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The rapid evolution of cyber threats has outpaced traditional detection methodologies, necessitating innovative approaches capable of addressing the adaptive and complex behaviors of modern adversaries. A novel framework was introduced, leveraging Temporal-Correlation Graphs to model the intricate relationships and temporal patterns inherent in malicious operations. The approach dynamically captured behavioral anomalies, offering a robust mechanism for distinguishing between benign and malicious activities in real-time scenarios. Extensive experiments demonstrated the framework's effectiveness across diverse ransomware families, with consistently high precision, recall, and overall detection accuracy. Comparative evaluations highlighted its better performance over traditional signature-based and heuristic methods, particularly in handling polymorphic and previously unseen ransomware variants. The architecture was designed with scalability and modularity in mind, ensuring compatibility with enterprise-scale environments while maintaining resource efficiency. Analysis of encryption speeds, anomaly patterns, and temporal correlations provided deeper insights into the operational strategies of ransomware, validating the framework's adaptability to evolving threats. The research contributes to advancing cybersecurity technologies by integrating dynamic graph analytics and machine learning for future innovations in threat detection. Results from this study underline the potential for transforming the way organizations detect and mitigate complex cyberattacks.

Recent advancements in 3D human pose estimation from single-camera images and videos have relied on parametric models, like SMPL. However, these models oversimplify anatomical structures, limiting their accuracy in capturing true joint locations and movements, which reduces their applicability in biomechanics, healthcare, and robotics. Biomechanically accurate pose estimation, on the other hand, typically requires costly marker-based motion capture systems and optimization techniques in specialized labs. To bridge this gap, we propose BioPose, a novel learning-based framework for predicting biomechanically accurate 3D human pose directly from monocular videos. BioPose includes three key components: a Multi-Query Human Mesh Recovery model (MQ-HMR), a Neural Inverse Kinematics (NeurIK) model, and a 2D-informed pose refinement technique. MQ-HMR leverages a multi-query deformable transformer to extract multi-scale fine-grained image features, enabling precise human mesh recovery. NeurIK treats the mesh vertices as virtual markers, applying a spatial-temporal network to regress biomechanically accurate 3D poses under anatomical constraints. To further improve 3D pose estimations, a 2D-informed refinement step optimizes the query tokens during inference by aligning the 3D structure with 2D pose observations. Experiments on benchmark datasets demonstrate that BioPose significantly outperforms state-of-the-art methods. Project website: \url{https://m-usamasaleem.github.io/publication/BioPose/BioPose.html}.

Multiphoton interference is crucial to many photonic quantum technologies. In particular, interference forms the basis of optical quantum information processing platforms and can lead to significant computational advantages. It is therefore interesting to study the interference arising from various states of light in large interferometric networks. Here, we implement a quantum walk in a highly stable, low-loss, multiport interferometer with up to 24 ultrafast time bins. This time-bin interferometer comprises a sequence of birefringent crystals which produce pulses separated by 4.3\,ps, all along a single optical axis. Ultrafast Kerr gating in an optical fiber is employed to time-demultiplex the output from the quantum walk. We measure one-, two-, and three-photon interference arising from various input state combinations, including a heralded single-photon state, a thermal state, and an attenuated coherent state at one or more input ports. Our results demonstrate that ultrafast time bins are a promising platform to observe large-scale multiphoton interference.

Both freeze-in of very weakly coupled dark matter and freeze-out of initially thermalized dark matter from the primordial heat bath provide interesting possibilities for dark matter creation in the early universe. Both scenarios allow for a calculation of baryon-dark matter coupling constants as a function of dark matter mass due to the constraint that freeze-in or freeze-out produce the observed dark matter abundance. Here we compare the resulting coupling constants in the two scenarios if dark matter couples to baryons through an antisymmetric tensor portal. The freeze-in scenario predicts much smaller coupling in agreement with the nonthermalization postulate. We find that the couplings as a function of mass behave very differently in the two scenarios., Comment: 6 pages, 6 figures,version accepted for publication in Journal of Subatomic Particles and Cosmology

