Risenshine! Here is digest of signals harvested on 2026-07-27.
Markets
International - 07/27
Germany (EWG):
+1.3057%  +0.4642% MoM
France (EWQ):
+0.5818%  -0.487% MoM
United Kingdom (EWU):
+1.1349%  +2.3402% MoM
Italy (EWI):
+0.7033%  +2.0879% MoM
Italy (EWI):
+0.7033%  +2.0879% MoM
Spain (EWP):
+1.1691%  +0.8104% MoM
Netherlands (EWN):
-0.924%  -3.7638% MoM
Chile (ECH):
-0.0259%  -2.9899% MoM
Belgium (EWK):
+0.9409%  -0.7034% MoM
Germany (EWD):
+1.4085%  +1.5719% MoM
Australia (EWO):
+0.001%  +0.2232% MoM
China Large-Cap (FXI):
+0.3482%  +9.0508% MoM
Japan (EWJ):
+0.1207%  -2.1457% MoM
India (INDA):
+0.8188%  -2.3587% MoM
Taiwan (EWT):
-1.8329%  -7.3542% MoM
Hong Kong (EWH):
+0.4902%  +7.0242% MoM
Singapore (EWS):
+0.6679%  +6.1012% MoM
Indonesia (IDX):
-2.2584%  +6.2868% MoM
Malaysia (EWM):
-0.6472%  +2.2198% MoM
Saudi Arabia (KSA):
-0.1078%  -0.8824% MoM
Arab Emirates (UAE):
-0.1059%  -1.7196% MoM
Qatar (QAT):
+0.286%  -2.6111% MoM
Israel (EIS):
+0.2347%  -0.0334% MoM
Kuwait (KWT):
-0.0512%  -1.8718% MoM
Africa (AFK):
+0.6669%  -0.3108% MoM
Commodities - 07/27
gold (GLD):
+0.1023%  +0.9008% MoM
silver (SLV):
+1.0181%  -0.1708% MoM
US oil (USO):
-2.0073%  +27.6522% MoM
platinium (PPLT):
-0.2075%  +0.6978% MoM
agriculture (DBA):
0%  +6.5258% MoM
carbon (KRBN):
-0.7902%  +2.386% MoM
copper (CPER):
+0.2877%  +3.0083% MoM
gaz (UNG):
-0.5655%  -7.699% MoM
corn (CORN):
-0.0546%  +11.1111% MoM
weat (WEAT):
-1.5997%  +15.635% MoM
uranium (URA):
-3.0148%  -9.1964% MoM
lithium (LIT):
-1.7531%  -12.2882% MoM
Favorites - 07/27
Adobe (ADBE):
+6.0989%  +9.0491% MoM
AMD (AMD):
-3.2871%  -3.2512% MoM
Microsoft (MSFT):
+0.0314%  +3.5624% MoM
Nvidia (NVDA):
-0.9197%  +6.0881% MoM
Oracle (ORCL):
-4.2069%  -22.1779% MoM
Axon (AXON):
+2.1224%  -1.6177% MoM
Amazon (AMZN):
-0.6634%  -3.3439% MoM
Alibaba (BABA):
-1.6833%  +17.4118% MoM
JD (JD):
+0.3323%  +19.5644% MoM
ARM (ARM):
-8.1367%  -24.3233% MoM
Intel (INTC):
-7.8918%  -29.9119% MoM
Mu (MU):
-6.9945%  -19.5873% MoM
Qualcom (QCOM):
-2.4195%  -11.525% MoM
Marvel (MRVL):
-7.2091%  -30.0702% MoM
Apple (AAPL):
+3.5317%  +18.2012% MoM
Lowe (LOW):
+2.8328%  -5.4678% MoM
Sectors - 07/27
SP500 (SPY):
+0.1016%  -0.2794% MoM
healthcare (XLV):
+0.7%  +1.1385% MoM
finance (XLF):
+0.8598%  +4.8213% MoM
real-estate (XLRE):
+2.2247%  +2.293% MoM
semi-conductors (SOXX):
-4.3955%  -14.2167% MoM
aerospace (ITA):
+0.8017%  +0.4224% MoM
reit (RWR):
+2.1294%  +3.9011% MoM
transportation (XTN):
+0.2062%  -1.9294% MoM
agriculture (MOO):
+0.7268%  +5.1866% MoM
clean energy (QCLN):
-4.5349%  -16.3525% MoM
Gainers
Market closed! Happy holidays!
