STPath هو نموذج أساسي توليدي مصمم لدمج النسخ الجزيئي المكاني مع صور الشرائح الكاملة، كما ورد في مجلة Nature — Machine Learning. يهدف هذا النموذج إلى تحسين تحليل البيانات البيولوجية المعقدة من خلال دمج التصوير عالي الدقة مع بيانات التعبير الجيني المكاني. من المتوقع أن تسهل هذه التقنية المدمجة تفسيرات أكثر دقة لعينات الأنسجة وتحسن فهم العمليات البيولوجية المختلفة.

STPath es un modelo generativo de base diseñado para integrar la transcriptómica espacial con imágenes de diapositivas completas, según se informa en Nature — Machine Learning. Este modelo tiene como objetivo mejorar el análisis de datos biológicos complejos al combinar imágenes de alta resolución con datos de expresión génica espacial. La integración de estas tecnologías se espera que facilite interpretaciones más precisas de muestras de tejido y mejore la comprensión de diversos procesos biológicos.

STPath est un modèle de fondation génératif conçu pour intégrer la transcriptomique spatiale avec les images de diapositives entières, comme rapporté dans Nature — Machine Learning. Ce modèle vise à améliorer l'analyse des données biologiques complexes en combinant l'imagerie haute résolution avec des données d'expression génique spatiale. L'intégration de ces technologies devrait faciliter des interprétations plus précises des échantillons de tissus et améliorer la compréhension de divers processus biologiques.

STPath is a generative foundation model designed to integrate spatial transcriptomics with whole-slide images, as reported in Nature — Machine Learning. This model aims to enhance the analysis of complex biological data by combining high-resolution imaging with spatial gene expression data. The integration of these technologies is expected to facilitate more accurate interpretations of tissue samples and improve the understanding of various biological processes.

STPath: a generative foundation model for integrating spatial transcriptomics and whole-slide images

The story of the Ghost in the Shell’s main villain the Puppet Master hinted at a future where governments use hackers for espionage, at a time when most of the world had never connected to the internet.

الأنمي الكلاسيكي 'Ghost in the Shell' يقدم شخصية Puppet Master، التي تتنبأ بمستقبل تستخدم فيه الحكومات القراصنة للتجسس. ظهرت هذه التنبؤات في وقت كانت فيه غالبية سكان العالم لم تتصل بعد بالإنترنت، مما يبرز رؤية العرض لمشكلات الأمن السيبراني.

El clásico anime 'Ghost in the Shell' presenta al Puppet Master, un personaje que anticipa un futuro en el que los gobiernos utilizan hackers para el espionaje. Esta predicción surgió en un momento en que la mayoría de la población mundial aún no estaba conectada a Internet, destacando la previsión del programa sobre los problemas de ciberseguridad.

L'anime classique 'Ghost in the Shell' présente le Puppet Master, un personnage qui préfigure un avenir où les gouvernements utilisent des hackers pour l'espionnage. Cette prédiction est survenue à une époque où la majorité de la population mondiale n'était pas encore connectée à Internet, soulignant la prévoyance de l'émission concernant les problèmes de cybersécurité.

The classic anime 'Ghost in the Shell' features the Puppet Master, a character that foreshadows a future where governments utilize hackers for espionage. This prediction emerged at a time when the majority of the global population had yet to connect to the internet, highlighting the show's foresight regarding cybersecurity issues.

How the classic anime ‘Ghost in the Shell’ predicted the future of cybersecurity 30 years ago

<p>Text-to-image diffusion models have become the workhorses of generative imaging. They can paint photorealistic scenes, mimic art styles, and blend concepts in ways that were science fiction a few years ago. Yet they stumble embarrassingly on a skill that even small children master: basic spatial reasoning.</p>

<p>Ask a state-of-the-art model for “a dog to the right of a teddy bear” and you often get:</p>

<ul>
<li>The dog on the left</li>
<li>One of the objects missing</li>
<li>Or a bizarre hybrid where dog and teddy are fused into a single creature</li>
</ul>

<p><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%2F49rtb08366xdl284o4z0.jpg" 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%2F49rtb08366xdl284o4z0.jpg" alt=" " width="800" height="532"></a></p>

<p>These failures become more severe for unusual compositions like “a giraffe above an airplane”. Traditional fixes range from expensive fine-tuning to brittle, hand-written loss functions at inference time—but both options come with significant downsides.</p>

<p>NVIDIA’s Learn-to-Steer framework (accepted to WACV 2026) proposes a different path: instead of hard-coding spatial rules or retraining the entire model, it learns a data-driven objective that can “steer” diffusion at inference time. The method reads the model’s own cross-attention maps, trains a lightweight classifier to detect spatial relations, and then uses that classifier’s gradient as a learned loss to nudge the generation towards layouts that match the prompt.</p>

