LAPIS: Diverse Research Applications

• LAPIS is a polysemous term that refers to various systems including a genomic API for outbreak surveillance, semiconductor detectors, art datasets, legal NLP systems, compiler frameworks, and Laplace importance sampling.
• It enables high-performance data querying with an in-memory, column-wise design for rapid genomic analyses and optimized processing in detector and compiler technologies.
• LAPIS also encompasses materials research on lapis lazuli, offering insights into its compositional analysis and cultural significance alongside its computational applications.

LAPIS is not a single technical object but a recurrent name and acronym used across several distinct research domains. In arXiv literature it denotes, among other things, a pathogen-genomics web service, a semiconductor process and detector platform, an art-focused dataset for personalized image aesthetic assessment, language-model-centered reasoning and interface systems, a performance-portable compiler framework, and, in the capitalization variant LapIS, Laplace importance sampling. The term also appears in materials research through studies of lapis lazuli as a geological and cultural-heritage material. The usages are therefore polysemous rather than historically continuous.

1. Principal research meanings

Across the supplied literature, LAPIS functions as a domain-specific label whose expansion depends entirely on context.

Expansion or referent	Domain	Representative paper
Lightweight API for Sequences	Genomic epidemiology, outbreak data services	(Chen et al., 2022)
LAPIS Semiconductor / LAPIS 0.2 µm FD-SOI process	SOI pixel detectors, X-ray and particle sensors	(Okihara et al., 2015)
Leuven Art Personalized Image Set	Personalized image aesthetic assessment	(Maerten et al., 10 Apr 2025)
LLM-Augmented Police Investigation System	Legal reasoning for police investigations	(Kim et al., 2024)
Lightweight API Specification for Intelligent Systems	LLM-facing API description	(Garcia, 20 Feb 2026)
MLIR-based compiler framework	Performance-portable scientific and ML compilation	(Kelley et al., 30 Sep 2025)
LAPIS-SHRED	Sparse spatiotemporal reconstruction and forecasting	(Bao et al., 1 Apr 2026)

A separate capitalization, LapIS, denotes Laplace importance sampling in work on randomized quasi-Monte Carlo (Zheng et al., 2023). Outside acronymic usage, lapis also appears in studies of lapis lazuli, where the term refers to the stone itself rather than to a computational framework (Giudice et al., 2016).

2. Outbreak genomics and pathogen surveillance

In genomic epidemiology, LAPIS stands for Lightweight API for Sequences, described as a fast REST API for very large open virus sequencing databases (Chen et al., 2022). It was developed in response to the data-access bottleneck exposed by the SARS-CoV-2 pandemic and the 2022 mpox outbreak, where laboratories generated millions of pathogen genomes but standard repositories and general-purpose systems were not optimized for recurrent surveillance questions such as filtering by date, country, lineage, amino-acid or nucleotide changes, stratifying counts by geography or time, and downloading cleaned aligned sequences.

The API distinguishes aggregated endpoints from per-sample/data-download endpoints. The principal aggregation endpoint, aggregated, returns either a total count or counts stratified by specified metadata fields; dedicated mutation-distribution endpoints include nuc-mutations, aa-mutations, nuc-insertions, and aa-insertions. Non-aggregated retrieval is provided by details, nuc-sequence, nuc-sequence-aligned, and aa-sequence-aligned/{gene}. LAPIS supports exact metadata filters, interval filters such as dateFrom and dateTo, lineage wildcards such as XBB.1*, insertion wildcards such as ins:1000:AAT?, advanced Boolean and threshold-style variant definitions such as [2-of: 100A, 101T, 102G], and the special maybe(...) semantics for ambiguity-aware upper-bound queries.

A notable semantic detail concerns mutation proportions: ambiguous or unknown bases are excluded from the denominator. In the paper’s example, if 10 sequences contain 3 with A5G, 3 with reference A, and 4 with N at position 5, the reported proportion is

$\frac{3}{6}=0.5$

rather than $\frac{3}{10}=0.3$. This design reflects the system’s emphasis on outbreak-oriented interpretation rather than on generic database behavior.

Implementation is centered on a new in-memory database engine. The storage model is column-wise: for each aligned genomic or amino-acid position, LAPIS stores a string whose $i$-th character corresponds to sequence $i$. This allows positional mutation queries to inspect only relevant columns rather than full sequences. Insertions are handled separately through an insertion store that maps inserted sequence values at a reference position to the set of sequence IDs carrying them. Query-time evaluation occurs in memory; PostgreSQL is used only as background storage. Operational optimizations include Zstd level 3 compression, Redis caching tied to data versions, and explicit version reporting through the LAPIS-Data-Version HTTP header and a dataVersion JSON field.

