arXiv subjects

Emily Guo

Publications and source records attributed to Emily Guo.

4 recordsLinked to original sources

arXiv2026

ShapeTalk: Combining Natural Language and Sketch for Time-Series Pattern Querying

Searching for time-series segments that match user-defined patterns is important in domains such as finance, climate science, and healthcare. However, existing visual query tools often struggle to support vague, composite, or fuzzy pattern descriptions, often requiring users to express their intent through precise sketches or rigid structured filters. We present ShapeTalk, a coordinated natural-language and sketch-based querying system for univariate time-series pattern search. Rather than treating text and sketch as a fused input stream, ShapeTalk uses them as complementary representations of analytic intent: natural language supports semantic and compositional pattern descriptions, while sketching supports direct geometric refinement. The two modalities are linked through a shared visual context, editable feature representations, and synchronized result views, enabling users to move between text and sketch during iterative query formulation. At its core is an LLM-based semantic parsing pipeline that translates free-form natural-language queries into interpretable and editable shape-feature constraints. We evaluate ShapeTalk through two usage scenarios, a user study with failure-case analysis, and an assessment of the LLM-based semantic parsing pipeline. The results show that ShapeTalk supports effective time-series pattern search, with natural language serving as an accessible entry point and sketching providing a complementary mechanism for refinement and recovery when textual specifications are insufficient.

cs.HC↗

arXiv2025

Efficient Speculative Decoding for Llama at Scale: Challenges and Solutions

Speculative decoding is a standard method for accelerating the inference speed of large language models. However, scaling it for production environments poses several engineering challenges, including efficiently implementing different operations (e.g., tree attention and multi-round speculative decoding) on GPU. In this paper, we detail the training and inference optimization techniques that we have implemented to enable EAGLE-based speculative decoding at a production scale for Llama models. With these changes, we achieve a new state-of-the-art inference latency for Llama models. For example, Llama4 Maverick decodes at a speed of about 4 ms per token (with a batch size of one) on 8 NVIDIA H100 GPUs, which is 10% faster than the previously best known method. Furthermore, for EAGLE-based speculative decoding, our optimizations enable us to achieve a speed-up for large batch sizes between 1.4x and 2.0x at production scale.

cs.CL↗

arXiv2024

Elastic Modulus Versus Cell Packing Density in MDCK Epithelial Monolayers

The elastic moduli of tissues are connected to their states of health and function. The epithelial monolayer is a simple, minimal, tissue model that is often used to gain understanding of mechanical behavior at the cellular or multi-cellular scale. Here we investigate how the elastic modulus of Madin Darby Canine Kidney (MDCK) cells depends on their packing density. Rather than measuring elasticity at the sub-cellular scale with local probes, we characterize the monolayer at the multi-cellular scale, as one would a thin slab of elastic material. We use a micro-indentation system to apply gentle forces to the apical side of MDCK monolayers, applying a normal force to approximately 100 cells in each experiment. In low-density confluent monolayers, we find that the elastic modulus decreases with increasing cell density. At high densities, the modulus appears to plateau. This finding will help guide our understanding of known collective behaviors in epithelial monolayers and other tissues where variations in cell packing density are correlated with cell motion.

cond-mat.soft↗

arXiv2024

Transitions Between Cooperative and Crowding-Dominated Collective Motion in non-Jammed MDCK Monolayers

Transitions between solid-like and fluid-like states in living tissues have been found in steps of embryonic development and in stages of disease progression. Our current understanding of these transitions has been guided by experimental and theoretical investigations focused on how motion becomes arrested with increased mechanical coupling between cells, typically as a function of packing density or cell cohesiveness. However, cells actively respond to externally applied forces by contracting after a time delay, so it is possible that at some packing densities or levels of cell cohesiveness, mechanical coupling stimulates cell motion instead of suppressing it. Here we report our findings that at low densities and within multiple ranges of cell cohesiveness, cell migration speeds increase with these measures of mechanical coupling. Our observations run counter to our intuition that cell motion will be suppressed by increasingly packing or sticking cells together and may provide new insight into biological processes involving motion in dense cell populations.

cond-mat.soft↗