LaTeX$ math. You can enable this feature by toggling the math option in your config/_default/params.yaml file.

To render inline or block math, wrap your LaTeX math with {{< math >}}$...${{< /math >}} or {{< math >}}$$...$${{< /math >}}, respectively. (We wrap the LaTeX math in the Wowchemy math shortcode to prevent Hugo rendering our math as Markdown. The math shortcode is new in v5.5-dev.)

Example math block:

{{< math >}}
$$
\gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}
$$
{{< /math >}}

renders as

$$\gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}$$

Example inline math {{< math >}}$\nabla F(\mathbf{x}_{n})${{< /math >}} renders as $\nabla F(\mathbf{x}_{n})$.

Example multi-line math using the math linebreak (\\):

{{< math >}}
$$f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\
1-p_{0}^{*} & \text{if }k=0.\end{cases}$$
{{< /math >}}

renders as

$$ f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\ 1-p_{0}^{*} & \text{if }k=0.\end{cases} $$

Diagrams

Wowchemy supports a Markdown extension for diagrams. You can enable this feature by toggling the diagram option in your config/_default/params.toml file or by adding diagram: true to your page front matter.

An example flowchart:

```mermaid
graph TD
A[Hard] -->|Text| B(Round)
B --> C{Decision}
C -->|One| D[Result 1]
C -->|Two| E[Result 2]
```

renders as

graph TD A[Hard] -->|Text| B(Round) B --> C{Decision} C -->|One| D[Result 1] C -->|Two| E[Result 2]

An example sequence diagram:

```mermaid
sequenceDiagram
Alice->>John: Hello John, how are you?
loop Healthcheck
John->>John: Fight against hypochondria
end
Note right of John: Rational thoughts!
John-->>Alice: Great!
John->>Bob: How about you?
Bob-->>John: Jolly good!
```

renders as

sequenceDiagram Alice->>John: Hello John, how are you? loop Healthcheck John->>John: Fight against hypochondria end Note right of John: Rational thoughts! John-->>Alice: Great! John->>Bob: How about you? Bob-->>John: Jolly good!

An example Gantt diagram:

```mermaid
gantt
section Section
Completed :done, des1, 2014-01-06,2014-01-08
Active :active, des2, 2014-01-07, 3d
Parallel 1 : des3, after des1, 1d
Parallel 2 : des4, after des1, 1d
Parallel 3 : des5, after des3, 1d
Parallel 4 : des6, after des4, 1d
```

renders as

gantt section Section Completed :done, des1, 2014-01-06,2014-01-08 Active :active, des2, 2014-01-07, 3d Parallel 1 : des3, after des1, 1d Parallel 2 : des4, after des1, 1d Parallel 3 : des5, after des3, 1d Parallel 4 : des6, after des4, 1d

An example class diagram:

```mermaid
classDiagram
Class01 <|-- AveryLongClass : Cool
Class03 *-- Class04
Class05 o-- Class06
Class07 .. Class08
Class09 --> C2 : Where am i?
Class09 --* C3
Class09 --|> Class07
Class07 : equals()
Class07 : Object[] elementData
Class01 : size()
Class01 : int chimp
Class01 : int gorilla
Class08 <--> C2: Cool label
```

renders as

classDiagram Class01 <|-- AveryLongClass : Cool Class03 *-- Class04 Class05 o-- Class06 Class07 .. Class08 Class09 --> C2 : Where am i? Class09 --* C3 Class09 --|> Class07 Class07 : equals() Class07 : Object[] elementData Class01 : size() Class01 : int chimp Class01 : int gorilla Class08 <--> C2: Cool label

An example state diagram:

```mermaid
stateDiagram
[*] --> Still
Still --> [*]
Still --> Moving
Moving --> Still
Moving --> Crash
Crash --> [*]
```

renders as

stateDiagram [*] --> Still Still --> [*] Still --> Moving Moving --> Still Moving --> Crash Crash --> [*]

Todo lists

You can even write your todo lists in Markdown too:

- [x] Write math example
 - [x] Write diagram example
- [ ] Do something else

renders as

Write math example
- Write diagram example
Do something else

Tables

Save your spreadsheet as a CSV file in your page’s folder and then render it by adding the Table shortcode to your page:

{{< table path="results.csv" header="true" caption="Table 1: My results" >}}

renders as

Table 1: My results
customer_id	score
1	0
2	0.5
3	1

Callouts

Academic supports a shortcode for callouts, also referred to as asides, hints, or alerts. By wrapping a paragraph in {{% callout note %}} ... {{% /callout %}}, it will render as an aside.

{{% callout note %}}
A Markdown aside is useful for displaying notices, hints, or definitions to your readers.
{{% /callout %}}

renders as

A Markdown aside is useful for displaying notices, hints, or definitions to your readers.