This chapter explores the essential role of Binding Corporate Rules (BCRs) in managing and facilitating secure health data transfers within corporate groups under the EU General Data Protection Regulation (GDPR). BCRs are tailored to ensure compliance with the GDPR and similar international data protection laws, presenting a flexible mechanism for transferring sensitive health and genomic data. The chapter situates BCRs within the broader spectrum of the GDPR international data transfer mechanisms, addressing the unique challenges posed by the sensitive nature of health data and the increased adoption of AI technologies. The European Data Protection Board (EDPB) Recommendations 1/2022 on BCRs, issued following the Schrems II decision, are critically analyzed, highlighting their stringent requirements and the need for a balanced approach that prioritizes data protection and an AI governance framework. The chapter outlines the BCR approval process, stressing the importance of streamlining this process to encourage broader adoption. It underscores the necessity of a multidisciplinary approach in developing BCRs, incorporating recently adopted international standards and frameworks, which offer valuable guidance for organizations to build trustworthy AI management systems. They guarantee the ethical development, deployment, and operation of AI, which is essential for its successful integration and the broader digital transformation. In conclusion, BCRs are positioned as essential tools for secure health data management, fostering transparency, accountability, and collaboration across international borders. The chapter calls for proactive measures to incentivize BCR adoption, streamline approval processes, and promote more innovative approaches, ensuring BCRs remain a robust mechanism for global data protection and compliance.

The General Data Protection Regulation contains a blanket prohibition on the transfer of personal data outside of the European Economic Area unless strict requirements are met. The rationale for this provision is to protect personal data and data subject rights by restricting data transfers to countries that may not have the same level of protection as the EEA. However, the ubiquitous and permeable character of new technologies such as cloud computing, and the increased inter connectivity between societies, has made international data transfers the norm and not the exception. The Schrems II case and subsequent regulatory developments have further raised the bar for companies to comply with complex and, often, opaque rules. Many firms are, therefore, pursuing technology-based solutions in order to mitigate this new legal risk. These emerging technological alternatives reduce the need for open-ended cross-border transfers and the practical challenges and legal risk that such transfers create after Schrems. This article examines one such alternative, namely a user-held data model. This approach takes advantage of personal data clouds that allows data subjects to store their data locally and in a more decentralised manner, thus decreasing the need for cross-border transfers and offering end-users the possibility of greater control over their data.

This introductory chapter briefly outlines the main theme of this volume, namely, to review the new opportunities and risks of digital healthcare from various disciplinary perspectives. These perspectives include law, public policy, organisational studies, and applied ethics. Based on this interdisciplinary approach, we hope that effective strategies may arise to ensure that benefits of this on-going revolution are deployed in a responsible and sustainable manner. The second part of the chapter comprises a brief review of the four parts and fourteen substantive chapters that comprise this volume.

Identifying and then implementing an effective response to disruptive new AI technologies is enormously challenging for any business looking to integrate AI into their operations, as well as regulators looking to leverage AI-related innovation as a mechanism for achieving regional economic growth. These business and regulatory challenges are particularly significant given the broad reach of AI, as well as the multiple uncertainties surrounding such technologies and their future development and effects. This article identifies two promising strategies for meeting the AI challenge, focusing on the example of Fintech. First, dynamic regulation, in the form of regulatory sandboxes and other regulatory approaches that aim to provide a space for responsible AI-related innovation. An empirical study provides preliminary evidence to suggest that jurisdictions that adopt a more proactive approach to Fintech regulation can attract greater investment. The second strategy relates to so-called innovation ecosystems. It is argued that such ecosystems are most effective when they afford opportunities for creative partnerships between well-established corporations and AI-focused startups and that this aspect of a successful innovation ecosystem is often overlooked in the existing discussion. The article suggests that these two strategies are interconnected, in that greater investment is an important element in both fostering and signaling a well-functioning innovation ecosystem and that a well-functioning ecosystem will, in turn, attract more funding. The resulting synergies between these strategies can, therefore, provide a jurisdiction with a competitive edge in becoming a regional hub for AI-related activity.