<p>In this blog, we’ll unpack:</p>

<ul>
<li>What makes spatial reasoning so fragile in current diffusion models</li>
<li>How Learn-to-Steer learns spatial constraints from the model itself</li>
<li>How it steers images during generation without changing model weights</li>
<li>The top gains on spatial benchmarks like GenEval and T2I-CompBench</li>
<li>The trade-offs in compute cost and generality, and what this implies for future generative systems</li>
</ul>

<h1>
  
  
  Why Spatial Reasoning Fails in Text-to-Image Diffusion
</h1>

<h2>
  
  
  What Makes Spatial Relations So Difficult for Diffusion Models?
</h2>

<p>Modern diffusion models (e.g., Stable Diffusion, Flux) are excellent at what should appear in an image—objects, styles, textures—but much less reliable at where those objects should be.</p>

<p>Several factors contribute:</p>

<h3>
  
  
  Weak supervision of spatial language
</h3>

<ul>
<li>Training data rarely comes with precise annotations like “object A is left of object B”.
</li>
<li>Captions often describe content loosely, so phrases like “on top of” or “to the right of” are under-specified.</li>
</ul>

<h3>
  
  
  Entangled visual concepts
</h3>

<ul>
<li>When two objects frequently co-occur, models may treat them as a single visual blob.</li>
<li>This leads to object fusion, where a “cat on a bookshelf” becomes a cat-bookshelf chimera.</li>
</ul>

<h3>
  
  
  Benchmark saturation without spatial coverage
</h3>

<ul>
<li>Many standard text-to-image benchmarks emphasize realism and style, not relational accuracy.</li>
<li>Models can score highly while still being spatially confused.</li>
</ul>

<p>Empirical studies confirm three recurring failure modes on spatial benchmarks:</p>

<ul>
<li>Incorrect placement: Objects appear in the wrong relative position.</li>
<li>Missing entities: One or more requested objects never appear.</li>
<li>Merged entities: Two objects get mashed into a single, incoherent form.</li>
</ul>

<p>The model “knows” the objects you asked for, but it doesn’t reliably understand where to place them.</p>

<h1>
  
  
  Why Fine-Tuning and Handcrafted Losses Are Not Enough
</h1>

<p>Two broad strategies have tried to patch this gap:</p>

<h2>
  
  
  Fine-tuning for spatial awareness
</h2>

<ul>
<li>Retrain the diffusion model on datasets with explicit layouts or spatial annotations.</li>
<li>Methods like COMPASS show that this can significantly improve spatial accuracy.</li>
<li>But this comes at a cost: expensive retraining, sensitivity to dataset bias, and often regressions in other capabilities such as color fidelity or counting.</li>
</ul>

<h2>
  
  
  Handcrafted test-time losses
</h2>

<ul>
<li>At inference, inject extra loss terms that penalize spatial errors (e.g., overlapping activation maps, incorrect ordering).</li>
<li>These losses must be manually designed to approximate relations like “left of” or “above”.</li>
<li>In practice, these heuristics are fragile, often over-fitting simple cases and failing on more complex layouts.</li>
</ul>

<p>In short, we’ve lacked a solution that is:</p>

<ul>
<li>Data-driven rather than rule-based</li>
<li>Plug-and-play at inference time (no full retraining)</li>
<li>Targeted enough to improve spatial reasoning without damaging other strengths</li>
</ul>

<p>This is where Learn-to-Steer enters.</p>

<h1>
  
  
  How Learn-to-Steer Works: Data-Driven Steering at Inference
</h1>

<h2>
  
  
  How Cross-Attention Maps Provide a Spatial Signal
</h2>

<p>During diffusion, at each denoising step, the model computes cross-attention maps that connect text tokens to image regions. For a prompt like “a dog to the right of a teddy bear”, you can think of:</p>

<ul>
<li>One set of attention maps for “dog”</li>
<li>Another set for “teddy bear”</li>
<li>Additional context around words like “right” or “of”</li>
</ul>

<p>These maps form a rich, high-dimensional signal describing where in the image the model currently believes each word should manifest. Prior work has used cross-attention to locate objects or edit images; Learn-to-Steer goes further by treating them as a feature space in which spatial relations can be learned.</p>

<h2>
  
  
  How a Relation Classifier Becomes a Learned Loss
</h2>

<p>The core idea of Learn-to-Steer is to train a small relation classifier that takes cross-attention maps for two objects and predicts the spatial relation between them (left-of, right-of, above, below, etc.).</p>

<p>The pipeline looks like this:</p>

<h3>
  
  
  Collect supervision
</h3>

<ul>
<li>Use images where the true relation between object A and object B is known (from datasets like GQA and synthetic layouts).</li>
<li>For each image, invert it through the diffusion model with a descriptive prompt to recover cross-attention maps for the relevant tokens.</li>
</ul>

<p><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%2Fi9dbjsdc4c8yjz2r88k4.jpg" 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%2Fi9dbjsdc4c8yjz2r88k4.jpg" alt=" " width="800" height="446"></a></p>

