arXiv:2511.00345v1 Announce Type: cross 
Abstract: Accurate and up-to-date geospatial data are essential for urban planning, infrastructure monitoring, and environmental management. Yet, automating urban monitoring remains difficult because curated datasets of specific urban features and their changes are scarce. We introduce OSMGen, a generative framework that creates realistic satellite imagery directly from raw OpenStreetMap (OSM) data. Unlike prior work that relies on raster tiles, OSMGen uses the full richness of OSM JSON, including vector geometries, semantic tags, location, and time, giving fine-grained control over how scenes are generated. A central feature of the framework is the ability to produce consistent before-after image pairs: user edits to OSM inputs translate into targeted visual changes, while the rest of the scene is preserved. This makes it possible to generate training data that addresses scarcity and class imbalance, and to give planners a simple way to preview proposed interventions by editing map data. More broadly, OSMGen produces paired (JSON, image) data for both static and changed states, paving the way toward a closed-loop system where satellite imagery can automatically drive structured OSM updates. Source code is available at https://github.com/amir-zsh/OSMGen.

OSMGen هو إطار مبتكر يقوم بإنشاء صور فضائية واقعية باستخدام بيانات OpenStreetMap الخام. هذه الابتكار يعالج تحديات المراقبة الحضرية من خلال توفير بيانات جغرافية دقيقة ومحدثة، وهي ضرورية للتخطيط الحضري وإدارة البنية التحتية.

OSMGen es un marco innovador que genera imágenes satelitales realistas utilizando datos en bruto de OpenStreetMap. Esta innovación aborda los desafíos de la monitorización urbana al proporcionar datos geoespaciales precisos y actualizados, que son cruciales para la planificación urbana y la gestión de infraestructuras.

OSMGen est un cadre révolutionnaire qui génère des images satellites réalistes à partir de données brutes d'OpenStreetMap. Cette innovation répond aux défis de la surveillance urbaine en fournissant des données géospatiales précises et à jour, essentielles pour la planification urbaine et la gestion des infrastructures.

OSMGen is a groundbreaking framework that generates realistic satellite images using raw OpenStreetMap data. This innovation addresses the challenges of urban monitoring by providing accurate and up-to-date geospatial data, which is crucial for urban planning and infrastructure management.

OSMGen: Highly Controllable Satellite Image Synthesis using OpenStreetMap Data

أطلقت DAZN رسميًا خدمتها على أمازون برايم فيديو في الولايات المتحدة والمملكة المتحدة، مما يسهل على عشاق الرياضة الوصول إلى الأحداث المباشرة والمحتوى الحصري. هذه الشراكة مهمة لأنها تجمع بين العروض الرياضية الواسعة لـ DAZN وقاعدة عملاء أمازون الكبيرة، مما قد يعزز تجربة المشاهدة ويزيد من الاشتراكات لكلا المنصتين.

DAZN ha lanzado oficialmente su servicio en Amazon Prime Video en los Estados Unidos y el Reino Unido, facilitando el acceso de los aficionados al deporte a eventos en vivo y contenido exclusivo. Esta asociación es significativa ya que combina la amplia oferta deportiva de DAZN con la vasta base de clientes de Amazon, lo que podría mejorar la experiencia del espectador y aumentar las suscripciones para ambas plataformas.

DAZN a officiellement lancé son service sur Amazon Prime Video aux États-Unis et au Royaume-Uni, facilitant ainsi l'accès des fans de sport aux événements en direct et au contenu exclusif. Ce partenariat est important car il combine l'offre sportive étendue de DAZN avec la vaste clientèle d'Amazon, ce qui pourrait améliorer l'expérience des téléspectateurs et augmenter les abonnements pour les deux plateformes.

DAZN has officially launched its service on Amazon Prime Video in the US and UK, making it easier for sports fans to access live events and exclusive content. This partnership is significant as it combines DAZN's extensive sports offerings with Amazon's vast customer base, potentially enhancing viewer experience and increasing subscriptions for both platforms.

DAZN is available through Amazon Prime Video in the US and UK

<img width="1741" height="990" src="https://the-decoder.com/wp-content/uploads/2024/02/sable_diffusion_3_examples-1.png" class="attachment-full size-full wp-post-image" alt="" style="height: auto; margin-bottom: 10px;" decoding="async" fetchpriority="high" />
 The High Court in London has thrown out Getty Images' main copyright case against Stability AI, marking a key moment for generative AI developers. The case focused on whether building an AI model from copyrighted images counts as copyright infringement.
The article <a href="https://the-decoder.com/uk-judge-rules-that-ai-image-generator-stable-diffusion-is-not-an-infringing-copy/">UK judge rules that AI image generator Stable Diffusion is not an &quot;infringing copy&quot;</a> appeared first on <a href="https://the-decoder.com">THE DECODER</a>.

حكم مهم من المحكمة العليا في لندن قضى بأن مولد الصور بالذكاء الاصطناعي Stable Diffusion لا ينتهك قوانين حقوق الطبع والنشر، كما زعمت Getty Images. هذه القرار حاسم لمستقبل الذكاء الاصطناعي التوليدي، حيث يضع سابقة قد تشجع على الابتكار والتطوير في هذا المجال. من خلال التأكيد على أنه يمكن بناء نماذج الذكاء الاصطناعي دون انتهاك حقوق الطبع والنشر، فتحت المحكمة الباب أمام تطبيقات أكثر إبداعًا لتكنولوجيا الذكاء الاصطناعي، مما قد يعود بالنفع على الفنانين والمطورين على حد سواء.

