Search Results for « LAMP »

We report observations of the lunar helium exosphere made between December 29, 2011, and January 26, 2012, with the Lyman Alpha Mapping Project (LAMP) ultraviolet spectrograph on NASA's Lunar Reconnaissance Orbiter Mission (LRO). The observations were made of resonantly scattered He I 584 from illuminated atmosphere against the dark lunar surface on the dawn side of the terminator. We find no or little variation of the derived surface He density with latitude but day-to-day variations that likely reflect variations in the solar wind alpha flux. The 5-day passage of the Moon through the Earth's magnetotail results in a factor of two decrease in surface density, which is well explained by model simulations.

Video generation has achieved remarkable progress in visual fidelity and controllability, enabling conditioning on text, layout, or motion. Among these, motion control - specifying object dynamics and camera trajectories - is essential for composing complex, cinematic scenes, yet existing interfaces remain limited. We introduce LAMP that leverages large language models (LLMs) as motion planners to translate natural language descriptions into explicit 3D trajectories for dynamic objects and (relatively defined) cameras. LAMP defines a motion domain-specific language (DSL), inspired by cinematography conventions. By harnessing program synthesis capabilities of LLMs, LAMP generates structured motion programs from natural language, which are deterministically mapped to 3D trajectories. We construct a large-scale procedural dataset pairing natural text descriptions with corresponding motion programs and 3D trajectories. Experiments demonstrate LAMP's improved performance in motion controllability and alignment with user intent compared to state-of-the-art alternatives establishing the first framework for generating both object and camera motions directly from natural language specifications. Code, models and data are available on our project page.

This protocol describes how to perform colorimetric LAMP / RT-LAMP reactions with homemade buffers, together with home-brewed BstLF and MMLV enzymes. It is based on previous protocols of our lab (see LAMP/RT-LAMP Reaction protocol), but since two of the most common indicators used in colorimetric LAMP (phenol red and neutral red) require weakly buffered reactions, the buffer composition needed to be changed. This was done according to indications by Poole et al (2017). The colorimetric methods rely on the inherent proton production by DNA polymerases during the amplification process, which leads to a drop in the reaction pH that can be followed using these pH indicators. The results can be seen directly with the "naked eye", without the need for equipment or hands-on processing (Tanner et al., 2015).

Tracking 3D human motion from egocentric multi-camera headset is challenged by severe egomotion, partial visibility or occlusions and lack of training data. Existing methods designed for monocular video often require static or slowly-moving cameras and cannot efficiently leverage multi-view, calibrated and localized input. This makes them brittle and prone to fail on dynamic egocentric captures. We propose LAMP (Localization Aware Multi-camera People Tracking): a novel, simple framework to solve this via early disentanglement of observer and target motion. LAMP introduces a two-step process. First, we leverage the known device 6 DoF motion and calibration to convert detected 2D body keypoints from all cameras over a temporal window into a unified 3D world reference frame. Second, an end-to-end-trained spatio-temporal transformer fits 3D human motion directly to this 3D ray cloud. This "lift-then-fit" approach allows LAMP to learn and leverage a natural human motion prior in the world-space, as well as providing an elegant framework to flexibly incorporate information from multiple temporally asynchronous, partially observing and moving cameras. LAMP achieves state-of-the-art results on monocular benchmarks, while significantly outperforming baselines for our targeted egocentric setting.

Colorimetric Loop Amplification (LAMP) is a fast diagnostic tool which identifies presence of pathogenic RNA or DNA through a colour change. This method is not used widely because it is not as sensitive as real time PCR (lacks in sensitivity) and the negative control also becomes positive eventually (lacks in specificity). At Genomics WA, have developed a solution e-solution, which increases the sensitivity and specificity of colorimetric LAMP. Use of this solution keeps the negative control negative for a long time, thus enabling low copy numbers of pathogens to be detected. Our solution improves the sensitivity and specificity of colorimetric LAMP.