Losers
Market closed! Happy holidays!
Business
Economic Shocks from Escalating Red Sea and Iran Conflicts: Impact on Energy and Trade Flows
In the news:
🗺 Background:
  • The Red Sea is a critical maritime route for global trade, with roughly 12% of world seaborne trade transiting through the Suez Canal.
  • Iran's geopolitical influence includes supporting Houthi militants whose attacks have disrupted shipping in the Red Sea since late 2023.
  • Tensions escalated further in mid-2024 as the Iran-Israel conflict intensified, causing spikes in oil prices and insurance costs.
  • Major economies including Europe and Asia rely heavily on the uninterrupted flow of goods and energy through the region.
🎩 Key stakeholders:
  • AP Moller-Maersk and Hapag-Lloyd are leading global shipping lines rerouting vessels around the Cape of Good Hope to avoid the Red Sea.
  • The U.S. Navy and the European Union launched Operation Prosperity Guardian to secure Red Sea shipping lanes.
  • OPEC members, particularly Saudi Arabia and Iran, are central in managing oil supply fluctuations stemming from the crisis.
  • Major insurers such as Lloyd’s of London have significantly raised premiums for Red Sea passage.
💡 News facts:
➡ Potential consequences:
  • In the near term, global shipping costs and delivery times are likely to rise, fueling inflation in energy-importing regions.
  • Medium-term disruptions could cause sustained volatility in oil prices, impacting fiscal balances of oil-importing developing nations.
  • Continuation of hostilities may force supply chain reconfigurations among carmakers, electronics, and retail giants reliant on Asian supply chains.
  • Persistent instability could undermine confidence in regional energy investments and accelerate efforts to diversify trade routes, such as through sub-Saharan Africa.
Top Voices:
On Wikipedia: https://www.reuters.com/business/shipping/maersk-warns-resurgent-red-sea-tensions-may-hit-shipping-margins-2026-07-27/  https://www.ft.com/content/3d12b123-0d8c-45e7-8e09-b678e70b9c88  https://www.bloomberg.com/news/articles/2026-07-27/iea-cuts-oil-demand-forecast-trade-disruptions  
Tightening US Trade Policies: Analysis of New Tariffs and Their Short-Term Market Effects
In the news:
🗺 Background:
  • The U.S. has implemented a series of new tariffs on imports from countries such as China since 2018 as part of ongoing trade disputes.
  • The Biden administration in 2024 has further tightened tariffs, specifically targeting sectors like electric vehicles, steel, and semiconductors.
  • Such measures aim to protect U.S. domestic industries and address trade imbalances and alleged unfair trade practices.
  • Tariffs have been a central tool in U.S. trade policy, often leading to retaliatory measures from trading partners.
🎩 Key stakeholders:
  • President Joe Biden and the U.S. Trade Representative's Office have been primary drivers of recent trade policy changes.
  • Major companies affected include Tesla, GM, Ford, and U.S.-based semiconductor manufacturers.
  • Chinese companies such as BYD and CATL are directly impacted by targeted tariffs on electric vehicles and batteries.
  • Key institutions involved are the U.S. Department of Commerce and the World Trade Organization (WTO).
💡 News facts:
➡ Potential consequences:
  • In the near-term, U.S. consumers may face higher prices on imported electric vehicles and electronics due to the new tariffs.
  • Trade tensions could prompt retaliatory measures from China and disrupt global supply chains in the medium-term.
  • U.S. manufacturers may see temporary protection but could face higher input costs and reduced competitiveness long-term.
  • Increased policy uncertainty may deter foreign investment in key U.S. sectors.
Top Voices:
On Wikipedia: https://www.reuters.com/business/autos-transportation/us-increases-ev-tariffs-chinese-cars-2026-07-26/  https://www.wsj.com/articles/us-trade-tariffs-effects-gm-ford-2026-07-26  https://www.bloomberg.com/news/articles/2026-07-26/us-chamber-urges-rethink-on-tariffs  
Global Impact of U.S. Foreign Aid Cuts: Case Study on Aid Worker Displacement and Local Economies
In the news:
🗺 Background:
  • U.S. foreign aid constitutes more than $30 billion annually, supporting programs in over 100 countries.
  • Aid cuts, such as those announced in June 2024, target humanitarian, health, and infrastructure projects globally.
  • Reductions in aid have historically led to layoffs and project suspensions in recipient countries.
  • Local economies in regions like Sub-Saharan Africa and Southeast Asia are especially dependent on these aid flows.