Un fallo significativo del Tribunal Superior de Londres ha determinado que el generador de imágenes AI Stable Diffusion no infringe las leyes de derechos de autor, como alegaba Getty Images. Esta decisión es crucial para el futuro de la IA generativa, ya que establece un precedente que podría fomentar la innovación y el desarrollo en el campo. Al afirmar que se pueden construir modelos de IA sin violar los derechos de autor, el tribunal ha abierto la puerta a aplicaciones más creativas de la tecnología de IA, lo que podría beneficiar tanto a artistas como a desarrolladores.

Un jugement important du tribunal de grande instance de Londres a déterminé que le générateur d'images AI Stable Diffusion ne viole pas les lois sur le droit d'auteur, comme l'affirmait Getty Images. Cette décision est cruciale pour l'avenir de l'IA générative, car elle établit un précédent qui pourrait encourager l'innovation et le développement dans ce domaine. En affirmant que des modèles d'IA peuvent être construits sans violer le droit d'auteur, le tribunal a ouvert la voie à des applications plus créatives de la technologie AI, ce qui pourrait bénéficier aux artistes et aux développeurs.

A significant ruling from the High Court in London has determined that the AI image generator Stable Diffusion does not infringe on copyright laws, as claimed by Getty Images. This decision is crucial for the future of generative AI, as it sets a precedent that could encourage innovation and development in the field. By affirming that AI models can be built without violating copyright, the court has opened the door for more creative applications of AI technology, which could benefit artists and developers alike.

UK judge rules that AI image generator Stable Diffusion is not an "infringing copy"

<h2>
 
 
 The Problem
</h2>

<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fw8cox74zkbcd0tqhipg2.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fw8cox74zkbcd0tqhipg2.png" alt=" " width="701" height="512"></a>

I spent a while trying to get a custom Windows XP-style start button to display correctly on my Linux Mint Cinnamon desktop. The button would work sometimes, but the solution was fragile and caused unintended side effects - like the start button image appearing in the Linux Mint Welcome Screen's lower right corner.

<h2>
 
 
 What Didn't Work
</h2>

<h3>
 
 
 The gsettings Approach ❌
</h3>

Initially, I tried using <code>gsettings</code> to force the icon: 


<div class="highlight js-code-highlight">
<pre class="highlight shell"><code>gsettings set com.linuxmint.mintmenu applet-icon "file:///home/user/Desktop/start-button.svg"
</code></pre>

</div>



Why this failed:

<ul>
<li>This sets a system-wide configuration that affects all applications</li>
<li>The start button image leaked into other parts of the system (Welcome Screen)</li>
<li>It created "messes" that looked unprofessional</li>
</ul>

<h3>
 
 
 The Base64 Data URI Approach ❌
</h3>

Next, I embedded the PNG image directly into the CSS as a base64 data URI: 


<div class="highlight js-code-highlight">
<pre class="highlight css"><code>background-image: url("data:image/png;base64,iVBORw0KGgo...") !important;
</code></pre>

</div>



Why this failed:

<ul>
<li>The base64 string was enormous (12KB+ image encoded)</li>
<li>The image simply wouldn't display despite correct CSS selectors</li>
<li>Overly complex and hard to maintain</li>
<li>The approach was "overworked" - too much effort for what should be simple</li>
</ul>

<h2>
 
 
 What Finally Worked ✅
</h2>

<h3>
 
 
 The Simple File Path Solution
</h3>

The solution is elegantly simple: 
Use a relative file path in CSS. 


<div class="highlight js-code-highlight">
<pre class="highlight css"><code>background-image: url("assets/start-button.png") !important;
</code></pre>

</div>



That's it. No base64 encoding. No gsettings. Just a clean, relative file path.

<h2>
 
 
 Step-by-Step Implementation
</h2>

<h3>
 
 
 1. Prepare Your Image
</h3>

Place your start button image in the theme's assets directory: 


<div class="highlight js-code-highlight">
<pre class="highlight shell"><code>~/.themes/Mint-XP/cinnamon/assets/start-button.png
</code></pre>

</div>



Image specs:

<ul>
<li>Dimensions: 122×40 pixels</li>
<li>Format: PNG (transparent background recommended)</li>
<li>Size: ~12KB</li>
</ul>

<h3>
 
 
 2. Edit the Theme CSS
</h3>

Open your theme's CSS file: 


<div class="highlight js-code-highlight">
<pre class="highlight shell"><code>~/.themes/Mint-XP/cinnamon/cinnamon.css
</code></pre>

</div>



Add this CSS block (or modify if it already exists): 


<div class="highlight js-code-highlight">
<pre class="highlight css"><code>/* === Windows XP Start Button Override === */
#panelLeft .applet-box:first-child {
 background-color: transparent !important;
 background-image: url("assets/start-button.png") !important;
 background-repeat: no-repeat !important;
 background-position: 0px center !important;
 background-size: 122px 40px !important;
 min-width: 122px !important;
 width: 122px !important;
 max-width: 122px !important;
 min-height: 40px !important;
 height: 40px !important;
 max-height: 40px !important;
 padding: 0 !important;
 margin: 0 !important;
 border: none !important;
 border-image: none !important;
 box-shadow: none !important;
}

/* Hide the default icon */
#panelLeft .applet-box:first-child StIcon,
#panelLeft .applet-box:first-child .applet-icon {
 width: 0 !important;
 height: 0 !important;
 icon-size: 0 !important;
 opacity: 0 !important;
 margin: 0 !important;
 padding: 0 !important;
}