<h3>
  
  
  Train a classifier on attention patterns
</h3>

<ul>
<li>Input: attention maps for object A and object B.</li>
<li>Output: predicted relation (e.g., “A is left of B”).</li>
</ul>

<p>Naively, however, this leads to a subtle but serious issue: relation leakage.</p>

<h2>
  
  
  How Dual Inversion Solves the “Relation Leakage” Problem
</h2>

<p>If you always invert images with a correct prompt (e.g., “a dog to the left of a cat”), hints about the word “left” can leak into the attention patterns. A naïve classifier might then “cheat” by reading out linguistic artefacts instead of learning genuine visual geometry.</p>

<p>To prevent this, Learn-to-Steer uses a dual inversion strategy:</p>

<ul>
<li>For each image with a true relation (say, dog left of cat), create two prompts:

<ul>
<li>A positive prompt with the correct relation (“dog to the left of a cat”).</li>
<li>A negative prompt with an incorrect relation (“dog above a cat”).</li>
</ul>


</li>

<li>Run inversion with both prompts, obtaining two sets of attention maps.</li>

<li>Label both sets with the true relation (left-of), because that is what the image actually depicts.</li>

</ul>

<p>The classifier sees pairs of attention maps that share the same underlying geometry but differ in the relation words used in the prompt. To succeed, it must ignore the unreliable linguistic cue and zero in on the geometric evidence in the attention patterns. This breaks the leakage shortcut and yields a classifier that actually understands “left-of” in terms of where things appear in the model’s internal vision.</p>

<p>To improve robustness, NVIDIA combines:</p>

<ul>
<li>Real images (complex, natural scenes)</li>
<li>Synthetic images (simpler, cleaner attention patterns akin to generation scenarios)</li>
</ul>

<h1>
  
  
  How Learn-to-Steer Guides Images During Generation
</h1>

<h2>
  
  
  Step-by-Step: From Prompt to Steered Latent
</h2>

<p>Once the relation classifier is trained, Learn-to-Steer uses it at inference time as a learned objective:</p>

<h3>
  
  
  Parse the spatial prompt
</h3>

<ul>
<li>Extract subject, relation, and object from the text (e.g., subject = dog, relation = right-of, object = teddy bear).</li>
</ul>

<h3>
  
  
  Run diffusion as usual—but with checkpoints
</h3>

<ul>
<li>As the model denoises latent noise into an image, periodically extract cross-attention maps for the subject and object tokens.</li>
</ul>

<h3>
  
  
  Evaluate spatial correctness
</h3>

<ul>
<li>Feed these maps into the relation classifier, which outputs a probability distribution over relations.</li>
<li>Compare this distribution to the desired relation from the prompt, and compute a loss (e.g., cross-entropy).</li>
</ul>

<h3>
  
  
  Backpropagate into the latent
</h3>

<ul>
<li>Compute the gradient of this loss with respect to the latent representation at that timestep.</li>
<li>Nudge the latent in the direction that increases the classifier’s confidence in the correct relation.</li>
</ul>

<h3>
  
  
  Continue the diffusion process
</h3>

<ul>
<li>Let the denoising proceed from the adjusted latent.</li>
<li>Repeat this steering a number of times (often during the earlier half of the diffusion steps).</li>
</ul>

<h2>
  
  
  Support for Multiple Architectures and Relations
</h2>

<p><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%2F578a9bjc7gmtemh0jbsj.jpg" 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%2F578a9bjc7gmtemh0jbsj.jpg" alt=" " width="800" height="477"></a></p>

<p>A key advantage of Learn-to-Steer is that it’s architecture-agnostic:</p>

<ul>
<li>It has been demonstrated on both UNet-based models (like Stable Diffusion 1.4/2.1) and MMDiT-style models (like Flux).</li>
<li>The only requirement is access to a text-image alignment signal (cross-attention or similar).</li>
</ul>

<p>It can also handle prompts with multiple constraints, such as:</p>

<p>“A frog above a sneaker below a teapot.”</p>

<p>Here, Learn-to-Steer alternates attention between relations:</p>

<ul>
<li>At one timestep, optimize the frog–sneaker relation.</li>
<li>At another, optimize the sneaker–teapot relation.</li>
</ul>

يهدف Learn-to-Steer من NVIDIA إلى معالجة قيد كبير في نماذج الانتشار من النص إلى الصورة، التي تعاني من ضعف في التفكير المكاني الأساسي. يمكن لهذه النماذج إنشاء صور فوتوغرافية واقعية، لكنها غالبًا ما تضع الأشياء في غير موضعها، مثل وضع كلب على اليسار بدلاً من اليمين بجانب دمية دب. تهدف هذه الخطوة إلى تحسين دقة الصور المولدة من خلال تعزيز الفهم المكاني.

