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X-Ray Polarimetric Observations of the Western Hotspot of Pictor A
No full textWe present the results of the analysis of a ∼2 Msec spectropolarimetric observation of the western hotspot (WHS) of the radio galaxy Pictor A. This is the brightest extragalactic radio source that allows us to carry out spatially resolved observations using the Imaging X-ray Polarimetry Explorer (IXPE) because its WHS is located at ∼250″ from the radio core. The Pictor A WHS can be detected with IXPE as a pointlike source, with very low contamination from the nuclear emission, and it is extremely polarized at both radio and optical frequencies, where its emission is well described as synchrotron radiation. We find no X-ray polarization for the Pictor A WHS. However, the derived upper limit allows us to set a first constraint on the radiative processes occurring therein, ruling out a simple synchrotron scenario where particles, with a random distribution of pitch angles, radiate in a uniform magnetic field. We also tested a scenario with a random magnetic field compressed along the jet direction
A HOLISTIC APPROACH: STRATEGIES FOR STUDENTS WITH CORTICAL/CEREBRAL VISUAL IMPAIRMENT
No full textThis study explored how Louisiana’s expanded definition of visual impairment has influenced professional practices in identifying and supporting students with Cortical or Cerebral Visual Impairment (CVI). CVI, a brain-based visual condition, remains understudied, leaving educators with limited evidence-based guidance. Using a qualitative phenomenological design, this study examined the experiences, challenges, and strategies of professionals across eight key roles: Assistive Technology (AT) Specialists, Occupational Therapists (OTs), Orientation and Mobility (O&M) Specialists, paraprofessionals, Speech-Language Pathologists (SLPs), special education teachers, special education administrators, and Teachers of the Visually Impaired (TVIs). Data were collected through semi-structured interviews and artifacts to capture interdisciplinary perspectives. Thematic analysis identified patterns in how professionals understand CVI, address learning needs, conduct assessments, and implement instructional strategies. Findings indicate that Louisiana’s policy shift has begun to reshape practice, leading to more interdisciplinary, collaborative, and holistic approaches to support. This study contributes to the limited body of CVI research and represents the first examination of how a state-level policy change has affected professional practice in this field
Robert Williams, 1943-1992: A Cultural History of Lowbrow Art
No full textThis thesis examines the early career of Robert Williams, an artist who emerged from the Los Angeles underground art scene and whose work came to define Lowbrow art, one of America’s most dynamic art movements. Despite his profound influence, Williams’s role as a key figure in the development of late twentieth-century American visual culture is largely overlooked by scholars and critics. This omission reflects the broader marginalization of subcultural movements within the prevailing narratives of postwar American art.
To address this knowledge gap, the study contextualizes Williams’s work within his biography, which intersects the subcultures of carnival life, Kustom Kulture, psychedelic art, underground comix, and punk. Each of these underground communities contributed to the chaotic visual energy, subversive humor, and complex symbolism that characterize his oeuvre. Drawing on personal interviews, alternative archives, and object-based analysis of previously unexamined visual materials, this study employs frameworks from subcultural theory and art historiography to understand Williams as an artist who positioned himself both within and against the dominant narratives of late-twentieth- and early-twenty-first-century American art.
The chapters trace Williams’s early career trajectory from his early experiences in hot rod and custom car culture to his role as founder of the Lowbrow art, a movement that transformed the artistic landscape of Los Angeles before expanding to the global stage. Ultimately, this thesis argues that Robert Williams is more than a peripheral figure of the Los Angeles underground; rather, he is a central force in the evolution of American visual culture. In doing so, it advocates for a more inclusive framework for understanding contemporary art, one that recognizes the subcultural, the commercial, and the transgressive as integral to the evolution of the American avant-garde
Supporting Best Practices in Family Engagement: Our Week of the Young Child Experience
No full textFamily engagement is a collaborative process where early childhood professionals, families, and children build positive, goal-oriented relationships. Incorporating family engagement into preschool programs is not merely beneficial but essential for promoting holistic child development in programs where children feel safe, cared for, and secure. In this article, we share the benefits of effective family engagement activities for families with young children and describe how we celebrated the National Association for the Education of Young Children\u27s 2025 Week of the Young Child
SEEING WHAT MATTERS: SAFETY-CRITICAL SEMANTIC SEGMENTATION VIA TRANSFER LEARNING ON CONSTRUCTION SITES
No full textAutonomous robots are increasingly deployed on construction sites for tasks such as progress monitoring, inspection, and safety assessment. For these robots to operate effectively, they must perceive and interpret complex, dynamic environments populated by workers, machinery, and unstructured terrain. Achieving reliable perception depends on high performing semantic segmentation models trained on large volumes of annotated data—an expensive and logistically challenging requirement in construction due to privacy restrictions, variable site access, and slow digitalization. This research addresses the challenge of limited labeled data by investigating transfer learning as a label-efficient approach for construction-site segmentation. Specifically, it explores whether road construction imagery—abundant and publicly available—can serve as a domain-adjacent pretraining source for building-site vision models. Two architectures representing distinct design paradigms were evaluated: the convolutional network DeepLabv3+ (ResNet-50 backbone with atrous spatial pyramid pooling) and the transformer-based SegFormer (MiT-B0 backbone). Each model was pretrained on two road-scene datasets—ROADWork (construction-specific) and Cityscapes (urban driving)—and subsequently fine-tuned on a 5,550-image building-site dataset collected with a Boston Dynamics Spot robot equipped with RGB