/* Hide the label text */
#panelLeft .applet-box:first-child .applet-label {
 width: 0 !important;
 height: 0 !important;
 font-size: 0 !important;
 opacity: 0 !important;
 margin: 0 !important;
 padding: 0 !important;
 color: transparent !important;
}

/* Adjust spacing for next applet */
#panelLeft .applet-box:first-child + .applet-box {
 margin-left: -2px !important;
 padding-left: 0 !important;
 border: none !important;
 border-image: none !important;
 box-shadow: none !important;
}

/* Remove borders from panel elements */
#panelLeft,
#panelLeft .applet-box,
#panelLeft .applet-box:first-child,
#panelLeft .applet-box:first-child + .applet-box,
.panel-launchers,
.panel-launcher,
.panel-launcher:first-child {
 border: none !important;
 border-image: none !important;
 box-shadow: none !important;
 outline: none !important;
}

/* Hide separators */
#panelLeft .applet-separator,
#panelLeft .applet-separator-line {
 border: none !important;
 background: none !important;
 width: 0 !important;
 margin: 0 !important;
 padding: 0 !important;
}

/* Ensure other panels aren't affected */
#panelRight .applet-box:first-child,
#panelCenter .applet-box:first-child,
#panelTop .applet-box:first-child,
#panelBottom .applet-box:first-child {
 background: none !important;
 background-image: none !important;
 width: auto !important;
 height: auto !important;
 min-width: auto !important;
 min-height: auto !important;
}
/* === End Windows XP Start Button Override === */
</code></pre>

</div>



<h3>
 
 
 3. Clear Any gsettings Overrides
</h3>

Ensure no system-wide settings are interfering: 


<div class="highlight js-code-highlight">
<pre class="highlight shell"><code>gsettings set com.linuxmint.mintmenu applet-icon ''
gsettings set org.cinnamon app-menu-icon-name ''
</code></pre>

</div>



<h3>
 
 
 4. Reload Cinnamon
</h3>

Press <code>Alt+F2</code>, type <code>r</code>, and press Enter to reload Cinnamon.

<h2>
 
 
 Key Lessons Learned
</h2>

<h3>
 
 
 1. Keep It Simple
</h3>

The simplest solution is often the best. A relative file path beat complex base64 encoding.

<h3>
 
 
 2. Avoid System-Wide Settings
</h3>

Using <code>gsettings</code> for theme customization can have unintended consequences. CSS-only solutions are safer and more maintainable.

<h3>
 
 
 3. The !important Flag Matters
</h3>

Cinnamon's CSS has high specificity. Using <code>!important</code> on every property ensures your styles take precedence.

<h3>
 
 
 4. Relative Paths Work
</h3>

CSS file paths are relative to the CSS file location:

<ul>
<li>CSS at: <code>~/.themes/Mint-XP/cinnamon/cinnamon.css</code>
</li>
<li>Image at: <code>~/.themes/Mint-XP/cinnamon/assets/start-button.png</code>
</li>
<li>Reference as: <code>url("assets/start-button.png")</code>
</li>
</ul>

<h3>
 
 
 5. Test Incrementally
</h3>

When I tested with a solid color background (<code>background-color: red !important;</code>), I confirmed the CSS selector was working. This proved the issue was with the image reference, not the targeting.

<h2>
 
 
 Troubleshooting
</h2>

If your button doesn't appear:

<ol>
<li>
Verify the image exists:
</li>
</ol>

<div class="highlight js-code-highlight">
<pre class="highlight shell"><code> ls -lh ~/.themes/YOUR-THEME/cinnamon/assets/start-button.png
</code></pre>

</div>



<ol>
<li>
Check file permissions:
</li>
</ol>

<div class="highlight js-code-highlight">
<pre class="highlight shell"><code> chmod 644 ~/.themes/YOUR-THEME/cinnamon/assets/start-button.png
</code></pre>

</div>



<ol>
<li>
Test with a solid color first:
Replace the background-image line temporarily with:
</li>
</ol>

<div class="highlight js-code-highlight">
<pre class="highlight css"><code> background-color: red !important;
</code></pre>

</div>



If you see a red box, your selector works and the issue is the image path.

<ol>
<li>
Check for typos in the path:

<ul>
<li>File name is case-sensitive</li>
<li>Path is relative to the CSS file</li>
<li>No leading slash for relative paths</li>
</ul>
</li>
<li>Clear gsettings: 
</li>
</ol>

<div class="highlight js-code-highlight">
<pre class="highlight shell"><code> gsettings reset com.linuxmint.mintmenu applet-icon
 gsettings reset org.cinnamon app-menu-icon-name
</code></pre>

</div>



<h2>
 
 
 Why This Matters
</h2>

Custom desktop theming is about more than aesthetics - it's about making your workspace yours. But when solutions are fragile or create side effects, they undermine the experience. This CSS-only approach is:

<ul>
<li>
Clean: No system-wide settings pollution</li>
<li>
Maintainable: Easy to modify or disable</li>
<li>
Portable: Works across Cinnamon updates</li>
<li>
Predictable: No side effects in other UI elements</li>
</ul>

<h2>
 
 
 Conclusion
</h2>

After 5 days of trying complex solutions, the answer was simple: use a relative file path in CSS. Sometimes the best solution is the one that doesn't try to be clever.

If you're customizing Cinnamon themes, remember: CSS file paths, not gsettings, and definitely not base64.




