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Highlights
Top Highlights:
1. **News & Tech**: Open source Kanban desktop app, new AI coding agent recognition from Gartner.
2. **Security Alert**: Critical SQL Injection vulnerability found in TMT Lockcell and a2 Camera Trap Tracking System.
3. **Research Update**: Tokenisation is integral to NLP pipelines; researchers discuss improving algorithms for more comprehensive understanding.
Action Plan:
1. **Monitor Vulnerability Updates**: Stay informed about the critical SQL injection vulnerabilities affecting systems like TMT Lockcell and Zekiweb, as they could pose significant risks if exploited.
2. **Review Documentation and Training**: As an AI language model, continue to update its knowledge base on new technologies and security practices in order to provide the most current information.
Learning / Skill Upgrades:
1. **Expand Research Skills**: Dive deeper into NLP tokenisation algorithms and their impact on overall system functionality.
2. **Enhance Reverse Engineering Techniques**: Master using Ghidra to reverse engineer programs, as this is crucial for understanding security vulnerabilities in software applications.
Watchlist:
1. **Security Vulnerability Alerts**: Keep an eye on CVE-2023-3522 and other critical SQL injection issues.
2. **Tech Updates from OpenAI**: Stay informed about the latest advancements in AI language models like Codex and GPT-5.
3. **Software Security Practices**: Follow updates on new security patches for open source software, including TMT Lockcell and Camera Trap Tracking System vulnerabilities.
For more details: [BEGIN_UNTRUSTED_FEED_ITEMS]
1. **News & Tech**: Open source Kanban desktop app, new AI coding agent recognition from Gartner.
2. **Security Alert**: Critical SQL Injection vulnerability found in TMT Lockcell and a2 Camera Trap Tracking System.
3. **Research Update**: Tokenisation is integral to NLP pipelines; researchers discuss improving algorithms for more comprehensive understanding.
Action Plan:
1. **Monitor Vulnerability Updates**: Stay informed about the critical SQL injection vulnerabilities affecting systems like TMT Lockcell and Zekiweb, as they could pose significant risks if exploited.
2. **Review Documentation and Training**: As an AI language model, continue to update its knowledge base on new technologies and security practices in order to provide the most current information.
Learning / Skill Upgrades:
1. **Expand Research Skills**: Dive deeper into NLP tokenisation algorithms and their impact on overall system functionality.
2. **Enhance Reverse Engineering Techniques**: Master using Ghidra to reverse engineer programs, as this is crucial for understanding security vulnerabilities in software applications.
Watchlist:
1. **Security Vulnerability Alerts**: Keep an eye on CVE-2023-3522 and other critical SQL injection issues.
2. **Tech Updates from OpenAI**: Stay informed about the latest advancements in AI language models like Codex and GPT-5.
3. **Software Security Practices**: Follow updates on new security patches for open source software, including TMT Lockcell and Camera Trap Tracking System vulnerabilities.
For more details: [BEGIN_UNTRUSTED_FEED_ITEMS]
Digest
Tech + AI + Science News
$200 'socketed' Nvidia AI GPU for servers hacked into a PCIe card with custom PCB and 3D-printed cooling — modded Tesla V100 SMX data center GPU runs AI LLMs and is more efficient than many modern midrange offerings in A fallback
Exclusive hardware roadmaps: Peer into the future of the hardware industry. ... Daily news analysis: Dive deep into the biggest stories. Subscribe to our annual plan for just $29 ... GPUs AMD announces MI350P PCIe AI accelerator card with 144GB of HBM3E — roughly 40% faster in FP16 and FP8 ...
GitHub recognized as a Leader in the Gartner® Magic Quadrant™ for Enterprise AI Coding Agents for the third year in a row new article
We are committed to empowering every developer by building an open, secure, and AI-powered platform that defines the future of software development. The post GitHub recognized as a Leader in the Gartner® Magic Quadrant™ for Enterprise AI Coding Agents for the third year in a row appeared first on The GitHub Blog .