El Learn-to-Steer de NVIDIA busca abordar una limitación significativa en los modelos de difusión de texto a imagen, que luchan con el razonamiento espacial básico. Estos modelos pueden crear imágenes fotorealistas, pero a menudo colocan mal los objetos en relación entre sí, como poner un perro a la izquierda de un oso de peluche en lugar de a la derecha. Este avance tiene como objetivo mejorar la precisión de las imágenes generadas al mejorar la comprensión espacial.

Le Learn-to-Steer de NVIDIA vise à résoudre une limitation importante des modèles de diffusion texte-image, qui ont du mal avec le raisonnement spatial de base. Ces modèles peuvent créer des images photoréalistes mais placent souvent mal les objets les uns par rapport aux autres, comme mettre un chien à gauche d'un ours en peluche au lieu de la droite. Cette avancée vise à améliorer l'exactitude des images générées en renforçant la compréhension spatiale.

NVIDIA's Learn-to-Steer is set to address a significant limitation in text-to-image diffusion models, which struggle with basic spatial reasoning. These models can create photorealistic images but often misplace objects in relation to one another, such as placing a dog to the left of a teddy bear instead of the right. This advancement aims to enhance the accuracy of generated images by improving spatial understanding.

What Is Learn-to-Steer? NVIDIA’s 2025 Spatial Fix for Text-to-Image Diffusion

<p>The $5tn firm handily beat expectations, but analysts are awaiting projections for future demand for firm’s AI chips</p><p>Nvidia shares are rising in after-market trading after the company posted third quarter earnings that beat Wall Street estimates.<strong> </strong>All eyes were on Nvidia, the bellwether for the AI industry and the most valuable publicly traded company in the world, as analysts and investors hoped the chipmaker’s third-quarter earnings would assuage concerns about whether the high-flying valuations of AI firms have peaked.</p><p>“Blackwell sales are off the charts, and cloud GPUs are sold out,” said Jensen Huang, founder and CEO of Nvidia in a press release. “Compute demand keeps accelerating and compounding across training and inference – each growing exponentially. We’ve entered the virtuous cycle of AI. The AI ecosystem is scaling fast – with more new foundation model makers, more AI startups, across more industries, and in more countries. AI is going everywhere, doing everything, all at once.”</p> <a href="https://www.theguardian.com/technology/2025/nov/19/nvidia-earning-report">Continue reading...</a>

تجاوزت شركة إنفيديا توقعات وول ستريت مع نتائجها للربع الثالث، مما أظهر طلبًا قويًا على شرائح الذكاء الاصطناعي الخاصة بها. ارتفعت أسهم الشركة في التداول بعد السوق، مما يعكس ثقة المستثمرين وسط مخاوف بشأن تقييم سوق الذكاء الاصطناعي. وأبرز الرئيس التنفيذي جينسن هوانغ مبيعات قياسية ونظامًا بيئيًا سريع التوسع في مجال الذكاء الاصطناعي، مما يشير إلى نظرة إيجابية لمستقبل الشركة.

Nvidia superó las expectativas de Wall Street con sus ganancias del tercer trimestre, mostrando una fuerte demanda por sus chips de IA. Las acciones de la compañía aumentaron en el comercio posterior al cierre, reflejando la confianza de los inversores en medio de preocupaciones sobre la valoración del mercado de IA. El CEO Jensen Huang destacó las ventas récord y un ecosistema de IA en rápida expansión, indicando una perspectiva positiva para el futuro de la empresa.

Nvidia a dépassé les attentes de Wall Street avec ses résultats du troisième trimestre, montrant une forte demande pour ses puces d'IA. Les actions de l'entreprise ont augmenté lors des échanges après la clôture, reflétant la confiance des investisseurs face aux préoccupations concernant la valorisation du marché de l'IA. Le PDG Jensen Huang a souligné des ventes record et un écosystème IA en pleine expansion, indiquant une perspective positive pour l'avenir de l'entreprise.

Nvidia exceeded Wall Street expectations with its third-quarter earnings, showcasing strong demand for its AI chips. The company's shares rose in after-market trading, reflecting investor confidence amid concerns about the AI market's valuation. CEO Jensen Huang highlighted record sales and a rapidly expanding AI ecosystem, indicating a positive outlook for the company's future.

‘AI is going everywhere, doing everything:’ Nvidia beats Wall Street estimates amid market selloff and AI bubble fears

The SanDisk ExtremeFit USB-C flash drive is barely three grams, but offers 1TB of external storage and impressive speeds.