System Info:

<ul>
<li>Linux Mint (Cinnamon Desktop Environment)</li>
<li>Theme: Mint-XP (custom modification)</li>
<li>Image: 122×40 PNG, ~12KB</li>
</ul>

Files Modified:

<ul>
<li>
<code>~/.themes/Mint-XP/cinnamon/cinnamon.css</code> (CSS styling)</li>
<li>
<code>~/.themes/Mint-XP/cinnamon/assets/start-button.png</code> (image asset)</li>
</ul>

Feel free to adapt this solution for your own custom panel buttons!

تقدم هذه المقالة دليلًا خطوة بخطوة حول كيفية تخصيص صورة زر البدء في لينكس مينت، مما يعزز تجربة المستخدم ويخصص بيئة سطح المكتب. تعتبر التخصيصات مهمة لأنها تتيح للمستخدمين التعبير عن فرديتهم وتحسين قابلية الاستخدام، مما يجعل تجربتهم الحاسوبية أكثر متعة وتناسبًا مع تفضيلاتهم.

Este artículo ofrece una guía paso a paso sobre cómo personalizar la imagen del botón de inicio en Linux Mint, mejorando la experiencia del usuario y personalizando el entorno de escritorio. La personalización es importante ya que permite a los usuarios expresar su individualidad y mejorar la usabilidad, haciendo que su experiencia informática sea más agradable y adaptada a sus preferencias.

Cet article propose un guide étape par étape sur la façon de personnaliser l'image du bouton de démarrage dans Linux Mint, améliorant ainsi l'expérience utilisateur et personnalisant l'environnement de bureau. La personnalisation est importante car elle permet aux utilisateurs d'exprimer leur individualité et d'améliorer l'utilisabilité, rendant leur expérience informatique plus agréable et adaptée à leurs préférences.

This article provides a step-by-step guide on how to customize the start button image in Linux Mint, enhancing the user experience and personalizing the desktop environment. Customization is important as it allows users to express their individuality and improve usability, making their computing experience more enjoyable and tailored to their preferences.

How-to use a Custom Start Button Image in Linux Mint

<A HREF="https://www.theinformation.com/articles/openais-data-center-partner-nears-120-million-stock-sale-employees"><IMG VSPACE="4" HSPACE="4" BORDER="0" ALIGN="RIGHT" SRC="http://www.techmeme.com/251105/i44.jpg"></A>
<A HREF="http://www.techmeme.com/251105/p44#a251105p44" TITLE="Techmeme permalink"><IMG WIDTH=11 HEIGHT=12 SRC="http://www.techmeme.com/img/pml.png" STYLE="border:none;padding:0;margin:0;"></A> <A HREF="https://www.theinformation.com/">The Information</A>: 
<A HREF="https://www.theinformation.com/articles/openais-data-center-partner-nears-120-million-stock-sale-employees">Sources: data center startup Crusoe is arranging an employee share sale involving ~$120M worth of shares that values it at ~$13B, up from $10B+ just weeks ago</A>&nbsp; &mdash;&nbsp; Crusoe, a startup developing a large data center in Texas for Oracle and OpenAI, is arranging an employee share sale that values &hellip;

تعتبر شركة كروسو، وهي شركة ناشئة في مجال مراكز البيانات، محط الأنظار مع خططها لبيع أسهم للموظفين بقيمة تقارب 120 مليون دولار. لا يعزز هذا التحرك فقط تقييم الشركة إلى حوالي 13 مليار دولار، بعد أن كان أكثر من 10 مليارات دولار قبل أسابيع قليلة، بل يعكس أيضًا ثقة متزايدة في قطاع التكنولوجيا. تشير مثل هذه المناورات المالية الكبيرة إلى إيمان قوي بإمكانات كروسو المستقبلية وقد تلهم شركات ناشئة أخرى للنظر في استراتيجيات مماثلة للنمو.

Crusoe, una startup de centros de datos, está en el centro de atención con planes para una venta de acciones a empleados valorada en aproximadamente 120 millones de dólares. Este movimiento no solo eleva la valoración de la empresa a alrededor de 13 mil millones de dólares, desde más de 10 mil millones hace solo unas semanas, sino que también refleja una creciente confianza en el sector tecnológico. Tales maniobras financieras significativas indican una fuerte creencia en el potencial futuro de Crusoe y podrían inspirar a otras startups a considerar estrategias similares para su crecimiento.

Crusoe, une startup de centres de données, fait parler d'elle avec des projets de vente d'actions aux employés d'une valeur d'environ 120 millions de dollars. Ce mouvement non seulement augmente la valorisation de l'entreprise à environ 13 milliards de dollars, contre plus de 10 milliards de dollars il y a quelques semaines, mais reflète également une confiance croissante dans le secteur technologique. De telles manœuvres financières significatives indiquent une forte croyance dans le potentiel futur de Crusoe et pourraient inspirer d'autres startups à envisager des stratégies similaires pour croître.

Crusoe, a data center startup, is making headlines with plans for an employee share sale valued at approximately $120 million. This move not only boosts the company's valuation to around $13 billion, up from over $10 billion just weeks ago, but also reflects growing confidence in the tech sector. Such significant financial maneuvers indicate a strong belief in Crusoe's future potential and could inspire other startups to consider similar strategies for growth.