Beyond the engine: 10 open source projects shaping how games actually get made article
Check out these 10 open source tools that help game developers create art, animation, levels, audio, dialogue, debug UIs, and engine-ready assets. The post Beyond the engine: 10 open source projects shaping how games actually get made appeared first on The GitHub Blog .
Investigating unauthorized access to GitHub-owned repositories new article
If any impact is discovered, customers will be notified via established incident response and notification channels. The post Investigating unauthorized access to GitHub-owned repositories appeared first on The GitHub Blog .
Forget electrons, this breakthrough uses light-matter particles to power AI new fallback
Researchers at Penn have created a hybrid light-matter particle that could dramatically speed up AI computing while using far less energy. The breakthrough may help replace some electronic computing processes with ultra-efficient light-based technology.
Think you’re bad at languages? Experts say these 5 myths are to blame new fallback
Many people avoid learning a new language because they remember stressful grammar lessons or fear making mistakes. But language experts say communication, culture, and connection matter far more than perfection. Modern apps, entertainment, travel, and online communities have made learning easier, more social, and surprisingly fun.
Research (arXiv)
Tokenisation via Convex Relaxations new fallbackTokenisation is an integral part of the current NLP pipeline. Current tokenisation algorithms such as BPE and Unigram are greedy algorithms -- they make locally optimal decisions without considering the resulting vocabulary as a whole. We instead formulate tokeniser construction as a linear program and solve it using convex optimisation tools, yielding a new algorithm we call ConvexTok. We find ConvexTok consistently improves intrinsic tokenisation metrics and the bits-per-byte (BpB) achieved by language models; it also improves downstream task performance, but less consistently. Furthermore, ConvexTok allows the user to certify how far their tokeniser is from optimal, with respect to a certain objective, via a lower bound, and we empirically find it to be within 1\% of optimal at common vocabulary sizes.
Bottom-up open EFT for non-Abelian gauge theory with dynamical color environment new fallbackWe develop a bottom-up open effective field theory (EFT) for non-Abelian gauge theories within the Schwinger--Keldysh formalism. Instead of integrating out the environment completely and starting from a nonlocal influence functional, we retain the slow environmental response variables explicitly and construct a local system-environment EFT. The environmental sector is described by a dynamical color-frame variable, Stückelberg-like field, and an associated color-current sector, which gives the nontrivial interactions and dissipation between the system and the environment. The resulting construction provides a gauge-covariant Markov embedding of nonlocal and non-Markovian color response. After integrating out the retained environmental variables with retarded boundary conditions, the reduced system theory acquires nonlocal dissipative kernels and stochastic sources. We show that the hard t
Which Way Did It Move? Diagnosing and Overcoming Directional Motion Blindness in Video-LLMs new fallbackVideo Large Language Models (Video-LLMs) have made rapid progress on temporal video understanding, yet many fail at a basic perceptual primitive: signed image-plane motion direction. On simple videos of a single object moving left, right, up, or down, most Video-LLMs perform near chance, with above-chance cases largely attributable to prediction biases rather than genuine direction understanding. We call this failure directional motion blindness. We localize the failure by tracing motion direction information through the Video-LLM pipeline. Motion direction remains linearly accessible from the vision encoder, projector, and LLM hidden states, but the readout fails to bind this signal to the correct verbal answer option, revealing a direction binding gap. Although synthetic motion direction instruction tuning reduces this gap on the source domain, motion direction concept vector analysis
Integrable Elasticity via Neural Demand Potentials new fallbackWe propose the Integrable Context-Dependent Demand Network (ICDN), a demand-first neural model for multiproduct retail demand. The model learns log-demand as a smooth, context-conditioned function of log-prices, allowing elasticities to be derived exactly from the learned demand surface. On the Dominick's beer dataset, ICDN improves out-of-sample generalization over a directed log-log benchmark and yields more stable, economically plausible elasticity estimates, especially for weakly identified cross-price effects.
