AI-Driven Data Lake Optimization: Integrating Quality Monitoring with Intelligent Physical Design Decisions

Cloud data lakes require continuous optimization across multiple dimensions: physical design (partitioning, compression), query execution, and data quality assurance. This paper presents AIDALOS (AI-Driven Autonomous Data Lake Optimization System), a framework that integrates quality monitoring with physical optimization decisions. The system uses reinforcement learning to adapt monitoring intensity and trigger physical design changes based on detected anomalies, drift patterns, and workload shifts. Deep Q-networks learn when to repartition tables, ensemble models select compression codecs based on data characteristics and access patterns, and neural cost estimators improve query plan selection. Our evaluation across five machine learning pipelines demonstrates that this integrated approach achieves 47% storage cost reduction and 62% query performance improvement compared to static configurations, with 89.9% F1-score for quality issue detection. The key insight is that quality signals drift detection, anomaly patterns, and workload changes should directly inform physical optimization decisions rather than treating these as separate concerns.https://www.jenrs.com/v05/i03/p001

Universal Bright-Bright Integrated Soliton Molecule via Parametric Binding

Dissipative Kerr solitons (DKSs) have emerged as the preferred solution for on-chip integrated optical frequency comb (OFC) generation in metrology. A multi-pumped DKS enables either all-optical trapping in the Kerr-induced synchronization regime, or a multi-component OFC with a locked repetition rate yet with constant frequency offsets between the components in the multi-color DKS regime. The multi-color DKS regime is of particular interest since nonlinear mixing between the DKS and the secondary pumped component generates idler waves at different frequencies that are useful for spectral extension of the DKS comb. Here, we explore multi-color idler generation at frequencies in which the resonator free spectral range matches that at the DKS. We demonstrate theoretically and experimentally that without phase matching, the idler forms a bright pulse fundamentally bound to the bright DKS through parametric interaction, despite occurring in normal dispersion. Our work can enable new applications in metrology and spectroscopy of quantum systems toward visible wavelengths, as the parametric nature of our bright-bright state eliminates dependence on dispersion regime or visible wavelength pumping.P.S., A.N, G.C, and C.M. acknowledge the support from the Air Force Office of Scientific Research (Grant No. FA9550-20-1-0357), a collaborative agreement with the National Center for Manufacturing Sciences (contract nos. 2022138-142232 and 2023200-142386) as sub-awards from the US Department of Defense (cooperative agreement nos. HQ0034-20-2-0007 and HQ0034-24-2-0001). P.S. and C.M. acknowledge the support from NIST (grant no. 60NANB24D106). A.N and G.C acknowledge the support from the Army Research Office (contract no. W911NF- 22-S-0010). S.O, K.S., and G.M. acknowledge the partial funding support from the Space Vehicles Directorate of the Air Force Research Laboratory and the NIST-on-a-chip program of the National Institute of Standards and Technologyhttp://arxiv.org/abs/2602.0671

Genome-Wide Assessment Reveals Ancestral Differences in Homozygosity Patterns Potentially Linked to Parkinson's Disease Etiology