Sources: data center startup Crusoe is arranging an employee share sale involving ~$120M worth of shares that values it at ~$13B, up from $10B+ just weeks ago (The Information)

تلقى تطبيق شازام على نظام iOS تحديثًا مثيرًا بتصميم أنيق من زجاج سائل، مما يعزز واجهة المستخدم والتجربة العامة. لا يجعل هذا التحديث التطبيق جذابًا بصريًا فحسب، بل يحسن أيضًا من وظيفته، مما يسهل على المستخدمين التعرف على الأغاني واكتشاف موسيقى جديدة. تعتبر مثل هذه التحديثات ضرورية للحفاظ على التطبيقات ذات صلة وجذابة في سوق تنافسي.

Shazam para iOS ha recibido una emocionante actualización con un elegante diseño de Liquid Glass, mejorando su interfaz de usuario y la experiencia general. Esta renovación no solo hace que la aplicación sea visualmente atractiva, sino que también mejora su funcionalidad, facilitando a los usuarios la identificación de canciones y el descubrimiento de nueva música. Estas actualizaciones son cruciales para mantener las aplicaciones relevantes y atractivas en un mercado competitivo.

Shazam pour iOS a reçu une mise à jour passionnante avec un design élégant en Liquid Glass, améliorant son interface utilisateur et son expérience globale. Ce relooking rend non seulement l'application visuellement attrayante, mais améliore également sa fonctionnalité, facilitant ainsi l'identification des chansons et la découverte de nouvelles musiques. De telles mises à jour sont essentielles pour maintenir les applications pertinentes et engageantes sur un marché concurrentiel.

Shazam for iOS has received an exciting update with a sleek Liquid Glass design, enhancing its user interface and overall experience. This makeover not only makes the app visually appealing but also improves functionality, making it easier for users to identify songs and discover new music. Such updates are crucial in keeping apps relevant and engaging in a competitive market.

Shazam for iOS just got a Liquid Glass makeover

<a href="https://www.techspot.com/news/110143-us-vibe-coding-startups-soar-value-but-their.html" target="_blank"><img src="https://www.techspot.com/images2/news/ts3_thumbs/2025/11/2025-11-05-ts3_thumbs-bbe.jpg" width="800" height="560" style="padding: 15px 0" title="US &quot;vibe coding&quot; startups soar in value, but their AI may be powered by Chinese models" /></a> Vibe coding is becoming one of the most popular use cases for generative services and AI models, driving up the valuations of several US companies. However, some of these models originate from China, which has raised concerns among some observers. <a href="https://www.techspot.com/news/110143-us-vibe-coding-startups-soar-value-but-their.html">Read Entire Article</a>

يكتسب البرمجة على أساس الإحساس شعبية كأحد التطبيقات للخدمات التوليدية ونماذج الذكاء الاصطناعي، مما يؤدي إلى ارتفاع قيم الشركات الناشئة في هذا المجال. تسلط هذه الظاهرة الضوء على الاهتمام المتزايد بأساليب البرمجة المبتكرة التي تستفيد من تكنولوجيا الذكاء الاصطناعي، على الرغم من أنها تثير تساؤلات حول الاعتماد على النماذج الصينية لهذه التطورات. مع تطور السوق، سيكون من الضروري فهم تداعيات هذا الاعتماد للمستثمرين والمطورين على حد سواء.

El vibe coding está ganando popularidad como una aplicación de servicios generativos y modelos de IA, lo que está impulsando un aumento en las valoraciones de las startups en este ámbito. Esta tendencia destaca el creciente interés en métodos de codificación innovadores que aprovechan la tecnología de IA, aunque plantea preguntas sobre la dependencia de modelos chinos para estos avances. A medida que el mercado evoluciona, comprender las implicaciones de esta dependencia será crucial para inversores y desarrolladores.

Le vibe coding gagne en popularité en tant qu'application des services génératifs et des modèles d'IA, entraînant une augmentation des valorisations des startups dans ce domaine. Cette tendance souligne l'intérêt croissant pour des méthodes de codage innovantes qui exploitent la technologie de l'IA, bien qu'elle soulève des questions sur la dépendance aux modèles chinois pour ces avancées. À mesure que le marché évolue, comprendre les implications de cette dépendance sera crucial pour les investisseurs et les développeurs.

Vibe coding is gaining traction as a popular application of generative AI, leading to a surge in the valuations of startups in this space. This trend highlights the growing interest in innovative coding methods that leverage AI technology, although it raises questions about the reliance on Chinese models for these advancements. As the market evolves, understanding the implications of this reliance will be crucial for investors and developers alike.

US "vibe coding" startups soar in value, but their AI may be powered by Chinese models

arXiv:2511.01929v1 Announce Type: new 
Abstract: Human trajectory data is crucial in urban planning, traffic engineering, and public health. However, directly using real-world trajectory data often faces challenges such as privacy concerns, data acquisition costs, and data quality. A practical solution to these challenges is trajectory generation, a method developed to simulate human mobility behaviors. Existing trajectory generation methods mainly focus on capturing individual movement patterns but often overlook the influence of population distribution on trajectory generation. In reality, dynamic population distribution reflects changes in population density across different regions, significantly impacting individual mobility behavior. Thus, we propose a novel trajectory generation framework based on a diffusion model, which integrates the dynamic population distribution constraints to guide high-fidelity generation outcomes. Specifically, we construct a spatial graph to enhance the spatial correlation of trajectories. Then, we design a dynamic population distribution aware denoising network to capture the spatiotemporal dependencies of human mobility behavior as well as the impact of population distribution in the denoising process. Extensive experiments show that the trajectories generated by our model can resemble real-world trajectories in terms of some critical statistical metrics, outperforming state-of-the-art algorithms by over 54%.