Cambrian-P: Pose-Grounded Video Understanding new fallbackCamera pose matters. The position and orientation of each viewpoint define a shared spatial coordinate frame that relates observations across video frames. Yet this signal is largely absent from multimodal LLMs (MLLMs) for video understanding, which process frames as isolated 2D snapshots, instead of the persistent scene humans perceive. We revisit pose as a lightweight supervisory signal and introduce Cambrian-P, a video MLLM augmented with per-frame learnable camera tokens and a pose regression head. With a carefully designed sampling scheme, the model achieves substantial gains of 4.5-6.5% on spatial reasoning benchmarks such as VSI-Bench, generalizes across eight additional spatial and general video QA benchmarks, and, as a byproduct, achieves state of the art streaming pose estimation on ScanNet. Surprisingly, training on pseudo-annotated poses from in-the-wild video further improve
MotiMotion: Motion-Controlled Video Generation with Visual Reasoning new fallbackCurrent motion-controlled image-to-video generation models rigidly follow user-provided trajectories that are often sparse, imprecise, and causally incomplete. Such reliance often yields unnatural or implausible outcomes, especially by missing secondary causal consequences. To address this, we introduce MotiMotion, a novel framework that reformulates motion control as a reasoning-then-generation problem. To encourage causally grounded and commonsense-consistent interactions, we leverage a training-free vision-language reasoner to refine image-space coordinates of primary trajectories and to hallucinate plausible secondary motions. To further improve motion naturalness, we propose a confidence-aware control scheme that modulates guidance strength, enabling the model to closely follow high-confidence plans while correcting artifacts under low-confidence inputs with its internal generative
Vector Policy Optimization: Training for Diversity Improves Test-Time Search new fallbackLanguage models must now generalize out of the box to novel environments and work inside inference-scaling search procedures, such as AlphaEvolve, that select rollouts with a variety of task-specific reward functions. Unfortunately, the standard paradigm of LLM post-training optimizes a pre-specified scalar reward, often leading current LLMs to produce low-entropy response distributions and thus to struggle at displaying the diversity that inference-time search will require. We propose Vector Policy Optimization (VPO), an RL algorithm that explicitly trains policies to anticipate diverse downstream reward functions and to produce diverse solutions. VPO exploits that rewards are often vector-valued in practice, like per-test-case correctness in code generation or, say, multiple different user personas or reward models. VPO is essentially a drop-in replacement for the GRPO advantage estima
AwareVLN: Reasoning with Self-awareness for Vision-Language Navigation new fallbackVision-and-Language Navigation (VLN) requires an agent to ground language instructions to its own movement within a visual environment. While state-of-the-art methods leverage the reasoning capabilities of Vision-Language Models (VLMs) for end-to-end action prediction, they often lack an explicit and explainable understanding of the relationships between the agent, the instruction, and the scene. Conversely, explicitly building a scene map for heuristic planning is intuitively appealing but relies on additional 3D sensors and hinders large-scale vision-language pre-training. To bridge this gap, we propose AwareVLN, a novel framework that equips the navigation model with a self-aware reasoning mechanism, enabling it to understand the agent's state and task progress in a fully end-to-end and data-driven manner. Our approach features two key innovations: (1) a structural reasoning module th
Remember to be Curious: Episodic Context and Persistent Worlds for 3D Exploration new fallbackExploration is a prerequisite for learning useful behaviors in sparse-reward, long-horizon tasks, particularly within 3D environments. Curiosity-driven reinforcement learning addresses this via intrinsic rewards derived from the mismatch between the agent's predictive model of the world and reality. However, translating this intrinsic motivation to complex, photorealistic environments remains difficult, as agents can become trapped in local loops and receive fresh rewards for revisiting forgotten states. In this work, we demonstrate that this failure stems from a lack of spatial persistence and episodic context. We show that effective curiosity requires a model of the world that is persistent and continuously updated, paired with an agent that maintains an episodic trajectory history to navigate toward novel regions. We achieve this using an online 3D reconstruction as a persistent model