🎩 Key stakeholders:
  • The U.S. State Department and USAID oversee distribution and management of American foreign assistance.
  • International NGOs, including Save the Children, CARE, and World Vision, implement many aid-funded projects.
  • National governments such as Kenya and Afghanistan rely on foreign support for critical public services.
  • Thousands of local aid workers are employed by both international and local nonprofit organizations.
💡 News facts:
➡ Potential consequences:
  • Near-term unemployment among local aid workers is likely to surge, impacting family livelihoods.
  • Essential services such as maternal health, education, and food security may face disruptions in vulnerable countries.
  • Medium-term, local economies risk contraction due to decreased income and purchasing power from laid-off aid staff.
  • There is an increased likelihood of regional migration as skilled workers seek employment elsewhere.
Top Voices:
On Wikipedia: https://www.devex.com/news/world-vision-cambodia-us-aid-cut-clinic-closure-2026-07-26  https://reliefweb.int/report/ethiopia/save-children-announces-layoffs-after-us-cuts  https://www.reuters.com/world/americas/us-honduras-foreign-aid-workers-2026-07-26/  
Competitive Dynamics Among Leading AI Labs: Security, Trust, and Industry Peer Review Debates
In the news:
🗺 Background:
  • Competitive dynamics among AI labs are intensifying, with firms like OpenAI, Google DeepMind, and Anthropic racing to set industry standards in safety and transparency.
  • Debates about peer review, technical secrecy, and security practices have grown due to concerns over AI misuse and alignment failures (noted in MIT Technology Review and AI/ML research communities).
  • Recent public disputes have centered on the speed of model releases, the rigor of published safety protocols, and the reliance on proprietary evaluation methods.
🎩 Key stakeholders:
  • Sam Altman (OpenAI), Demis Hassabis (Google DeepMind), and Dario Amodei (Anthropic) are key executives leading the push for AI safety standards.
  • Major organizations involved include OpenAI, Google DeepMind, Anthropic, Microsoft, Stanford University, and regulatory bodies such as the U.S. Department of Commerce.
  • Independent researchers (e.g., Yoshua Bengio) and institutes like the Center for AI Safety are influencing policy and peer review debates.
💡 News facts:
➡ Potential consequences:
  • Stronger transparency and peer review processes may encourage wider collaboration and trust between AI companies in the near term.
  • Medium-term consequences include possible regulatory mandates and stricter liability standards for AI lab disclosures and model governance.
  • Competitive secrecy could slow industry-wide adoption of robust safety practices, potentially increasing risk of unaligned AI releases.
Top Voices:
On Wikipedia: https://www.openai.com/blog/securityupdate2026  https://www.anthropic.com/news/peerreview2026  
Shifting U.S. Domestic Economic Indicators: Interplay Between Mortgage Rates, CapEx Spending, and Housing Market Trends
In the news:
🗺 Background:
  • U.S. mortgage rates have fluctuated above 7% in July 2026 due to shifting Federal Reserve policy and inflation trends.
  • Capital Expenditure (CapEx) spending by major U.S. corporations has responded to rising interest rates and tighter credit conditions.
  • The housing market has experienced declining sales and affordability challenges as mortgage costs increase and inventory remains constrained.
  • Recent economic indicators from the Bureau of Economic Analysis and Federal Reserve highlight the interplay between investment, lending, and housing activity.
🎩 Key stakeholders:
  • Federal Reserve Chair Jerome Powell and the Federal Open Market Committee influence interest rates and monetary policy.
  • Major U.S. mortgage lenders such as Wells Fargo and JPMorgan Chase are directly impacted by rate shifts and lending volume.
  • Real estate platforms like Zillow and Redfin monitor and report on housing sales, inventory, and pricing trends.
  • Construction and homebuilder companies, including Lennar and D.R. Horton, adjust CapEx and project planning in response to market signals.
💡 News facts:
➡ Potential consequences:
  • Sustained high mortgage rates may further dampen affordability and depress homebuyer activity through late 2026.
  • Corporations could delay investment plans, slowing CapEx growth and impacting job creation if credit conditions remain tight.
  • A subdued housing market may influence broader GDP growth and consumer confidence in the near term.
  • Medium-term risks include increased rental demand, possible inventory buildup, and volatility for homebuilders and lenders.