تسلط دراسة جديدة الضوء على أهمية بيانات مسار الإنسان في التخطيط الحضري والصحة العامة. تتناول التحديات مثل الخصوصية وجودة البيانات من خلال تقديم نموذج انتشار لتوليد سلوكيات تنقل الإنسان المحاكية، مما يوفر حلاً عمليًا لتحسين استخدام البيانات.

Un nuevo estudio destaca la importancia de los datos de trayectoria humana en la planificación urbana y la salud pública. Aborda desafíos como la privacidad y la calidad de los datos al introducir un modelo de difusión para generar comportamientos de movilidad humana simulados, ofreciendo una solución práctica para una mejor utilización de los datos.

Une nouvelle étude souligne l'importance des données de trajectoire humaine dans l'urbanisme et la santé publique. Elle aborde des défis tels que la confidentialité et la qualité des données en introduisant un modèle de diffusion pour générer des comportements de mobilité humaine simulés, offrant une solution pratique pour une meilleure utilisation des données.

A new study highlights the importance of human trajectory data in urban planning and public health. It addresses challenges like privacy and data quality by introducing a diffusion model for generating simulated human mobility behaviors, offering a practical solution for better data utilization.

Dynamic Population Distribution Aware Human Trajectory Generation with Diffusion Model

arXiv:2510.22736v2 Announce Type: replace 
Abstract: Cross-view localization and synthesis are two fundamental tasks in cross-view visual understanding, which deals with cross-view datasets: overhead (satellite or aerial) and ground-level imagery. These tasks have gained increasing attention due to their broad applications in autonomous navigation, urban planning, and augmented reality. Cross-view localization aims to estimate the geographic position of ground-level images based on information provided by overhead imagery while cross-view synthesis seeks to generate ground-level images based on information from the overhead imagery. Both tasks remain challenging due to significant differences in viewing perspective, resolution, and occlusion, which are widely embedded in cross-view datasets. Recent years have witnessed rapid progress driven by the availability of large-scale datasets and novel approaches. Typically, cross-view localization is formulated as an image retrieval problem where ground-level features are matched with tiled overhead images feature, extracted by convolutional neural networks (CNNs) or vision transformers (ViTs) for cross-view feature embedding. Cross-view synthesis, on the other hand, seeks to generate ground-level views based on information from overhead imagery, generally using generative adversarial networks (GANs) or diffusion models. This paper presents a comprehensive survey of advances in cross-view localization and synthesis, reviewing widely used datasets, highlighting key challenges, and providing an organized overview of state-of-the-art techniques. Furthermore, it discusses current limitations, offers comparative analyses, and outlines promising directions for future research. We also include the project page via https://github.com/GDAOSU/Awesome-Cross-View-Methods.

تسلط التطورات الأخيرة في تحديد المواقع المتقاطعة والتوليف الضوء على أهميتها في مجالات مثل الملاحة الذاتية والتخطيط الحضري. من خلال تقدير المواقع الجغرافية بفعالية من الصور على مستوى الأرض، تمهد هذه التقنيات الطريق لتطبيقات محسّنة في الواقع المعزز. لا يُظهر هذا التقدم فقط إمكانيات مجموعات البيانات المتقاطعة، بل يفتح أيضًا فرصًا جديدة للابتكار في الفهم البصري.

Los recientes avances en localización y síntesis de vistas cruzadas destacan su importancia en campos como la navegación autónoma y la planificación urbana. Al estimar de manera efectiva las posiciones geográficas a partir de imágenes a nivel del suelo, estas técnicas están allanando el camino para aplicaciones mejoradas en realidad aumentada. Este progreso no solo muestra el potencial de los conjuntos de datos de vistas cruzadas, sino que también abre nuevas oportunidades para la innovación en la comprensión visual.

Les récentes avancées en matière de localisation et de synthèse croisées soulignent leur importance dans divers domaines tels que la navigation autonome et l'urbanisme. En estimant efficacement les positions géographiques à partir d'images au niveau du sol, ces techniques ouvrent la voie à des applications améliorées en réalité augmentée. Ce progrès met en lumière le potentiel des ensembles de données croisées et ouvre de nouvelles opportunités d'innovation dans la compréhension visuelle.

The recent advancements in cross-view localization and synthesis highlight their significance in various fields like autonomous navigation and urban planning. By effectively estimating geographic positions from ground-level images, these techniques are paving the way for enhanced applications in augmented reality. This progress not only showcases the potential of cross-view datasets but also opens up new opportunities for innovation in visual understanding.

Cross-view Localization and Synthesis -- Datasets, Challenges and Opportunities

arXiv:2510.27224v1 Announce Type: new 
Abstract: Accurate building instance segmentation and height classification are critical for urban planning, 3D city modeling, and infrastructure monitoring. This paper presents a detailed analysis of YOLOv11, the recent advancement in the YOLO series of deep learning models, focusing on its application to joint building extraction and discrete height classification from satellite imagery. YOLOv11 builds on the strengths of earlier YOLO models by introducing a more efficient architecture that better combines features at different scales, improves object localization accuracy, and enhances performance in complex urban scenes. Using the DFC2023 Track 2 dataset -- which includes over 125,000 annotated buildings across 12 cities -- we evaluate YOLOv11's performance using metrics such as precision, recall, F1 score, and mean average precision (mAP). Our findings demonstrate that YOLOv11 achieves strong instance segmentation performance with 60.4\% mAP@50 and 38.3\% mAP@50--95 while maintaining robust classification accuracy across five predefined height tiers. The model excels in handling occlusions, complex building shapes, and class imbalance, particularly for rare high-rise structures. Comparative analysis confirms that YOLOv11 outperforms earlier multitask frameworks in both detection accuracy and inference speed, making it well-suited for real-time, large-scale urban mapping. This research highlights YOLOv11's potential to advance semantic urban reconstruction through streamlined categorical height modeling, offering actionable insights for future developments in remote sensing and geospatial intelligence.