The production figures reported for the private SARS-CoV-2 deployment are unusually large for an interactive API. As of January 2023 the instance contained more than 14.5 million SARS-CoV-2 sequences and exceeded 400 GB. Between 25 January and 4 February 2023 it processed over 20 million requests with mean response time 411 ms, median response time 1 ms, 72% of responses served from cache, and 83% of requests completed within 500 ms. It is the backend for genspectrum.org, including CoV-Spectrum and the mpox dashboard, and the paper explicitly suggests that infrastructures such as GenBank could integrate LAPIS-like API functionality rather than forcing secondary systems to duplicate storage (Chen et al., 2022).

3. SOI process technology and monolithic pixel detectors

In detector electronics, LAPIS refers to LAPIS Semiconductor Co., Ltd. and related process technology for 0.2 µm fully-depleted SOI monolithic pixel detectors (Okihara et al., 2015). The core device concept is a monolithic SOI detector in which the readout/electrical circuitry is fabricated in the top silicon layer, while the X-ray sensing p–n junction diode is formed in the high-resistivity handle wafer. This supports a single-substrate architecture with a depleted sensing volume beneath integrated CMOS.

The process literature emphasizes several detector-specific innovations. A buried P-well (BPW) biased with GND suppresses back-gate effects; the paper reports that with BPW there is “There is no Id-Vg change even with Vback = 100 V” (Okihara et al., 2015). A double-SOI wafer introduces a middle-SOI layer beneath the transistors to compensate BOX charging effects and reduce crosstalk; with negative middle-SOI bias, IV characteristics become nearly pre-irradiation even after exposure up to 10 Mrad. For high-resistivity FZ-SOI, slow ramp-up and ramp-down thermal recipes reduce slip generation to an “acceptance level for pixel sensor fabrication”. Backside processing with wet etching after back grinding and laser anneal removes a 0.3 µm-thick damaged layer and suppresses abnormal leakage current under full depletion. For large-area imagers, stitching exposure yields chips of about 66 mm width and 30 mm height, with 14 µm boundary area, pattern thinning less than 0.1 µm, and about 0.015 µm Y-direction alignment.

A related study analyzes shielding mechanisms for LAPIS-fabricated SOI pixel detectors in counting-type pixels (Lu et al., 2015). The problem is circuit-to-sensor charge injection and crosstalk. With a coupling capacitance of

$0.2~\mathrm{fF}/\mu\mathrm{m}^2$

a $1~\mathrm{V}$ step on a $1~\mu\mathrm{m}^2$ area induces about $0.2~\mathrm{fC}$, or roughly 1200 $e^-$, equivalent to about a 4.3 keV X-ray quantum. Prototype chips CPIXTEG3 and CPIXTEG3b compared Nested-well and Double-SOI shielding. The paper concludes that a shielding layer is indispensable for counting-type pixels and that Double-SOI is superior to Nested-well in terms of shielding effectiveness and design flexibility; in the Nested-well implementation the counter produced spurious signals corresponding to about 20 ke$^{-}$, whereas in the Double-SOI version the authors report no discernable crosstalk correlated to any clock edge.

The same foundry platform underlies integration-type sensors such as INTPIX4 (Mitsui et al., 2018). That detector uses the 0.2 um low-leakage fully depleted (FD)-SOI CMOS process of Lapis Semiconductor Co., Ltd., with 1 poly, 5 metal layers, MIM capacitor and DMOS options, Core / I/O voltages of 1.8 V / 3.3 V, and 8-inch SOI wafers. INTPIX4 has 512 × 832 pixels, 17 × 17 µm$\frac{3}{10}=0.3$0 pitch, 13 parallel output lines, and per-pixel correlated double sampling (CDS). The transition from Cz to FZ substrates after 2011 reduced leakage current and enabled a thicker sensing depth at moderate bias; reported FZ performance includes detector gain 13.1 µV/e, FWHM = 624 electrons, and 12.8% at 17.48 keV.

A later LAPIS-based prototype for collider tracking is CPV3, fabricated in the LAPIS 200 nm SOI process and focused on a pinned depleted diode (PDD) with AC-coupling on the diode (Dong et al., 2021). The design targets very small pixels, 16 × 20 µm$\frac{3}{10}=0.3$1, with about $\frac{3}{10}=0.3$2 backside bias on a 310 µm sensor. The study reports very low leakage current, $\frac{3}{10}=0.3$3 charge-voltage factor, raw equivalent input capacitance around 12 fF for one structure, and about 3.5 fF of equivalent input capacitance for the optimized in-pixel PDD capacitance. Among the three PDD variants, PDD3 showed the lowest equivalent diode capacitance and better charge sharing. Taken together, these papers situate LAPIS as a detector-oriented SOI process line spanning X-ray imaging, counting pixels, and collider-grade monolithic sensor R&D (Okihara et al., 2015).