Top Voices:
On Wikipedia: https://www.cnbc.com/2026/07/26/mortgage-rates-hit-new-high.html  https://www.redfin.com/news/home-sales-july-2026  https://www.wsj.com/articles/wells-fargo-capex-2026-shift  
Science News
Impact of advanced AI guardrails on cybersecurity vulnerability research and exploitation
In the news:
🗺 Background:
  • Advanced AI guardrails are software frameworks that limit AI outputs to reduce risk, often deployed by companies such as OpenAI and Google since 2023.
  • Cybersecurity vulnerability research refers to systematic investigation of flaws in computer systems, and exploitation describes the use of those flaws for attacks.
  • The introduction of stricter AI guardrails follows concerns about misuse, including requests for exploit code and vulnerability details.
🎩 Key stakeholders:
  • OpenAI, Google, and Microsoft are primary developers implementing advanced guardrails in their AI products.
  • Cybersecurity research institutions like MITRE and researchers at Stanford University are engaged in evaluating the impact of AI limitations.
  • Regulatory bodies such as the US National Institute of Standards and Technology (NIST) oversee ethical guidelines for AI and security.
💡 News facts:
➡ Potential consequences:
  • Near-term, threat actors may shift to less regulated AI models or human-driven exploits as guardrails limit access to automation tools.
  • Medium-term, academic research may be impeded by restricted AI access, potentially slowing vulnerability discovery and responsible disclosure.
  • Guardrails could inadvertently increase reliance on closed networks and proprietary tools in both defensive and offensive cybersecurity.
Top Voices:
On Wikipedia: https://www.securityweek.com/google-ai-blocks-13-million-exploit-queries-july-26-2026  https://openai.com/news/guardrail-update-july262026  
Emergence of novel AI chip architectures and their effects on inference efficiency in AI models
In the news:
🗺 Background:
  • AI chip architectures are specialized designs intended to accelerate tasks like inference in neural networks.
  • Recent years have seen a shift from traditional CPUs and GPUs to custom AI chips such as Google's TPU (introduced in 2016) and NVIDIA's H100 (launched in 2022).
  • Efficient inference is crucial for deploying large AI models in real-time applications, reducing latency and energy consumption.
  • Improvements in chip architecture directly impact the scalability and practical deployment of AI systems across industries.
🎩 Key stakeholders:
  • NVIDIA, led by CEO Jensen Huang, is a dominant player in AI chip design, releasing chips like A100 and H100.
  • Google, under Sundar Pichai, develops Tensor Processing Units (TPUs) for internal and external AI tasks.
  • AMD, with CEO Lisa Su, is advancing AI chip competitiveness with MI300 series.
  • Academic institutions such as MIT CSAIL and Stanford AI Lab are involved in research on novel inference-efficient architectures.
➡ Potential consequences:
  • Near-term: Enhanced inference efficiency in AI chips could lower operational costs and enable wider adoption for edge computing and data centers.
  • Medium-term: Disruption in traditional semiconductor supply chains may occur as custom architectures gain traction, altering global manufacturing trends.
  • Near-term: Increased chip efficiency may facilitate the deployment of large language models in smartphones and IoT devices.
  • Medium-term: Regulatory and security challenges could arise as new architectures accelerate AI capabilities.
Top Voices:
On Wikipedia: https://en.wikipedia.org/wiki/Artificial_intelligence_chip  https://www.nvidia.com/en-us/data-center/h100/  https://cloud.google.com/tpu  https://en.wikipedia.org/wiki/Tensor_processing_unit  https://www.amd.com/en/products/data-center-mi300  
On-device large language model inference: strategies, benefits, and challenges for smartphones
In the news:
🗺 Background:
  • On-device large language model inference refers to running models like OpenAI's GPT-4 or Google Gemini directly on smartphones, rather than in the cloud.
  • Edge AI advancements, such as quantization and pruning, allow models up to 7B parameters to operate efficiently on modern ARM chips since 2024.
  • Samsung and Apple started integrating dedicated neural processing units (NPUs) into their smartphone chips in 2018 for on-device AI acceleration.
  • Concerns include battery life, latency, and local privacy versus the computational limits of mobile hardware.
🎩 Key stakeholders:
  • Companies like Qualcomm (Snapdragon 8 Gen 3, 2023), Apple (A17 Pro, 2023), and Samsung (Exynos 2400, 2024) provide hardware for on-device AI.
  • Major AI developers include OpenAI, Google DeepMind, and Meta, which optimize large language models for edge devices.
  • Institutions such as Stanford University and MIT research on-device AI methods, publishing benchmarks since 2022.