Background Recessive genetic variation and extended runs of homozygosity (ROHs) may contribute to the unexplained heritability of Parkinson's disease (PD), particularly in diverse and understudied populations. Objective We conducted the first large-scale, multi-ancestral investigation of PD to examine the impact of genome-wide homozygosity on disease risk and age at onset (AAO). Using genotyping, imputed, and whole-genome sequencing data from 36,127 PD cases and 19,475 controls across nine ancestral populations from the Global Parkinson's Genetics Program, we aimed to identify novel regions of homozygosity contributing to PD heritability. Methods We analyzed ROHs for total length (SROH), number (NROH), average length (AVROH), and genomic inbreeding coefficient (FROH). ROHs were intersected with known PD, pallido-pyramidal syndrome, and atypical parkinsonism gene regions and risk loci to assess pleomorphic or pleiotropic contributions. Homozygosity mapping identified ROH overlaps in families, consanguineous individuals, and early-onset PD (EOPD) cases. Results Significant differences in SROH, AVROH, NROH, and FROH were observed between case status across ancestries, persisting after excluding known PD-associated recessive genes. Our analysis revealed distinct patterns of ROH enrichment associated with AAO, suggesting recessive genetic modifiers of PD. Homozygosity mapping was used to prioritize 52 variants either segregating in families or present in individuals with consanguinity. In total, 1,559 ROHs in consanguineous individuals and EOPD overlapped known PD gene regions and risk loci. Conclusions ROH regions contribute to PD heritability across ancestries, partly reflecting recessive genetic architecture. Larger and more diverse whole-genome sequencing studies are needed to identify rare recessive variants influencing PD risk. © 2026 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.This research was supported in part by the Intra-mural Research Program of the NIH, National Institute on Aging (NIA),National Institutes of Health, Department of Health and Human Services;project numbers ZO1 AG000535 and ZIA AG000949, as well as theNational Institute of Neurological Disorders and Stroke (NINDS, pro-gram # ZIANS003154) and the National Human Genome ResearchInstitute (NHGRI). Additional funding was provided by The MichaelJ. Fox Foundation for Parkinson’s Research through grant MJFF-009421/17483.https://onlinelibrary.wiley.com/doi/abs/10.1002/mds.7018

Transcriptome data sets of Piscinibacter sakaiensis grown in 702 complex medium, maltose, and PET plastic

Piscinibacter sakaiensis 201-F6 is a gram-negative bacterium capable of metabolizing and fermenting poly(ethylene) terephthalate (PET) plastics and simple sugars. Here, we announce three transcriptome data sets of Piscinibacter sakaiensis grown in 702 complex medium, maltose, and PET plasticThis work is supported by National Institute of General Medical Sciences. Grants(s) #R01GM147142https://journals.asm.org/doi/10.1128/mra.01466-2

Two-Stage Multiple Test Procedures Controlling False Discovery Rate with auxiliary variable and their Application to Set4Delta Mutant Data

In this paper, we present novel methodologies that incorporate auxiliary variables for multiple hypotheses testing related to the main point of interest while effectively controlling the false discovery rate. When dealing with multiple tests concerning the primary variable of interest, researchers can use auxiliary variables to set preconditions for the significance of primary variables, thereby enhancing test efficacy. Depending on the auxiliary variable's role, we propose two approaches: one terminates testing of the primary variable if it does not meet predefined conditions, and the other adjusts the evaluation criteria based on the auxiliary variable. Employing the copula method, we elucidate the dependence between the auxiliary and primary variables by deriving their joint distribution from individual marginal distributions.Our numerical studies, compared with existing methods, demonstrate that the proposed methodologies effectively control the FDR and yield greater statistical power than previous approaches solely based on the primary variable. As an illustrative example, we apply our methods to the Set 4Δ mutant dataset. Our findings highlight the distinctions between our methodologies and traditional approaches, emphasising the potential advantages of our methods in introducing the auxiliary variable for selecting more genes.This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1A2C1A01100526).http://arxiv.org/abs/2602.1827

Harnessing resonant noise emission to measure cavity-pump detuning for microcomb stabilization

LASE, San Francisco, California, United States, 4 March 2026We demonstrate an in situ detection technique to track the effective cavity-pump detuning without any active perturbation of the pump laser that is generating soliton microcombs in the cavity. The technique utilizes a narrow band notch filter to isolate the pump from the cavity output, a commonly used technique to avoid the saturation of the photodetector in the detection circuitry of the microcomb repetition frequency, and measures the effective detuning by detecting the peak of the resonant profile, corresponding to the hot cavity resonance, present at the isolated pump. The technique is applicable to determine the effective detuning regardless of the existence of soliton states in the cavity.The authors thank Drs. Tobias J. Kippenberg and Xurong Li for the microresonator device used in this demonstration. Work at UMBC was supported by collaborative agreements 2022138-142232 and 2023200-142386 with the National Center for Manufacturing Sciences as sub-awards from US DoD cooperative agreements HQ0034-20-2- 0007 and HQ0034-24-2-0001https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13879/1387906/Harnessing-resonant-noise-emission-to-measure-cavity-pump-detuning-for/10.1117/12.3080763.ful