تسلط ورقة جديدة الضوء على نموذج YOLOv11 المبتكر، الذي يعزز من تقسيم المباني وتصنيف الارتفاعات باستخدام صور الأقمار الصناعية. هذه التطورات مهمة للتخطيط الحضري ومراقبة البنية التحتية، حيث تسمح بجمع بيانات أكثر دقة وكفاءة. من خلال تحسين كيفية استخراج وتصنيف معلومات المباني، يمكن أن يحول YOLOv11 عمليات نمذجة المدن والتخطيط، مما يجعلها أكثر فعالية واستجابة للاحتياجات الحضرية.

Un nuevo artículo destaca el innovador modelo YOLOv11, que mejora la segmentación de instancias de edificios y la clasificación de alturas utilizando imágenes de satélite. Este avance es significativo para la planificación urbana y el monitoreo de infraestructuras, ya que permite una recolección de datos más precisa y eficiente. Al mejorar la forma en que extraemos y clasificamos la información sobre edificios, YOLOv11 podría transformar los procesos de modelado y planificación de ciudades, haciéndolos más efectivos y receptivos a las necesidades urbanas.

Un nouvel article met en avant le modèle innovant YOLOv11, qui améliore la segmentation des instances de bâtiments et la classification de la hauteur à l'aide d'images satellites. Cette avancée est significative pour l'urbanisme et le suivi des infrastructures, car elle permet une collecte de données plus précise et efficace. En améliorant la manière dont nous extrayons et classifions les informations sur les bâtiments, YOLOv11 pourrait transformer les processus de modélisation et de planification urbaine, les rendant plus efficaces et réactifs aux besoins urbains.

A new paper highlights the innovative YOLOv11 model, which enhances building instance segmentation and height classification using satellite imagery. This advancement is significant for urban planning and infrastructure monitoring, as it allows for more accurate and efficient data collection. By improving how we extract and classify building information, YOLOv11 could transform city modeling and planning processes, making them more effective and responsive to urban needs.

Mask-to-Height: A YOLOv11-Based Architecture for Joint Building Instance Segmentation and Height Classification from Satellite Imagery

arXiv:2511.01934v1 Announce Type: new 
Abstract: Training tool-augmented LLMs has emerged as a promising approach to enhancing language models' capabilities for complex tasks. The current supervised fine-tuning paradigm relies on constructing extensive domain-specific datasets to train models. However, this approach often struggles to generalize effectively to unfamiliar or intricate tool-use scenarios. Recently, reinforcement learning (RL) paradigm can endow LLMs with superior reasoning and generalization abilities. In this work, we address a key question: Can the pure RL be used to effectively elicit a model's intrinsic reasoning capabilities and enhance the tool-agnostic generalization? We propose a dynamic generalization-guided reward design for rule-based RL, which progressively shifts rewards from exploratory to exploitative tool-use patterns. Based on this design, we introduce the Tool-Zero series models. These models are trained to enable LLMs to autonomously utilize general tools by directly scaling up RL from Zero models (i.e., base models without post-training). Experimental results demonstrate that our models achieve over 7% performance improvement compared to both SFT and RL-with-SFT models under the same experimental settings. These gains are consistently replicated across cross-dataset and intra-dataset evaluations, validating the effectiveness and robustness of our methods.

تقدم Tool Zero نهجًا مبتكرًا لتدريب نماذج اللغة باستخدام التعلم المعزز النقي من الصفر. تهدف هذه الطريقة إلى تعزيز قدرات نماذج اللغة في المهام المعقدة، متجاوزةً قيود الضبط الخاضع للإشراف التقليدي الذي غالبًا ما يواجه صعوبات مع السيناريوهات غير المألوفة.

Tool Zero presenta un enfoque innovador para entrenar modelos de lenguaje utilizando aprendizaje por refuerzo puro desde cero. Este método busca mejorar las capacidades de los modelos de lenguaje para tareas complejas, superando las limitaciones del ajuste fino supervisado tradicional que a menudo tiene dificultades con escenarios desconocidos.

Tool Zero propose une approche innovante pour former des modèles de langage en utilisant un apprentissage par renforcement pur dès le départ. Cette méthode vise à améliorer les capacités des modèles de langage pour des tâches complexes, surmontant les limites de l'affinage supervisé traditionnel qui peine souvent avec des scénarios inconnus.

Tool Zero introduces an innovative approach to training language models using pure reinforcement learning from scratch. This method aims to enhance the capabilities of language models for complex tasks, overcoming the limitations of traditional supervised fine-tuning that often struggles with unfamiliar scenarios.