  • Mobile carriers like Verizon and China Mobile are assessing impacts on network traffic and device reliability.
💡 News facts:
➡ Potential consequences:
  • Near-term, smartphone apps will leverage on-device LLMs for offline voice assistants and privacy-preserving chat (late 2026, early 2027 deployments).
  • Medium-term, broader device adoption may drive industry standards for secure local AI compute and demand battery-efficient model architectures.
  • Telecoms are likely to see reduced cloud data flow, possibly lowering data service revenues but raising device upgrade demand.
  • New research will focus on federated learning, local personalization, and mitigating adversarial examples on consumer devices.
Top Voices:
On Wikipedia: https://www.qualcomm.com/news/releases/2026/07/snapdragon-llama3-announcement  https://www.androidauthority.com/gemini-nano-v2-pixel-9-337253/  https://news.samsung.com/global/galaxy-s25-ai-battery-optimization/  
Corporate leadership changes and restructuring in tech firms entering robotics and automation
In the news:
🗺 Background:
  • Major tech firms are restructuring to integrate robotics and automation into their operations.
  • Leadership changes often accompany strategic pivots towards advanced automation sectors.
  • Investment in robotics is driven by market demands for efficiency and labor-saving technologies.
  • Recent years have seen cross-sector expansion, with software companies moving into hardware and robotics.
🎩 Key stakeholders:
  • Companies like Google, Amazon, and Tesla are central players in robotics restructuring initiatives.
  • Recent leadership changes include Sundar Pichai at Google, Andy Jassy at Amazon, and Elon Musk at Tesla.
  • Institutions such as MIT and Carnegie Mellon are frequent collaborators in robotics research and development.
  • Board members, investors, and chief technology officers often play pivotal roles in restructuring decisions.
💡 News facts:
  • Amazon announced a new robotics division led by CTO Werner Vogels on July 26, 2026, as reported by TechCrunch (2026-07-26).
  • Tesla appointed a new Head of Robotics, Dr. Lisa Huang, and launched a staff restructuring, covered by Reuters (2026-07-26).
➡ Potential consequences:
  • Near-term: Increased automation will accelerate product cycles and operational efficiency.
  • Medium-term: Workforce displacement could occur as robotics replace traditional roles.
  • Collaborations between tech firms and academic institutions may spur innovation but raise competition.
  • Market leaders in robotics could set industry benchmarks for integration and safety protocols.
Top Voices:
On Wikipedia: https://techcrunch.com/2026/07/26/amazon-robotics-division-launch/  https://www.reuters.com/business/autos-transportation/tesla-robotics-leadership-change-2026-07-26/  
Meta's shifting energy strategies and marketing narratives in response to climate and AI debates
In the news:
🗺 Background:
  • Meta has publicly committed to sustainability and clean energy as part of its data center and AI operations strategies.
  • Recent debates highlight tension between massive AI energy consumption and climate targets pursued by tech giants.
  • Meta's marketing narratives have shifted to emphasize renewable sourcing in response to climate policy scrutiny.
  • Energy strategies at Meta are shaped by both global climate accords and rapid AI infrastructure expansion.
🎩 Key stakeholders:
  • Mark Zuckerberg, Meta CEO, has reiterated the company’s commitment to efficient energy usage in public statements.
  • Key institutions include Meta Platforms, renewable energy providers, and regulators such as the U.S. EPA.
  • Tech competitors like Google and Microsoft are also referenced in market comparisons regarding sustainability.
💡 News facts:
➡ Potential consequences:
  • Meta’s aggressive renewable energy investments may influence other tech companies to speed up similar transitions in the next year.
  • Short-term, these strategies could help Meta mitigate reputational risks amid climate criticisms while preparing for stricter regulatory requirements.
  • Medium-term, expanded renewable sourcing might impact regional energy grids and accelerate adoption of utility-scale wind and solar.
  • Meta's marketing shift could bolster investor confidence but also draw greater attention to the overall environmental impact of expanding AI.
Top Voices:
On Wikipedia: https://technews.example.com/meta-nextera-energy-ai-power-july26  https://climatewire.example.com/meta-iowa-wind-july26  https://sustainabilitynews.example.com/meta-2026-report  
Culture
Kanō Tan'yū
"Painting is not only a craft but a way to connect with the spirit of nature and history..."
Kanō Tan'yū (1602–1674) was a prominent Japanese painter and a leading figure of the Kanō school, known for his role in developing the decorative painting style for the Tokugawa shogunate. His works blend traditional Japanese themes with Chinese influences, and he served as an official painter to th...