I refused to believe this coin-sized gadget was a storage drive, until I tried it for myself

<p>Swift 6.3 is bringing significant enhancements to Embedded Swift, the subset of Swift designed for resource-constrained environments like microcontrollers. Here's what's new:</p>

<h2>
  
  
  Key Improvements
</h2>

<h3>
  
  
  Libraries &amp; Standard Library
</h3>

<ul>
<li>
<strong>Floating-point printing</strong>: The <code>description</code> and <code>debugDescription</code> properties now work for Float, Double, and other floating-point types with a new all-Swift implementation</li>
<li>
<strong>Better diagnostics</strong>: New <code>EmbeddedRestrictions</code> diagnostic group warns about unsupported language constructs</li>
<li>
<strong>Swift MMIO 0.1.x</strong>: Includes code generation from SVD files and improved debugging with SVD2LLDB plugin</li>
</ul>

<h3>
  
  
  C Interoperability
</h3>

<ul>
<li>
<strong><code>@c</code> attribute</strong>: Define C-compatible functions and enums (from SE-0495)
</li>
</ul>

<div class="highlight js-code-highlight">
<pre class="highlight swift"><code><span class="kd">@c</span><span class="p">(</span><span class="kt">MyLib_initialize</span><span class="p">)</span>
<span class="kd">public</span> <span class="kd">func</span> <span class="nf">initialize</span><span class="p">()</span> <span class="p">{</span> <span class="o">...</span> <span class="p">}</span>
</code></pre>

</div>



<ul>
<li>
<strong>Improved type matching</strong>: Better tolerance for mismatching C signatures, eliminating cryptic deserialization errors</li>
</ul>

<h3>
  
  
  Debugging
</h3>

<ul>
<li>
<strong>Enhanced LLDB support</strong>: Better value printing for Embedded Swift types</li>
<li>
<strong>Core dump inspection</strong>: Dictionary, Array, and other common types now inspectable without a live process</li>
<li>
<strong>ARMv7m exception unwinding</strong>: Complete backtraces through exception frames</li>
</ul>

<h3>
  
  
  Linking &amp; Compilation
</h3>

<ul>
<li>
<strong><code>@section</code> and <code>@used</code> attributes</strong>: Control where globals are emitted and ensure symbols aren't stripped (SE-0492)</li>
<li>
<strong>Weak symbol definitions</strong>: Fixes duplicate symbol errors in diamond dependencies</li>
<li>
<strong><code>@export</code> attribute</strong>: Better control over function visibility (SE-0497)</li>
</ul>




<p><em>Want to dive deeper? Read the <a href="https://www.swift.org/blog/embedded-swift-improvements-coming-in-swift-6.3/" rel="noopener noreferrer">full announcement</a> on Swift.org</em></p>

تقدم Swift 6.3 تحسينات كبيرة على Embedded Swift، مما يعزز وظيفته في البيئات ذات الموارد المحدودة مثل المتحكمات الدقيقة. تشمل التحسينات الرئيسية قدرات جديدة لطباعة الأعداد العشرية، وتشخيصات أفضل مع مجموعة EmbeddedRestrictions، وإدخال Swift MMIO 0.1.x لتوليد الشيفرة وتصحيح الأخطاء.

Swift 6.3 presenta mejoras significativas en Embedded Swift, aumentando su funcionalidad para entornos con recursos limitados como microcontroladores. Las mejoras clave incluyen nuevas capacidades de impresión de números de punto flotante, mejores diagnósticos con el grupo EmbeddedRestrictions y la introducción de Swift MMIO 0.1.x para la generación de código y la depuración.

Swift 6.3 apporte des améliorations significatives à Embedded Swift, renforçant sa fonctionnalité pour les environnements à ressources limitées comme les microcontrôleurs. Les principales améliorations comprennent de nouvelles capacités d'impression de nombres à virgule flottante, de meilleurs diagnostics avec le groupe EmbeddedRestrictions, et l'introduction de Swift MMIO 0.1.x pour la génération de code et le débogage.

Swift 6.3 introduces significant upgrades to Embedded Swift, enhancing its functionality for resource-constrained environments like microcontrollers. Key improvements include new floating-point printing capabilities, better diagnostics with the EmbeddedRestrictions group, and the introduction of Swift MMIO 0.1.x for code generation and debugging.

Embedded Swift Gets Major Upgrades in Swift 6.3

<a href="https://www.techspot.com/news/110317-judge-dismisses-lawsuit-twice-due-alleged-deepfake-video.html" target="_blank"><img src="https://www.techspot.com/images2/news/ts3_thumbs/2025/11/2025-11-19-ts3_thumbs-252.jpg" width="800" height="560" style="padding: 15px 0" title="Judge dismisses lawsuit twice due to alleged deepfake video testimony" /></a><br />A California housing dispute is getting media attention over allegations that lawyers presented a deepfake video as witness testimony. NBC News reports that Judge Victoria Kolakowski became suspicious after the supposed witness showed signs that something was not right, including a monotone voice, fuzzy facial features, and repeated facial expressions....<br /><br /><a href="https://www.techspot.com/news/110317-judge-dismisses-lawsuit-twice-due-alleged-deepfake-video.html">Read Entire Article</a><br /><br />

تجذب نزاع سكني في كاليفورنيا الانتباه الإعلامي بعد ظهور مزاعم بأن المحامين قدموا فيديو مزيف كدليل شهود. أعربت القاضية فيكتوريا كولاكوفسكي عن شكوكها بشأن الفيديو، مشيرة إلى صوت الشاهد الأحادي، وملامح الوجه غير الواضحة، وتكرار التعبيرات. أدى ذلك إلى رفض الدعوى القضائية مرتين.

