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The recent advancements in generative AI techniques, which have significantly increased the online dissemination of altered images and videos, have raised serious concerns about the credibility of digital media available on the Internet and distributed through information channels and social networks. This issue particularly affects domains that rely heavily on trustworthy data, such as journalism, forensic analysis, and Earth observation. To address these concerns, the ability to geolocate a non-geo-tagged ground-view image without external information, such as GPS coordinates, has become increasingly critical. This study tackles the challenge of linking a ground-view image, potentially exhibiting varying fields of view (FoV), to its corresponding satellite image without the aid of GPS data. To achieve this, we propose a novel four-stream Siamese-like architecture, the Quadruple Semantic Align Net (SAN-QUAD), which extends previous state-of-the-art (SOTA) approaches by leveraging semantic segmentation applied to both ground and satellite imagery. Experimental results on a subset of the CVUSA dataset demonstrate significant improvements of up to 9.8\% over prior methods across various FoV settings., Comment: 9 pages, 4 figures

Smart focal-plane and in-chip image processing has emerged as a crucial technology for vision-enabled embedded systems with energy efficiency and privacy. However, the lack of special datasets providing examples of the data that these neuromorphic sensors compute to convey visual information has hindered the adoption of these promising technologies. Neuromorphic imager variants, including event-based sensors, produce various representations such as streams of pixel addresses representing time and locations of intensity changes in the focal plane, temporal-difference data, data sifted/thresholded by temporal differences, image data after applying spatial transformations, optical flow data, and/or statistical representations. To address the critical barrier to entry, we provide an annotated, temporal-threshold-based vision dataset specifically designed for face detection tasks derived from the same videos used for Aff-Wild2. By offering multiple threshold levels (e.g., 4, 8, 12, and 16), this dataset allows for comprehensive evaluation and optimization of state-of-the-art neural architectures under varying conditions and settings compared to traditional methods. The accompanying tool flow for generating event data from raw videos further enhances accessibility and usability. We anticipate that this resource will significantly support the development of robust vision systems based on smart sensors that can process based on temporal-difference thresholds, enabling more accurate and efficient object detection and localization and ultimately promoting the broader adoption of low-power, neuromorphic imaging technologies. To support further research, we publicly released the dataset at \url{https://dx.doi.org/10.21227/bw2e-dj78}., Comment: 8 pages

The dynamics of perfect fluid as a source in the context of modified gravity, specifically $f(Q,T)$ gravity, are examined within the Friedmann-Lema\^itre-Robertson-Walker (FLRW) cosmological model. This gravity is a generic function of the non-metricity scalar $Q$ and its trace $T$. We investigate the characteristics of the derived cosmological model using a parameterized form of Hubble's parameter, $H(z) = H_0\left[\Omega_{0m}(1+z)^3 + (1-\Omega_{0m})\right]^{\frac{1}{2}}$ (Mahmood et al. Int. J. Geom. Methods Mod. Phys., https://doi.org/10.1142/S0219887824502049). Our examination reveals how physical parameters such as energy density, pressure, and the equation of state parameter, among others, in our model accurately describe the physical behavior of the cosmos. Furthermore, we explore the kinematic parameters in our model, which provide valuable insights into the cosmos's expansion history, including its acceleration, deceleration, and the evolution of its large-scale structure. By exploring these aspects, we gain a deeper understanding of the cosmos's dynamics and evolution within the context of modified gravity., Comment: 18 pages, 10 figures

Chemical syntheses that provide nanocrystals (NCs) with narrow distributions in size and shape are critical for NC research. This has led to the investigation of magic-sized NCs (MSNCs), a class of semiconductor crystallites that grow in discrete steps, potentially offering a single size and shape (i.e., monodispersity). However, the photoluminescence (PL) spectra of CdSe MSNCs measured at room temperature have been reported to be broader than those of state-of-the-art quantum dots. This difference could be due to the smaller size of MSNCs, which broadens their line widths, or due to their residual size dispersity. To better understand the optical performance of MSNCs, here we perform single-particle spectroscopy. Our results show that, while CdSe MSNCs do exhibit particle-to-particle variations that lead to modest broadening of their ensemble emission spectra, the largest contribution comes from the single-particle line width. By examining MSNCs with different sizes and shells, we conclude that this single-particle broadening is consistent with exciton coupling to acoustic phonons from the NC surface. Because of their small size, this coupling and the role of residual size dispersity have a larger impact on the ensemble emission line widths. Notably, when small (<2.7 nm diameter) MSNCs and quantum dots are compared, the ensemble PL line widths of MSNCs are actually sharper. Due to their small size, MSNCs also exhibit strong anti-bunching $[g^{(2)}(0) \sim 0.05]$ at room temperature. Thus, MSNCs represent a bright, spectrally pure class of quantum emitter, useful for applications in optoelectronic and quantum-information technologies where strong three-dimensional confinement is required.

The purpose of this clinical study is to test broad spectral range (635-1060 nm) time domain diffuse optical spectroscopy in monitoring the response of breast cancer patients to neoadjuvant chemotherapy (NAC). The broadband operation allows us to fully analyze tissue composition in terms of hemoglobin, water, lipids and collagen concentration, which has never been systematically studied until now during the course of therapy. Patients are subjected to multiple breast optical imaging sessions, each one performed at different stages of NAC, both on tumor-bearing and contralateral healthy breast. We correlate the optical results with conventional imaging techniques and pathological response. Preliminary outcomes on data of 10 patients show an average significant reduction in the concentrations of oxy-hemoglobin (-53%, p = 0.0020), collagen (-36%, p = 0.0039) and water (-15%, p = 0.0195), and increase in lipids (+39%, p = 0.0137) from baseline to the end of therapy in the tumor-bearing breast of patients who responded to therapy at least partially. With respect to scattering, the scattering amplitude, a, increases slightly by the end of the therapy compared to the baseline, while the scattering slope, b, shows no significant change. Some change in concentrations of constituents was noticed also in the contralateral healthy breast, even though it was significant only for oxy-hemoglobin concentration. Collagen seems to be the only component distinguishing between complete (p = 0.0423) and partial responders (p = 0.1012) by the end of 2-3 weeks after the start of therapy, suggesting that it could serve as a potential biomarker to measure NAC effectiveness early during treatment. These initial results are consistent with those of standard medical modalities and highlight the sensitivity of the technique to changes that occur in breast composition during NAC.