Model-Free Adaptive Output Feedback Vibration Suppression in a Cantilever Beam

AIAA SCITECH 2026 Forum, 12-16 January 2026, Orlando, FLThis paper presents a model-free adaptive control approach to suppress vibrations in a cantilevered beam excited by an unknown disturbance. The cantilevered beam under harmonic excitation is modeled using a lumped parameter approach. Based on retrospective cost optimization, a sampled-data adaptive controller is developed to suppress vibrations caused by external disturbances. Both displacement and acceleration measurements are considered for feedback. Since acceleration measurements are more sensitive to spillover, which excites higher frequency modes, a filter is developed to extract key displacement information from the acceleration data and enhance suppression performance. The vibration suppression performance is compared using both displacement and acceleration measurements.https://arc.aiaa.org/doi/10.2514/6.2026-184

Gino Unfiltered

Gino Renne is the longtime president of UFCW 1994 MCGEO, the county government employees union. Sunil Dasgupta talks with Gino Renne about the county's politics, history, the role of unions, and the challenges of economic development and rent stabilization. Music by Seth Kibel.https://open.spotify.com/episode/1mXvbVkT9KYlspajiw4cY

Potomac Sewage Spill, MD Budget, AI in Classrooms

The snow emergency has captured attention; hundreds of millions of raw sewage has spilled into the Potomac River from a pipe break under the American Legion Bridge, which joins Maryland and Virginia. DC Water, which runs the pipe, is working on it, but no local jurisdiction has issued a health advisory. Maryland Governor Wes Moore presented the last budget of his first term. We have takeaways. Montgomery County Board of Education has been considering a written policy on the use of AI in classrooms. We break it down. And more. Newly in public domain music by George Gershwin, Paul Whiteman band, and Marian Andersen.https://open.spotify.com/episode/3m4bOiCXjeS0UYBN8L0SM

Segment Anything but Farms: Comparing Segmentation Paradigms for Rural UAV Captured Ultra-High-Resolution Imagery

In South Asia, where 80% of farms are smallholder plots under 0.5 hectares, a 30 cm earthen ridge (”Aali”) separates two fields that appear identical in every measurable visual feature, same crop, same growth stage, same irrigation, same spectral signature; yet they represent distinct land parcels requiring individual damage attribution after flooding. This is the fundamental challenge of agricultural boundary detection: the boundaries that matter encode ownership and land tenure, not visual discontinuity. Foundation models trained on datasets emphasizing highcontrast physical edges fail catastrophically. SAM family models (224M to 848M parameters) achieve only 35-51% Field IoU despite zero-shot building detection at 80-95% on the same dataset. Even ‘DelineateAnything’, pre-trained on 22.9 million global farm instances, achieves only 72% mAP@50 on our 4.31 cm/pixel drone imagery. We systematically document why classical computer vision, foundation models, and extensive post-processing (SAM2 + 5stage pipeline with 4,096 prompts, watershed segmentation, six geometric filters) cannot achieve deployment accuracy on semantic boundaries. U-Net (MiT-B4) achieves 95.37% Field IoU, YOLOv11 reaches 92.9% mAP@50 (+20.9 pp over the 22.9M-instance baseline), and our novel panoptic formulation—treating fields as instance ”things” and surrounding context as semantic ”stuff”—achieves 97% Field IoU while extracting 2,646 individual parcels, meeting the accuracy required for flood compensation and land validation. All datasets were collected during the 2025 monsoon season in Nepal’s Koshi River basin (Saptari and Sunkoshi districts), capturing active flood conditions and post-event field states.This research was funded by the Food and Agriculture Organization of the United Nations (FAO) for post-disaster agricultural assessment in Nepal, with UAV and drone imagery support from NAXA. This work was also partially supported by ONR Grant #N00014-23-1-2119, U.S. Army Grants #W911NF2120076 and #W911NF2410367, NSF CAREER Award #1750936, NSF CNS EAGER Grant #2233879, and NSF IIS Grant #2509680https://openaccess.thecvf.com/content/WACV2026W/GeoCV/papers/Chugh_Segment_Anything_but_Farms_Comparing_Segmentation_Paradigms_for_Rural_UAV_WACVW_2026_paper.pd