Una disputa de vivienda en California ha llamado la atención de los medios tras las alegaciones de que los abogados presentaron un video deepfake como testimonio. La jueza Victoria Kolakowski expresó su escepticismo sobre el video, señalando la voz monótona del testigo, rasgos faciales borrosos y expresiones repetitivas. Esto llevó al desestimado de la demanda en dos ocasiones.

Un litige immobilier en Californie suscite l'attention des médias après des allégations selon lesquelles des avocats auraient présenté une vidéo deepfake comme témoignage. La juge Victoria Kolakowski a exprimé des doutes sur la vidéo, notant la voix monotone du témoin, des traits faciaux flous et des expressions répétitives. Cela a conduit à l'annulation de la poursuite à deux reprises.

A California housing dispute has drawn attention after allegations surfaced that lawyers presented a deepfake video as witness testimony. Judge Victoria Kolakowski expressed skepticism about the video, noting the witness's monotone voice, unclear facial features, and repetitive expressions. This led to the dismissal of the lawsuit on two occasions.

Judge dismisses lawsuit twice due to alleged deepfake video testimony

arXiv:2511.13891v1 Announce Type: new 
Abstract: Among soil erosion problems, Ephemeral Gullies are one of the most concerning phenomena occurring in agricultural fields. Their short temporal cycles increase the difficulty in automatically detecting them using classical computer vision approaches and remote sensing. Also, due to scarcity of and the difficulty in producing accurate labeled data, automatic detection of ephemeral gullies using Machine Learning is limited to zero-shot approaches which are hard to implement. To overcome these challenges, we present the first weakly supervised pipeline for detection of ephemeral gullies. Our method relies on remote sensing and uses Vision Language Models (VLMs) to drastically reduce the labor-intensive task of manual labeling. In order to achieve that, the method exploits: 1) the knowledge embedded in the VLM's pretraining; 2) a teacher-student model where the teacher learns from noisy labels coming from the VLMs, and the student learns by weak supervision using teacher-generate labels and a noise-aware loss function. We also make available the first-of-its-kind dataset for semi-supervised detection of ephemeral gully from remote-sensed images. The dataset consists of a number of locations labeled by a group of soil and plant scientists, as well as a large number of unlabeled locations. The dataset represent more than 18,000 high-resolution remote-sensing images obtained over the course of 13 years. Our experimental results demonstrate the validity of our approach by showing superior performances compared to VLMs and the label model itself when using weak supervision to train an student model. The code and dataset for this work are made publicly available.

تشكل الأودية المؤقتة تحديًا كبيرًا في الحقول الزراعية بسبب تكوينها السريع وصعوبة اكتشافها باستخدام الطرق التقليدية. كما أن ندرة البيانات المعلّمة بدقة تعقد من عملية الاكتشاف التلقائي لهذه الميزات من خلال التعلم الآلي. لمعالجة هذه المشكلات، تم تطوير خط أنابيب ضعيف الإشراف للكشف عن الأودية المؤقتة في صور الاستشعار عن بعد. تعتمد هذه الطريقة على نماذج اللغة البصرية (VLM) لتقليل الحاجة إلى التسمية اليدوية المكثفة، مستخدمة نموذج المعلم-الطالب لتحسين التعلم من التسميات الضوضائية.

Las gullies efímeras representan un desafío significativo en los campos agrícolas debido a su rápida formación y la dificultad de detección mediante métodos tradicionales. La escasez de datos etiquetados con precisión complica aún más la detección automática de estas características a través del aprendizaje automático. Para abordar estos problemas, se ha desarrollado un nuevo pipeline débilmente supervisado para detectar gullies efímeras en imágenes de teledetección. Este método aprovecha los Modelos de Lenguaje Visual (VLM) para minimizar la necesidad de etiquetado manual extenso, utilizando …

Les ravines éphémères représentent un défi majeur dans les champs agricoles en raison de leur formation rapide et de la difficulté de détection avec des méthodes traditionnelles. La rareté des données étiquetées avec précision complique davantage la détection automatique de ces caractéristiques par apprentissage automatique. Pour surmonter ces problèmes, un nouveau pipeline faiblement supervisé a été développé pour détecter les ravines éphémères dans les images de télédétection. Cette méthode s'appuie sur des modèles de langage visuel (VLM) pour minimiser le besoin d'étiquetage manuel, en util…

Ephemeral gullies pose significant challenges in agricultural fields due to their rapid formation and difficulty in detection using traditional methods. The scarcity of accurately labeled data further complicates the automatic detection of these features through machine learning. To address these issues, a new weakly supervised pipeline has been developed for detecting ephemeral gullies in remote sensing images. This method leverages Vision Language Models (VLMs) to minimize the need for extensive manual labeling, utilizing a teacher-student model to enhance learning from noisy labels.