and LiDAR sensors. Experiments were conducted under varying annotation budgets ranging from 20 to 1,000 labeled images to simulate real-world data scarcity. Results show that performance improves steeply up to approximately 420 images, then gradually saturates near 600. Domain-aligned pretraining from ROADWork consistently outperformed Cityscapes across all budgets, with the advantage most pronounced for safety-critical classes such as ix Workers and Equipment. At full data scale, SegFormer–ROADWork achieved 0.65 mIoU, slightly surpassing DeepLab–ROADWork (0.64 mIoU) and significantly outperforming all Cityscapes-initialized counterparts. The study demonstrates that domain-adjacent pretraining substantially enhances segmentation accuracy under limited supervision, especially for transformer based models that rely less on inductive biases. These findings provide practical guidance for robotic perception in construction: select pretrained sources that closely resemble the target environment, allocate labeling resources up to roughly 600 images for maximal efficiency, and prefer lightweight transformer architectures when domain alignment is feasible
Loop quantum gravitational signatures via Love numbers
No full textLoop quantum gravitational effects can resolve the central singularity of black holes, while potentially leaving tiny traces of quantization in the exterior spacetime. We show the way these residues can, in principle, be explored using tidal Love numbers (TLNs). We consider loop quantized Schwarzschild black hole, in particular the Ashtekar-Olmedo-Singh (AOS) model, and study the static response to external tidal fields of spin zero (scalar field), spin one (vector field), and spin two (axial gravitational field) types. We find that, in contrast to the classical theory, where TLNs vanish, they are nonvanishing and negative for all three responses and for all multipoles. Besides, the magnitude of TLNs decreases as the black hole mass increases, and TLNs, in response to the axial gravitational field, have the largest magnitude among these three responses. Our results show that for black holes of mass M≳4.3×104MPl, the AOS model is consistent with current and next-generation detection limits for TLNs. Our findings suggest that the quantum deformability of loop-quantized black holes, arising from the inherent fuzziness of spacetime geometry, reveals a fundamentally distinct internal structure compared to their classical counterparts. This unique feature manifests as quantum hair, which, in principle, can be detected by future observations
Echoes of Love beyond the horizon: A bridge to recovering information from black holes
No full textWe provide further evidence that information is preserved during black hole evaporation and may be recoverable, provided quantum gravitational effects resolve the singularity. We demonstrate that due to quantum gravity effects, black holes acquire quantum hair, manifested by nonzero tidal Love numbers, revealing a distinct internal structure similar to neutron stars. Interestingly, the magnitude of these Love numbers is Planck-scale suppressed, implying significant tidal deformation in the late stage of evaporation. Depending on the final state of the black hole, information may be retrieved through correlations in Hawking radiation, baby universes or via remnants
DETECTION OF SWEET POTATO FEATHERY MOTTLE VIRUS INFECTION VIA REMOTE HYPERSPECTRAL IMAGING
No full textIn sweetpotato, global production exceeds 90 million tons annually, yet yields are threatened by numerous pathogens, including the potyvirus sweet potato feathery mottle virus (SPFMV). Virus infections can reduce yields by 25–40%, but surveillance is hampered by asymptomatic infections and the labor-intensive nature of detection. Hyperspectral imaging (HSI), combined with machine learning (ML), offers a promising pathway to scalable, non-destructive detection; however, studies focused on virus infection in sweetpotato remain limited.
This study demonstrates the feasibility of ML-enabled HSI for SPFMV detection under greenhouse conditions via a curated spectral library of two commercial cultivars, \u27Beauregard\u27 and \u27Orleans\u27, and the biological indicator Ipomoea setosa across early infection stages with both symptomatic and asymptomatic plants [3–20 days post inoculation (DPI)]. The infection status of each imaged plant was confirmed via reverse transcriptase quantitative polymerase chain reaction confirmed SPFMV-positive and SPFMV-negative plants. Supervised classifiers trained on standardized and transformed reflectance data (400–1000 nm) achieved study-wide accuracies ≥ 76%. Evaluation of principal components analysis and vegetation index-based transformations revealed the feasibility and effectiveness of these methods to both reduce computational load and improve model performance.
Results showed relative viral load increased over time in all three hosts, and we identified a viral-load threshold above which classification accuracy increased sharply. Importantly, successful detection was achievable as early as 3 DPI, indicating that spectral changes preceded prominent symptom expression. Feature-level analyses implicated red-edge and near-infrared regions, as well as chlorophyll-related indices, as informative markers from 3 to 20 DPI.
This work contributes a reusable, labeled spectral library tailored to SPFMV, a transparent imaging-to-model pipeline suited for greenhouse screening and pre-field studies, and quantitative evidence that HSI-ML can detect SPFMV regardless of symptomology. Collectively, these data establish a practical foundation for hyperspectral, ML-based pathogen surveillance in sweetpotato and point toward scalable remote-sensing systems for early, non-destructive virus monitoring
Effects of Code Scaffolding in Increasing Student Confidence in Programming Cryptography
No full textCryptography is essential for secure communications, and new threats require more students willing to program and interact with cryptographic systems. Previous research is focused on tools for teaching these systems at a high level, teaching through attacks against these systems, and proper use of these systems in software development. In this paper, we seek to design a workshop to use scaffolded Python code to teach how these cryp- tographic systems are designed. We explore the use of code scaffolding for students to program an example implementation of the McEliece crypto- graphic system to build confidence in working with these systems. Using qualitative measures, we observed an increase in student confidence in working with cryptographic systems. This further supports the use of scaffolding in helping students engage with more difficult problem-solving subjects