4. Machine-learning datasets and latent spatiotemporal inference

In computational aesthetics, LAPIS denotes the Leuven Art Personalized Image Set, introduced as a dataset for personalized image aesthetic assessment (PIAA) and described as the first dataset with images of artworks that is suitable for PIAA (Maerten et al., 10 Apr 2025). It contains 11,723 images sourced from WikiArt, mostly paintings with some sketches, spanning 26 styles and 7 genres. The dataset includes 7,976 figurative works and 3,747 abstract works. Annotation was conducted in an online study with 552 participants recruited through Prolific; after quality control, each image had ratings from an average of 24 annotators. LAPIS provides both per-image aesthetic ratings and rich side information: personal attributes such as age, nationality, gender, education level, colorblindness, art interest, and art familiarity, plus image attributes including style, genre, and 31 image attributes expanding to 47 image features.

The benchmarking results are notable because they expose a large protocol gap. Under a traditional train/test split, PIAA-MIR and PIAA-ICI achieve SROCC 0.6958 and 0.6941 on LAPIS, but under the stricter 4-fold cross-validation with disjoint train/test users they drop to $\frac{3}{10}=0.3$4 and $\frac{3}{10}=0.3$5. The ablation study shows strong sensitivity to Art interest and to Style and genre, while removing Objective image attributes slightly improves the baseline SROCC from 0.69583 to 0.70118. The paper interprets this as evidence that current PIAA models do not generalize well to unseen users and that artistic image aesthetics remain difficult, especially for abstract or less-preferred genres.

In a different machine-learning setting, LAPIS-SHRED stands for LAtent Phase Inference from Short time sequences using SHallow REcurrent Decoders (Bao et al., 1 Apr 2026). It addresses reconstruction and forecasting of complete spatiotemporal dynamics from hyper-sparse sensor observations confined to short temporal windows. The framework uses a three-stage pipeline: a SHRED model is pre-trained on simulation data to map sensor time-histories into a latent space; a temporal sequence model is trained on simulation-derived latent trajectories to propagate latent states forward or backward; and, at deployment, a short real observation window is encoded and expanded into a full latent trajectory before decoding back to the physical field. The architecture supports bidirectional inference, multiscale backbones, and even single-frame terminal inputs through static padding.

The empirical scope is broad: the paper reports six experiments spanning turbulent flows, multiscale propulsion physics, volatile combustion transients, and satellite-derived environmental fields. The summary table gives NRMSE 0.046 for 2D KS, 0.044 on velocity and 0.036 on vorticity for 2D KF, 0.037 forward and 0.033 backward for 2D KVS, 0.114 for HF-RDE, 0.025 on pressure and 0.032 on temperature for 1D RDE ignition, and 0.167 forward and 0.130 backward for NDSI. The paper states that full trajectories can be reconstructed from windows spanning only $\frac{3}{10}=0.3$6 of the temporal domain using as few as 3 sensors, often with NRMSE below 5%. This suggests a conceptually distinct but similarly named LAPIS: a latent-trajectory inference layer built on top of SHRED rather than a dataset (Bao et al., 1 Apr 2026).

5. Language-model and API-oriented systems

In legal NLP, LAPIS expands to LLM-Augmented Police Investigation System, an AI decision-support system for police officers conducting criminal investigations in Korean (Kim et al., 2024). The system formalizes a crime-investigation legal reasoning task in which, given a hypothesis $\frac{3}{10}=0.3$7 and investigative context $\frac{3}{10}=0.3$8, an evaluator model returns both a binary assessment and a rationale. Retrieval is integrated through a Crime Investigation KnowledgeBase (CIKB), and deployment follows the pattern

$\frac{3}{10}=0.3$9

The dataset, Crime Investigation Legal Reasoning (CIRL), was built from the Korean National Police Agency’s annual Criminal Law Proficiency Exam from 2013 to 2023. The authors collected 1,760 original exam questions and converted them into 4,895 hypothesis instances, then expanded the rationale-bearing training set to 14,105 instances with GPT-4 generation and domain-expert curation by 20 law enforcement officials affiliated with the Korean National Police Agency.

CIKB contains 19,388 dense embeddings, derived from 687 paragraphs of criminal investigation textbooks, 3,304 paragraphs of criminal law articles, and 15,397 paragraphs of criminal court rulings. At inference time the system retrieves the top 5 relevant premises. The best evaluator is EEVE-Korean-Instruct-10.8B-v1.0, fine-tuned for 5 epochs with Paged AdamW, learning rate 0.0001, and QLoRA on two NVIDIA A100 80GB Tensor Core GPUs for approximately 30 hours. On the held-out test set, the reported total performance is ACC 0.74, F1 0.79, slightly above GPT-4’s 0.73 / 0.79 overall, with especially strong gains on the Crime Investigation category. The paper’s qualitative analysis emphasizes that LAPIS grounds rationales in premises and may avoid failures such as GPT-4 citing a non-existent precedent.