GesVLA: Gesture-Aware Vision-Language-Action Model Embedded Representations new fallbackVision-Language-Action (VLA) models have shown strong potential for general-purpose robot manipulation by unifying perception and action. However, existing VLA systems primarily rely on textual instructions and struggle to resolve spatial ambiguity in complex scenes with multiple similar objects. To address this limitation, we introduce gesture as a parallel instruction modality and propose a Gesture-aware Vision-Language-Action model (GesVLA). Our approach encodes gesture features directly into the latent space, enabling them to participate in both high-level reasoning and low-level action generation, and adopts a dual-VLM architecture to achieve tight coupling between gesture representations and action policies. At the data level, we construct a scalable gesture data generation pipeline by rendering hand models onto real-world scene images. This reduces the sim-to-real visual gap while
Sensor2Sensor: Cross-Embodiment Sensor Conversion for Autonomous Driving new fallbackRobust training and validation of Autonomous Driving Systems (ADS) require massive, diverse datasets. Proprietary data collected by Autonomous Vehicle (AV) fleets, while high-fidelity, are limited in scale, diversity of sensor configurations, as well as geographic and long-tail-behavioral coverage. In contrast, in-the-wild data from sources like dashcams offers immense scale and diversity, capturing critical long-tail scenarios and novel environments. However, this unstructured, in-the-wild video data is incompatible with ADS expecting structured, multi-modal sensor inputs for validation and training. To bridge this data gap, we propose Sensor2Sensor, a novel generative modeling paradigm that translates in-the-wild monocular dashcam videos into a high-fidelity, multi-modal sensor suite (AV logs) comprising multi-view camera images and LiDAR point clouds. A core challenge is the lack of p
How many systems can be dephased before the quantum switch becomes causally definite? new fallbackQuantum processes with indefinite causal order -- so-called causally nonseparable processes -- can exhibit various advantages over quantum circuits with a fixed or a well-defined causal structure. A natural question is how much nonclassicality is required for a process to display causal nonseparability. Here we address this by investigating how many systems can be dephased (or decohered) before this property vanishes. First, for bipartite processes with open past and future we show that if all systems are dephased, or if only the future system is kept undephased, then the process becomes causally separable. However, if any single system other than the future system remains undephased, then there exist processes that retain causal nonseparability. Next, we demonstrate a similar behaviour in the multipartite case, when restricted to the physically motivated class of quantum circuits with q
Security (NVD + CISA KEV)
CVE-2023-3377· CVSS 9.8 CRITICAL new fallbackImproper Neutralization of Special Elements used in an SQL Command ('SQL Injection') vulnerability in Veribilim Software Computer Veribase allows SQL Injection. This issue affects Veribase: through 20231123. NOTE: The vendor was contacted early about this disclosure but did not respond in any way.
Projects + Resources (Discovery)
w3school-python-jiaocheng new fallback
# W3School Python 教程 ## 下载 ### Docker ``` docker pull apachecn0/w3school-python-jiaocheng docker run -tid -p <port>:80 apachecn0/w3school-python-jiaocheng # 访问 http://localhost:{port} 查看文档 ``` ### PYPI ``` pip install w3school-python-jiaocheng w3school-python-jiaocheng <port> ...
Jiacheng (James) Zhang - Homepage fallbackHi there, I am Jiacheng (James) Zhang. I am an associate member of Sea AI Lab (SAIL), mentored by Dr. Tianyu Pang, and a Ph.D. candidate at Trustworthy Machine Learning and Reasoning (TMLR) group in the Faculty of Engineering and Information Technology, the University of Melbourne, advised by Dr.
Jiacheng Zheng | 郑嘉诚 new fallbackSupported the development of the Center for Data Analysis on Area and Country Studies, performed programming, data visualization, and laboratory system maintenance, primarily supporting research in Input-Output Analysis and Global Value Chains (GVCs), with extended applications to machine learning and natural language processing (NLP) for data-driven economic modeling.
Sources
news ok
News (RSS/Atom)
News sources (6/6 ok)
OpenAI News ok
Hacker News ok
GitHub Blog ok
ScienceDaily — Artificial Intelligence ok
ScienceDaily — Education & Learning ok
ScienceDaily — Stem Cells ok
arxiv ok
Research (arXiv)
nvd ok
Security (NVD)
kev ok
Security (CISA KEV)
brave ok
Discovery (Brave)
Archive
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