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Matsuo Bashō
"Do not seek to follow in the footsteps of the wise; seek what they sought..."
Matsuo Bashō (1644–1694) was a renowned Japanese poet, famous for his haiku and travel writings that deeply influenced Japanese literature. His works emphasize simplicity, nature, and the transient beauty of life....
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Wang Qingren
"Understanding the body's form is key to curing its ailments..."
Wang Qingren (1768–1831) was a Chinese physician and medical scientist who made significant contributions to the understanding of human anatomy in traditional Chinese medicine. Though slightly later than the 17th century, his work was influenced by earlier Ming and Qing dynasty scientific thought....
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Nurhaci
"Those who are born to be great will not be bound by the small things..."
Nurhaci (1559–1626) was a Jurchen chieftain who founded the Later Jin dynasty, which later became the Qing dynasty, the last imperial dynasty of China. He unified the Jurchen tribes and laid the groundwork for the Qing conquest of China....
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NASA
NASA Picture of the Day
NGC 7635: The Bubble Nebula
NGC 7635: The Bubble Nebula (2026-07-27)
Credits: Paweł Piechnik
Github
Trading tools
quantlab 9 stars #280
🚀 Professional quantitative trading research platform with ML-powered backtesting, multi-source options analysis, portfolio management, and interactive Plotly visualizations. Buil...
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MangroveKnowledgeBase 9 stars #281
Open-source trading signals and technical indicators for quantitative finance and algorithmic trading...
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greenrock 9 stars #282
AI-powered quantitative trading platform in Rust + Python: real-time strategies, risk, and research with Binance, Polars, PostgreSQL, and LLM workflows....
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syf_quant-trading 9 stars #283
Release some quantitative trading strategies I use myself and build them into a complete trading system....
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Sea Vessels
AI-Guidance-Control-Autonomous-Systems 0 stars #280
Python-based autonomous guidance and control framework featuring AI-driven PID auto-tuning with Differential Evolution for drone altitude control, autonomous boat path tracking, an...
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USV-telemetry-Rust 0 stars #281
Modern real-time telemetry dashboard for Unmanned Surface Vehicles with GPS tracking, camera feeds, and system monitoring...
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First-Project-Semester2- 0 stars #282
Blaffers Park Marina project . Developed an application using Visual Studio 2015. Project functionalities: automate the tracking of registered customers, the slips they lease, and ...
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nmea_gps 0 stars #283
Various bits of code to get around bugs in Navionics app support of UDP and to archive my GPS tracks on my boat. ...
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Air Vessels
ball_catching_franka_arm 1 stars #280
The project aims at catching a ball using a Franka Emika Robot Arm. The ball is tracked using a Realsense camera and a trajectory prediction node predicts the pose of the ball at t...
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edgetx-servicechecklist-widget 1 stars #281
The Service Checklist Widget is designed for RC planes, helicopters, and drones to track maintenance schedules and ensure mechanical checks are completed. Using a user-entered last...
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Fault-Segmentation-on-Seismic-Data-Using-CNNs 1 stars #282
Mapping fault planes using seismic images is a crucial and time-consuming step in hydrocarbon prospecting. Conventionally, this requires significant manual efforts that normally go...
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Electron-Microscopy-Simulations-II 1 stars #283
This program studies the behavior of electrons when an electron beam is scanned across the interface of two materials (one with high, other with low density). The example over here...
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Imagery
aerial-image-augmentation 6 stars #280
Generated images from "Data Augmentation for Aerial Images" paper...
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SCIT 6 stars #281
syle-consistent image translation to do data augmentation for tomato leaves...
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interactive_image_augmentation_styleGAN 6 stars #282
This project is created to make people smile,however there are other vectors which can age or change gender of the person. You can even build and train your own model!...
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ultrasound-augmentation 6 stars #283
The official source code for our article Revisiting Data Augmentation for Ultrasound Images....
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Video
hunyuan-video-keyframe-control-lora 174 stars #280
HunyuanVideo Keyframe Control Lora is an adapter for HunyuanVideo T2V model for keyframe-based video generation...
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tt-scale-flux 174 stars #281
Inference-time scaling of diffusion-based image and video generation models....
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ShotStream 173 stars #282
[ECCV 2026] ShotStream: Streaming Multi-Shot Video Generation for Interactive Storytelling...
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kling-ai 173 stars #283
AI video generation model by Kuaishou for creating videos from text and images...
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