We construct exact analytical Taub-NUT solutions in the context of $f(T)$ gravity. We study the physical properties of the solutions, and compare them with those of the Taub-NUT solution in Einstein gravity., Comment: 13 pages, references added, typos corrected

We propose a quantum information processing platform that utilizes the ultrafast time-bin encoding of photons. This approach offers a pathway to scalability by leveraging the inherent phase stability of collinear temporal interferometric networks at the femtosecond-to-picosecond timescale. The proposed architecture encodes information in ultrafast temporal bins processed using optically induced nonlinearities and birefringent materials while keeping photons in a single spatial mode. We demonstrate the potential for scalable photonic quantum information processing through two independent experiments that showcase the platform's programmability and scalability, respectively. The scheme's programmability is demonstrated in the first experiment, where we successfully program 362 different unitary transformations in up to 8 dimensions in a temporal circuit. In the second experiment, we show the scalability of ultrafast time-bin encoding by building a passive optical network, with increasing circuit depth, of up to 36 optical modes. In each experiment, fidelities exceed 97\%, while the interferometric phase remains passively stable for several days., Comment: 7 pages, 3 figures

The quantum walk (QW) has proven to be a valuable testbed for fundamental inquiries in quantum technology applications such as quantum simulation and quantum search algorithms. Many benefits have been found by exploring implementations of QWs in various physical systems, including photonic platforms. Here, we propose a novel platform to perform quantum walks using an ultrafast time-bin encoding (UTBE) scheme. This platform supports the scalability of quantum walks to a large number of steps while retaining a significant degree of programmability. More importantly, ultrafast time bins are encoded at the picosecond time scale, far away from mechanical fluctuations. This enables the scalability of our platform to many modes while preserving excellent interferometric phase stability over extremely long periods of time without requiring active phase stabilization. Our 18-step QW is shown to preserve interferometric phase stability over a period of 50 hours, with an overall walk fidelity maintained above $95\%$, Comment: 13 pages, 8 figures

We are at a pivotal time in our country. As reading achievement declines pre-and post-COVID-19, large portions of our nation's students continue to be underserved by our education system while many teachers do not have needed support to provide evidence-based literacy instruction (NAEP, 2024). In this white paper, the Collaborative for Academic, Social, and Emotional Learning (CASEL) and leading researchers across diverse disciplines urge policymakers to consider the full breadth of research on literacy to enable strong reading development and to improve teacher knowledge and their ability to deliver rigorous instruction. We demonstrate evidence for an integrated approach to building foundational reading skills that also focus on comprehension, language, knowledge, and social and emotional learning. Since the 2000 National Reading Panel, new insights have emerged from a comprehensive understanding of the Science of Reading about the nature of reading, how students develop their skills in it, and effective teaching pedagogy. In many states, legislation has been passed in the past few years emphasizing a comprehensive approach to improving reading and literacy. While this is a good start, more must be done to support the literacy development of all students and close gaps in reading achievement. Such an undertaking cannot be achieved without comprehensive and long-term literacy programs based on solid empirical evidence. Current research in the fields of human development, learning sciences, and neurosciences build upon each other and demonstrate that learning environments and instruction must support students' social and emotional development and well-being to drive student literacy development. The white paper also puts forward research and policy recommendations to support a comprehensive approach to supporting student literacy development and instruction through: (1) Strengthening Education Research, including by: (a) Updating the National Reading Panel and Commissioning Studies on Literacy Development; and (b) Bridging the Science of Reading and the Science of Learning and Development; (2) Strengthening Teacher Preparation and Professional Development; and (3) Expanding Access to Reading Supports in the Classroom, School, and Community Libraries.