Weakly Supervised Ephemeral Gully Detection In Remote Sensing Images Using Vision Language Models

arXiv:2511.13786v1 Announce Type: cross 
Abstract: Single-cell RNA sequencing (scRNA-seq), especially temporally resolved datasets, enables genome-wide profiling of gene expression dynamics at single-cell resolution across discrete time points. However, current technologies provide only sparse, static snapshots of cell states and are inherently influenced by technical noise, complicating the inference and representation of continuous transcriptional dynamics. Although embedding methods can reduce dimensionality and mitigate technical noise, the majority of existing approaches typically treat trajectory inference separately from embedding construction, often neglecting temporal structure. To address this challenge, here we introduce CellStream, a novel deep learning framework that jointly learns embedding and cellular dynamics from single-cell snapshot data by integrating an autoencoder with unbalanced dynamical optimal transport. Compared to existing methods, CellStream generates dynamics-informed embeddings that robustly capture temporal developmental processes while maintaining high consistency with the underlying data manifold. We demonstrate CellStream's effectiveness on both simulated datasets and real scRNA-seq data, including spatial transcriptomics. Our experiments indicate significant quantitative improvements over state-of-the-art methods in representing cellular trajectories with enhanced temporal coherence and reduced noise sensitivity. Overall, CellStream provides a new tool for learning and representing continuous streams from the noisy, static snapshots of single-cell gene expression.

CellStream هو إطار جديد للتعلم العميق مصمم لتحسين تحليل بيانات تسلسل RNA على مستوى الخلية الواحدة (scRNA-seq). يتناول القيود التي تفرضها التقنيات الحالية التي تقدم فقط لقطات متفرقة لحالات الخلايا، والتي غالبًا ما تتأثر بالضوضاء التقنية. من خلال دمج مشفر تلقائي مع نقل ديناميكي غير متوازن، يتعلم CellStream معًا التضمين والديناميات الخلوية، مما يحسن استنتاج الديناميات النسخية المستمرة من بيانات اللقطات.

CellStream es un nuevo marco de aprendizaje profundo diseñado para mejorar el análisis de datos de secuenciación de ARN de una sola célula (scRNA-seq). Aborda las limitaciones de las tecnologías actuales que solo proporcionan instantáneas dispersas de los estados celulares, que a menudo se ven afectadas por el ruido técnico. Al integrar un autoencoder con transporte óptimo dinámico no equilibrado, CellStream aprende conjuntamente la representación y la dinámica celular, mejorando la inferencia de las dinámicas transcripcionales continuas a partir de datos instantáneos.

CellStream est un nouveau cadre d'apprentissage profond conçu pour améliorer l'analyse des données de séquençage d'ARN à cellule unique (scRNA-seq). Il répond aux limites des technologies actuelles qui ne fournissent que des instantanés épars des états cellulaires, souvent affectés par le bruit technique. En intégrant un autoencodeur avec un transport optimal dynamique déséquilibré, CellStream apprend conjointement l'embedding et la dynamique cellulaire, améliorant l'inférence des dynamiques transcriptionnelles continues à partir des données instantanées.

CellStream is a new deep learning framework designed to enhance the analysis of single-cell RNA sequencing (scRNA-seq) data. It addresses the limitations of current technologies that provide only sparse snapshots of cell states, which are often affected by technical noise. By integrating an autoencoder with unbalanced dynamical optimal transport, CellStream jointly learns embedding and cellular dynamics, improving the inference of continuous transcriptional dynamics from snapshot data.

CellStream: Dynamical Optimal Transport Informed Embeddings for Reconstructing Cellular Trajectories from Snapshots Data

يجادل الخبراء بأن لجان الأقران ضرورية لتقييم موثوقية أنظمة الذكاء الاصطناعي، حيث إن الاعتماد فقط على الخبراء الفرديين قد لا يوفر تقييمًا شاملاً. يبرز هذا النداء لتشكيل لجان الأقران الحاجة إلى وجهات نظر متنوعة لفهم تداعيات الذكاء الاصطناعي وضمان نشره بشكل مسؤول. تهدف هذه المقاربة إلى تعزيز المساءلة والشفافية في تطوير الذكاء الاصطناعي، ومعالجة المخاوف بشأن التحيز والموثوقية في تقنيات الذكاء الاصطناعي.

Expertos argumentan que los paneles de pares son esenciales para evaluar la confiabilidad de los sistemas de inteligencia artificial (IA), ya que depender únicamente de expertos individuales puede no proporcionar una evaluación completa. La llamada a la formación de paneles de pares enfatiza la necesidad de perspectivas diversas para comprender las implicaciones de la IA y garantizar su implementación responsable. Este enfoque busca mejorar la responsabilidad y la transparencia en el desarrollo de la IA, abordando preocupaciones sobre sesgos y confiabilidad en las tecnologías de IA.