A different LLM-centered use of the name is LAPIS: Lightweight API Specification for Intelligent Systems (Garcia, 20 Feb 2026). Here LAPIS is a domain-specific specification format designed for LLM consumption, motivated by the claim that OpenAPI is the wrong optimization target for LLMs. The document structure has up to seven fixed-order sections: [meta], [types], [ops], [webhooks], [errors], [limits], and [flows], with [meta] and [ops] required. The design centralizes repeated information, represents operations as signature-like entries, gives first-class structure to webhook triggers and rate limits, and allows operation-flow declarations that OpenAPI either duplicates redundantly or cannot represent structurally.

The token-efficiency claims are substantial. Against five production API specifications—GitHub (1,080 endpoints), DigitalOcean (545 endpoints), Twilio (197 endpoints), HTTPBin, and Petstore—the paper reports an average reduction of 85.5% versus OpenAPI YAML and 88.6% versus OpenAPI JSON using the cl100k_base tokenizer. For GitHub, the count falls from 1,811,843 YAML tokens to 313,101 LAPIS tokens; for DigitalOcean, from 586,731 to 54,201; and for Twilio, from 306,453 to 24,197. The paper also highlights structural waste in OpenAPI, noting that GitHub’s spec contains 1,594 error definitions across 1,080 operations but only 14 unique error codes, with 404 repeated 531 times. In this meaning, LAPIS is a reasoning-optimized projection of OpenAPI rather than an operational API service (Garcia, 20 Feb 2026).

6. Compiler infrastructure and other scientific usages

In programming languages and systems, LAPIS is also the name of an MLIR-based compiler framework for scientific computing and machine learning (Kelley et al., 30 Sep 2025). The stated goal is simultaneous performance, portability, and productivity, particularly where computational science and machine learning workflows converge. The compiler lowers linalg-on-tensors MLIR through a custom Kokkos dialect and emits standalone Kokkos C++. It introduces lowering passes such as linalg-to-kokkoskernels, dense-linalg-to-parallel-loops, kokkos-loop-mapping, and kokkos-dualview-management, and supports both dense and sparse linear algebra as well as interoperability between PyTorch and Kokkos.

The dense GEMM benchmark is numerically explicit. For 4096 × 4096 FP32 SGEMM, LAPIS and KokkosKernels are effectively identical: 1.83 ms versus 1.86 ms on Granite Rapids, 0.359 ms versus 0.358 ms on H100, and 0.264 ms versus 0.263 ms on MI300A. For SpMV, the paper reports performance similar to handwritten Kokkos Kernels, somewhat slower than cuSPARSE, similar to MKL on Granite Rapids, and similar to rocSPARSE on MI300A. It also demonstrates model integration for ResNet18 and a SciML surrogate in MALA, with MALA inference for 8748 atoms taking about 0.5 ms. In this usage, LAPIS is a compiler and runtime-emission framework rather than an API or dataset.

A mathematically separate capitalization, LapIS, refers to Laplace importance sampling in work on importance sampling combined with randomized quasi-Monte Carlo (Zheng et al., 2023). The proposal is Gaussian with mean at the mode of the log-target and covariance given by the inverse negative Hessian:

$i$0

Under a randomly shifted rank-1 lattice rule and suitable weighted-RKHS conditions, the paper proves the RMSE bound

$i$1

For LapIS, sufficient conditions include both the growth condition

$i$2

and the additional eigenvalue condition

$i$3

The paper explicitly remarks that the convergence conditions for LapIS are more strict than for ODIS, so the term here denotes a specific importance density rather than a standalone system.

Outside acronymic usage, lapis remains a materials-science and heritage-science term through lapis lazuli. A provenance study based on CL, SEM-EDS, micro-Raman, IL, PIXE, and PIGE proposes source criteria for material from Afghanistan, Pamir Mountains in Tajikistan, Chile, and Siberia (Giudice et al., 2016). Chilean samples are associated with wollastonite and strong bands at ~560 nm and ~620 nm; Pamir material with a ~690 nm diopside band and cancrinite-group UV luminescence; Siberian material with Ba > 1% and Sr ~6000 ppm. A later colorimetric microscopy study treats lapis lazuli as a heterogeneous optical material composed primarily of blue lazurite, yellow pyrite, and white calcite (Jany, 2023). For the polished lapis sample, the reported mean dominant wavelength is

$i$4

with standard deviation 12 nm, and the mean excitation purity is

$i$5

with standard deviation 0.069. These non-acronymic usages underscore that LAPIS can signify either an institutional or algorithmic acronym or the mineral term itself, depending on disciplinary context.