The conditional generative adversarial rainfall model "cGAN" developed for the UK \cite{Harris22} was trained to post-process into an ensemble and downscale ERA5 rainfall to 1km resolution over three regions of the USA and the UK. Relative to radar data (stage IV and NIMROD), the quality of the forecast rainfall distribution was quantified locally at each grid point and between grid points using the spatial correlation structure. Despite only having information from a single lower quality analysis, the ensembles of post processed rainfall produced were found to be competitive with IFS ensemble forecasts with lead times of between 8 and 16 hours. Comparison to the original cGAN trained on the UK using the IFS HRES forecast indicates that improved training forecasts result in improved post-processing. The cGAN models were additionally applied to the regions that they were not trained on. Each model performed well in their own region indicating that each model is somewhat region specific. However the model trained on the Washington DC, Atlantic coast, region achieved good scores across the USA and was competitive over the UK. There are more overall rainfall events spread over the whole region so the improved scores might be simply due to increased data. A model was therefore trained using data from all four regions which then outperformed the models trained locally.

This papers explores the question of human authorship when works are created with generative AI tools., Comment: 3 figures

Recent experimental progress in quantum optics has enabled measurement of single photons on ultrafast timescales, beyond the resolution limit of single photon detectors. The energy-time degree of freedom has emerged as a promising avenue for quantum technologies, as entanglement between the frequency and temporal properties of two photons can be fully explored and utilized. Here, we implement optical Kerr shutters in single mode fibers to map out the sub-picosecond correlations of energy-time entangled photon pairs. These measurements, in addition to joint spectral measurements of the photon pair state, are used to verify entanglement by means of the violation of a time-bandwidth inequality., Comment: 5 pages, 4 figures

Flare frequency distributions represent a key approach to addressing one of the largest problems in solar and stellar physics: determining the mechanism that counter-intuitively heats coronae to temperatures that are orders of magnitude hotter than the corresponding photospheres. It is widely accepted that the magnetic field is responsible for the heating, but there are two competing mechanisms that could explain it: nanoflares or Alfv\'en waves. To date, neither can be directly observed. Nanoflares are, by definition, extremely small, but their aggregate energy release could represent a substantial heating mechanism, presuming they are sufficiently abundant. One way to test this presumption is via the flare frequency distribution, which describes how often flares of various energies occur. If the slope of the power law fitting the flare frequency distribution is above a critical threshold, $\alpha=2$ as established in prior literature, then there should be a sufficient abundance of nanoflares to explain coronal heating. We performed $>$600 case studies of solar flares, made possible by an unprecedented number of data analysts via three semesters of an undergraduate physics laboratory course. This allowed us to include two crucial, but nontrivial, analysis methods: pre-flare baseline subtraction and computation of the flare energy, which requires determining flare start and stop times. We aggregated the results of these analyses into a statistical study to determine that $\alpha = 1.63 \pm 0.03$. This is below the critical threshold, suggesting that Alfv\'en waves are an important driver of coronal heating., Comment: 1,002 authors, 14 pages, 4 figures, 3 tables, published by The Astrophysical Journal on 2023-05-09, volume 948, page 71

Imposing the Born rule as a fundamental principle of quantum mechanics would require the existence of normalizable wave functions also for relativistic particles. Indeed, the Fourier transforms of normalized k-space amplitudes yield normalized x-space wave packets which reproduce the standard k-space expectation values for energy and momentum from local momentum pseudo-densities. However, in the case of bosonic fields, the wave packets are nonlocally related to the corresponding relativistic quantum fields, and therefore the canonical local energy-momentum densities differ from the pseudo-densities and appear nonlocal in terms of the wave packets. We examine the relation between the canonical energy density, the canonical charge density, the energy pseudo-density, and the Born density for the massless free Klein-Gordon field. We find that those four proxies for particle location are tantalizingly close even in this extremely relativistic case: In spite of their nonlocal mathematical relations, they are mutually local in the sense that their maxima do not deviate beyond a common position uncertainty $\Delta x$. Indeed, they are practically indistinguishable in cases where we would expect a normalized quantum state to produce particle-like position signals, viz. if we are observing quanta with momenta $p\gg\Delta p\ge\hbar/2\Delta x$. We also translate our results to massless Dirac fields. Our results confirm and illustrate that the normalized energy density provides a suitable measure for positions of bosons, whereas normalized charge density provides a suitable measure for fermions., Comment: 34 pages, 23 figures. Please see paper for full abstract with LaTeX symbols included