Tool Zero: Training Tool-Augmented LLMs via Pure RL from Scratch

arXiv:2505.15064v3 Announce Type: replace-cross 
Abstract: Why and when is deep better than shallow? We answer this question in a framework that is agnostic to network implementation. We formulate a deep model as an abstract state-transition semigroup acting on a general metric space, and separate the implementation (e.g., ReLU nets, transformers, and chain-of-thought) from the abstract state transition. We prove a bias-variance decomposition in which the variance depends only on the abstract depth-$k$ network and not on the implementation (Theorem 1). We further split the bounds into output and hidden parts to tie the depth dependence of the variance to the metric entropy of the state-transition semigroup (Theorem 2). We then investigate implementation-free conditions under which the variance grow polynomially or logarithmically with depth (Section 4). Combining these with exponential or polynomial bias decay identifies four canonical bias-variance trade-off regimes (EL/EP/PL/PP) and produces explicit optimal depths $k^\ast$. Across regimes, $k^\ast>1$ typically holds, giving a rigorous form of depth supremacy. The lowest generalization error bound is achieved under the EL regime (exp-decay bias + log-growth variance), explaining why and when deep is better, especially for iterative or hierarchical concept classes such as neural ODEs, diffusion/score-matching models, and chain-of-thought reasoning.

تستكشف هذه المقالة مزايا النماذج العميقة مقارنة بالنماذج الضحلة في إطار لا يعتمد على تنفيذات الشبكات المحددة. تناقش كيف يمكن فهم النماذج العميقة على أنها شبه مجموعات انتقالية للحالة المجردة وتقدم تحليلًا لتفكيك التحيز والتباين الذي يبرز دور العمق في تحديد التباين.

Este artículo explora las ventajas de los modelos profundos sobre los superficiales en un marco que no depende de implementaciones específicas de redes. Se discute cómo los modelos profundos pueden entenderse como semigrupos de transición de estado abstractos y se presenta una descomposición de sesgo-varianza que destaca el papel de la profundidad en la determinación de la varianza.

Cet article explore les avantages des modèles profonds par rapport aux modèles peu profonds dans un cadre qui ne dépend pas d'implémentations spécifiques de réseaux. Il discute de la manière dont les modèles profonds peuvent être compris comme des semi-groupes de transition d'état abstraits et présente une décomposition biais-variance qui met en évidence le rôle de la profondeur dans la détermination de la variance.

This article explores the advantages of deep models over shallow ones in a framework that doesn't depend on specific network implementations. It discusses how deep models can be understood as abstract state-transition semigroups and presents a bias-variance decomposition that highlights the role of depth in determining variance.

Why and When Deep is Better than Shallow: An Implementation-Agnostic State-Transition View of Depth Supremacy

arXiv:2511.02241v1 Announce Type: cross 
Abstract: Traditional neural networks, while powerful, rely on biologically implausible learning mechanisms such as global backpropagation. This paper introduces the Structurally Adaptive Predictive Inference Network (SAPIN), a novel computational model inspired by the principles of active inference and the morphological plasticity observed in biological neural cultures. SAPIN operates on a 2D grid where processing units, or cells, learn by minimizing local prediction errors. The model features two primary, concurrent learning mechanisms: a local, Hebbian-like synaptic plasticity rule based on the temporal difference between a cell's actual activation and its learned expectation, and a structural plasticity mechanism where cells physically migrate across the grid to optimize their information-receptive fields. This dual approach allows the network to learn both how to process information (synaptic weights) and also where to position its computational resources (network topology). We validated the SAPIN model on the classic Cart Pole reinforcement learning benchmark. Our results demonstrate that the architecture can successfully solve the CartPole task, achieving robust performance. The network's intrinsic drive to minimize prediction error and maintain homeostasis was sufficient to discover a stable balancing policy. We also found that while continual learning led to instability, locking the network's parameters after achieving success resulted in a stable policy. When evaluated for 100 episodes post-locking (repeated over 100 successful agents), the locked networks maintained an average 82% success rate.

تقدم هذه المقالة نموذجًا مبتكرًا يسمى شبكة الاستدلال التنبؤية التكيفية الهيكلية (SAPIN)، والتي تستلهم من الثقافات العصبية البيولوجية. على عكس الشبكات العصبية التقليدية التي تستخدم الانتشار العكسي العالمي، يستخدم SAPIN مبادئ الاستدلال النشط لتعزيز التعلم والقدرة على التكيف، مما يظهر اتجاهًا واعدًا للنماذج الحاسوبية المستقبلية.

Este artículo presenta un modelo innovador llamado la Red de Inferencia Predictiva Adaptativa Estructural (SAPIN), que se inspira en las culturas neuronales biológicas. A diferencia de las redes neuronales tradicionales que utilizan retropropagación global, SAPIN emplea principios de inferencia activa para mejorar el aprendizaje y la adaptabilidad, mostrando una dirección prometedora para futuros modelos computacionales.

Cet article présente un modèle révolutionnaire appelé le Réseau d'Inference Prédictive Adaptatif Structurel (SAPIN), qui s'inspire des cultures neuronales biologiques. Contrairement aux réseaux neuronaux traditionnels qui utilisent la rétropropagation globale, le SAPIN utilise les principes de l'inférence active pour améliorer l'apprentissage et l'adaptabilité, montrant une direction prometteuse pour les futurs modèles computationnels.

This article presents a groundbreaking model called the Structurally Adaptive Predictive Inference Network (SAPIN), which draws inspiration from biological neural cultures. Unlike traditional neural networks that use global backpropagation, SAPIN employs active inference principles to enhance learning and adaptability, showcasing a promising direction for future computational models.

OSMGen: Highly Controllable Satellite Image Synthesis using OpenStreetMap Data

OSMGen: Highly Controllable Satellite Image Synthesis using OpenStreetMap Data

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