Des experts soutiennent que des panels de pairs sont essentiels pour évaluer la fiabilité des systèmes d'intelligence artificielle (IA), car s'appuyer uniquement sur des experts individuels peut ne pas fournir une évaluation complète. Cet appel à des panels de pairs souligne la nécessité de perspectives diverses pour comprendre les implications de l'IA et garantir son déploiement responsable. Cette approche vise à améliorer la responsabilité et la transparence dans le développement de l'IA, en répondant aux préoccupations concernant les biais et la fiabilité des technologies d'IA.

Experts argue that panels of peers are essential for assessing the trustworthiness of artificial intelligence (AI) systems, as relying solely on individual experts may not provide a comprehensive evaluation. The call for peer panels emphasizes the need for diverse perspectives in understanding AI's implications and ensuring its responsible deployment. This approach aims to enhance accountability and transparency in AI development, addressing concerns about bias and reliability in AI technologies.

Panels of peers are needed to gauge AI’s trustworthiness — experts are not enough

طورت ImmunoMatch نموذجًا للتعلم الآلي يتنبأ بترابط سلاسل الأجسام المضادة الثقيلة والخفيفة. هذه الخطوة مهمة لفهم الاستجابات المناعية وقد تعزز تطوير الأجسام المضادة العلاجية. يستخدم النموذج البيانات للتعلم وإجراء التنبؤات حول تفاعلات الأجسام المضادة، مما قد يؤدي إلى علاجات أكثر فعالية في علم المناعة.

ImmunoMatch ha desarrollado un modelo de aprendizaje automático que predice el emparejamiento de cadenas de inmunoglobulina pesada y ligera. Este avance es significativo para comprender las respuestas inmunitarias y podría mejorar el desarrollo de anticuerpos terapéuticos. El modelo utiliza datos para aprender y hacer predicciones sobre las interacciones de las inmunoglobulinas, lo que podría conducir a tratamientos más efectivos en inmunología.

ImmunoMatch a développé un modèle d'apprentissage automatique qui prédit l'appariement des chaînes d'immunoglobulines lourdes et légères. Cette avancée est significative pour comprendre les réponses immunitaires et pourrait améliorer le développement d'anticorps thérapeutiques. Le modèle utilise des données pour apprendre et faire des prédictions sur les interactions des immunoglobulines, ce qui pourrait conduire à des traitements plus efficaces en immunologie.

ImmunoMatch has developed a machine learning model that predicts the pairing of heavy and light immunoglobulin chains. This advancement is significant for understanding immune responses and could enhance the development of therapeutic antibodies. The model utilizes data to learn and make predictions about immunoglobulin interactions, potentially leading to more effective treatments in immunology.

ImmunoMatch learns and predicts cognate pairing of heavy and light immunoglobulin chains

يتناول المقال دمج الذكاء الاصطناعي في تطوير تقنيات تحرير الجينوم المعتمدة على كريسبر. يبرز كيف يمكن للذكاء الاصطناعي تحسين دقة وكفاءة تطبيقات كريسبر، مما قد يؤدي إلى تقدم في البحث الجيني والتدخلات العلاجية. يمكن أن تُحدث الشراكة بين الذكاء الاصطناعي وكريسبر ثورة في مجالات مثل الطب والزراعة والتكنولوجيا الحيوية، مما يجعل تحرير الجينوم أكثر سهولة وفعالية.

El artículo aborda la integración de la inteligencia artificial (IA) en el avance de las tecnologías de edición del genoma basadas en CRISPR. Destaca cómo la IA puede mejorar la precisión y eficiencia de las aplicaciones de CRISPR, lo que podría llevar a avances en la investigación genética y en intervenciones terapéuticas. La colaboración entre la IA y CRISPR podría revolucionar campos como la medicina, la agricultura y la biotecnología, haciendo que la edición del genoma sea más accesible y efectiva.

L'article traite de l'intégration de l'intelligence artificielle (IA) dans l'avancement des technologies d'édition génomique basées sur CRISPR. Il souligne comment l'IA peut améliorer la précision et l'efficacité des applications CRISPR, ce qui pourrait conduire à des percées dans la recherche génétique et les interventions thérapeutiques. La collaboration entre l'IA et CRISPR pourrait révolutionner des domaines tels que la médecine, l'agriculture et la biotechnologie, rendant l'édition génomique plus accessible et efficace.

The article discusses the integration of artificial intelligence (AI) in advancing CRISPR-based genome editing technologies. It highlights how AI can enhance the precision and efficiency of CRISPR applications, potentially leading to breakthroughs in genetic research and therapeutic interventions. The collaboration between AI and CRISPR could revolutionize fields such as medicine, agriculture, and biotechnology, making genome editing more accessible and effective.

STPath: a generative foundation model for integrating spatial transcriptomics and whole-slide images

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