Recent advances in vision-language models have made zero-shot navigation feasible, enabling robots to follow natural language instructions without requiring labeling. However, existing methods that explicitly store language vectors in grid or node-based maps struggle to scale to large environments due to excessive memory requirements and limited resolution for fine-grained planning. We introduce LAMP (Language Map), a novel neural language field-based navigation framework that learns a continuous, language-driven map and directly leverages it for fine-grained path generation. Unlike prior approaches, our method encodes language features as an implicit neural field rather than storing them explicitly at every location. By combining this implicit representation with a sparse graph, LAMP supports efficient coarse path planning and then performs gradient-based optimization in the learned field to refine poses near the goal. This coarse-to-fine pipeline, language-driven, gradient-guided optimization is the first application of an implicit language map for precise path generation. This refinement is particularly effective at selecting goal regions not directly observed by leveraging semantic similarities in the learned feature space. To further enhance robustness, we adopt a Bayesian framework that models embedding uncertainty via the von Mises-Fisher distribution, thereby improving generalization to unobserved regions. To scale to large environments, LAMP employs a graph sampling strategy that prioritizes spatial coverage and embedding confidence, retaining only the most informative nodes and substantially reducing computational overhead. Our experimental results, both in NVIDIA Isaac Sim and on a real multi-floor building, demonstrate that LAMP outperforms existing explicit methods in both memory efficiency and fine-grained goal-reaching accuracy.

This protocol describes the preparation of reaction buffer for homemade LAMP / RT-LAMP reactions which involves the use of home-brewed BstLF and MMLV enzymes. It also contains the instructions to prepare the enzyme storage buffers. Home-brewed enzymes information: BstLF polymerase: expression and purification protocol. expression plasmid sequence. M-MLV reverse transcriptase: expression and purification protocol. expression plasmid sequence. Testing We test the buffers on SARS-CoV-2 LAMP or RT-LAMP reactions with synthetic dsDNA or ssRNA respectively. The general performance of Homemade buffer seems appropriate and no amplification was obtained for non-template controls (NTC). To properly formulate the buffer we tested different KCl concentrations which is known to be a critical factor in the buffer. For the home-brewed BstLF, we found that 50 mM KCl is the best concentration, displaying equal or better performance than the commercial buffer. No amplification was observed for 150 mM KCl formulation. [1] Zhang, Y. et al. Enhancing colorimetric loop-mediated isothermal amplification speed and sensitivity with guanidine chloride. Biotechniques69, (2020).

With the impressive progress in diffusion-based text-to-image generation, extending such powerful generative ability to text-to-video raises enormous attention. Existing methods either require large-scale text-video pairs and a large number of training resources or learn motions that are precisely aligned with template videos. It is non-trivial to balance a trade-off between the degree of generation freedom and the resource costs for video generation. In our study, we present a few-shot-based tuning framework, LAMP, which enables text-to-image diffusion model Learn A specific Motion Pattern with 8~16 videos on a single GPU. Specifically, we design a first-frame-conditioned pipeline that uses an off-the-shelf text-to-image model for content generation so that our tuned video diffusion model mainly focuses on motion learning. The well-developed text-to-image techniques can provide visually pleasing and diverse content as generation conditions, which highly improves video quality and generation freedom. To capture the features of temporal dimension, we expand the pretrained 2D convolution layers of the T2I model to our novel temporal-spatial motion learning layers and modify the attention blocks to the temporal level. Additionally, we develop an effective inference trick, shared-noise sampling, which can improve the stability of videos with computational costs. Our method can also be flexibly applied to other tasks, e.g. real-world image animation and video editing. Extensive experiments demonstrate that LAMP can effectively learn the motion pattern on limited data and generate high-quality videos. The code and models are available at https://rq-wu.github.io/projects/LAMP.

This protocol describes how to perform LAMP / RT-LAMP reactions with home-made reagents, which means home-made buffers and positive controls, together with home-brewed BstLF and MMLV enzymes. Home-brewed enzymes information: BstLF polymerase: expression and purification protocol. expression plasmid sequence. M-MLV reverse transcriptase: expression and purification protocol. expression plasmid sequence. Positive control information Sample preparation protocol here. Testing We test this protocol in LAMP or RT-LAMP reactions with SARS-CoV-2 synthetic dsDNA or ssRNA respectively. As shown in Figure 1, the general performance of reactions with homemade reagents seems appropriate compared with the commercial standard option. No amplification was observed in non-template controls (NTC). [1] Zhang, Y. et al. Enhancing colorimetric loop-mediated isothermal amplification speed and sensitivity with guanidine chloride. Biotechniques69, (2020).