Spoilers

Add a spoiler to a page to reveal text, such as an answer to a question, after a button is clicked.

{{< spoiler text="Click to view the spoiler" >}}
You found me!
{{< /spoiler >}}

renders as

Click to view the spoiler

You found me!

Icons

Academic enables you to use a wide range of icons from Font Awesome and Academicons in addition to emojis.

Here are some examples using the icon shortcode to render icons:

{{< icon name="terminal" pack="fas" >}} Terminal
{{< icon name="python" pack="fab" >}} Python
{{< icon name="r-project" pack="fab" >}} R

renders as

Terminal
Python
R

Slides

Tue, 05 Feb 2019 00:00:00 +0000

Create slides in Markdown with Hugo Blox Builder

Hugo Blox Builder | Documentation

Features

Efficiently write slides in Markdown
3-in-1: Create, Present, and Publish your slides
Supports speaker notes
Mobile friendly slides

Controls

Next: Right Arrow or Space
Previous: Left Arrow
Start: Home
Finish: End
Overview: Esc
Speaker notes: S
Fullscreen: F
Zoom: Alt + Click
PDF Export

Code Highlighting

Inline code: variable

Code block:

porridge = "blueberry"
if porridge == "blueberry":
 print("Eating...")

Math

In-line math: $x + y = z$

Block math:

$$ f\left( x \right) = ;\frac{{2\left( {x + 4} \right)\left( {x - 4} \right)}}{{\left( {x + 4} \right)\left( {x + 1} \right)}} $$

Fragments

Make content appear incrementally

{{% fragment %}} One {{% /fragment %}}
{{% fragment %}} **Two** {{% /fragment %}}
{{% fragment %}} Three {{% /fragment %}}

Press Space to play!

One Two Three

A fragment can accept two optional parameters:

class: use a custom style (requires definition in custom CSS)
weight: sets the order in which a fragment appears

Speaker Notes

Add speaker notes to your presentation

{{% speaker_note %}}

- Only the speaker can read these notes
- Press `S` key to view
 {{% /speaker_note %}}

Press the S key to view the speaker notes!

Themes

black: Black background, white text, blue links (default)
white: White background, black text, blue links
league: Gray background, white text, blue links
beige: Beige background, dark text, brown links
sky: Blue background, thin dark text, blue links

night: Black background, thick white text, orange links
serif: Cappuccino background, gray text, brown links
simple: White background, black text, blue links
solarized: Cream-colored background, dark green text, blue links

Custom Slide

Customize the slide style and background

{{< slide background-image="/media/boards.jpg" >}}
{{< slide background-color="#0000FF" >}}
{{< slide class="my-style" >}}

Custom CSS Example

Let’s make headers navy colored.

Create assets/css/reveal_custom.css with:

.reveal section h1,
.reveal section h2,
.reveal section h3 {
 color: navy;
}

Questions?

Ask

Documentation

Dominik Engel

Example Talk

Worker Harness

ARCOL: Aspect Ratio Constrained Orthogonal Layout

Motivation

Method

Evaluation

Limitations

EfficientMonoHair: Fast Strand-Level Reconstruction from Monocular Video via Multi-View Direction Fusion

Motivation

Method

Evaluation

Limitations

ClickAIXR: On-Device Multimodal Vision-Language Interaction with Real-World Objects in Extended Reality

Motivation

Method

Evaluation

Limitations

RaRa Clipper: A Clipper for Gaussian Splatting Based on Ray Tracer and Rasterizer

Motivation

Method

Evaluation

Limitations

S2D: Sparse-To-Dense Keymask Distillation for Unsupervised Video Instance Segmentation

A Survey on Quality Metrics for Text-to-Image Generation

Motivation

Method

Evaluation

Limitations

CutS3D: Cutting Semantics in 3D for 2D Unsupervised Instance Segmentation

Attention-Guided Masked Autoencoders for Learning Image Representations

HPSCAN: Human Perception-Based Scattered Data Clustering

Motivation

Method

Evaluation

Limitations

Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling

Deep Learning in Volume Rendering

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Leveraging Self-Supervised Vision Transformers for Segmentation-based Transfer Function Design

Motivation

Method

Evaluation

Limitations

Monocular Depth Decomposition of Semi-Transparent Volume Renderings

Motivation

Method

Evaluation

Limitations

Differentiable Electron Microscopy Simulation: Methods and Applications for Visualization

Motivation

Method

Evaluation

Limitations

Finding Nano-Ötzi: Cryo-Electron Tomography Visualization Guided by Learned Segmentation

Motivation

Method

Evaluation

Limitations

Property-Based Testing for Visualization Development

Motivation

Method

Evaluation

Limitations

Deep Volumetric Ambient Occlusion

Motivation

Method

Evaluation

Limitations

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