As physicists pursue precision neutrino measurements, complementary experiments covering varied oscillation landscapes have become essential for resolving current tensions in global fits. This thesis presents projected sensitivities and forecasted performance of two next-generation long-baseline experiments: DUNE and T2HK, through detailed simulations addressing fundamental questions including neutrino mass ordering, leptonic CP violation, and the octant of $\theta_{23}$. We demonstrate through simulated analyses that while each experiment alone faces inherent degeneracies, their complementary features enable breakthrough projected sensitivities in both standard oscillation parameter measurements and forecasted searches for new physics beyond the Standard Model. The combined simulation results reveal that DUNE-T2HK synergy will be crucial for achieving a comprehensive understanding of neutrino properties in the coming decade.

This paper presents mathematical modeling and numerical analysis of bifurcation and synchronization phenomena in a system of coupled oscillators driven by a finite-power energy source and generating two-dimensional stick-slip translational-rotational vibrations. The mechanical system consists of rigid disks placed on moving belts and asymmetrically connected to a support via springs, with each disk simultaneously performing translational and rotational motion. The belts are driven by a common DC motor. The disk contacts the belt over a finite contact area, resulting in mutually coupled frictional force and torque through their dependence on the linear and angular slip velocity. The paper utilizes special approximations of the resultant frictional forces and torques based on generalizations of Padé's developements, in which special smoothing elements are introduced to avoid singularities when the relative motion between the disk and belt disappears. Furthermore, the model also provides a smooth approximation of the friction model, in which static friction is greater than kinetic friction. Numerical analysis was conducted based on direct numerical simulations, Poincare maps, and bifurcation diagrams. It was shown that the system can exhibit a two-dimensional stick-slip phenomenon, and the system exhibits predominantly periodic dynamics, sometimes with a very long period. In this work, a system of two oscillators driven by a common, limited-power motor was studied, which can synchronize and oscillate in phase or in counterphase.

Deconfined quantum critical points (DQCPs) represent an unconventional class of quantum criticality beyond the Landau-Ginzburg-Wilson-Fisher paradigm. Nevertheless, both their theoretical identification and experimental realization remain challenging. Here we report compelling evidence of a DQCP in quantum Hall bilayers with half-filled $n=2$ Landau levels in each layer, based on large-scale variational uniform matrix product state (VUMPS) simulations and exact diagonalization (ED). By systematically analyzing the ground-state fidelity, low-lying energy spectra, exciton superfluid and stripe order parameters, and ground-state energy derivatives, we identify a direct and continuous quantum phase transition between two distinct symmetry-breaking phases by tuning the layer separation: an exciton superfluid phase with spontaneous $U(1)$ symmetry breaking at small separation, and a unidirectional charge density wave with broken translational symmetry at large separation. Our results highlight quantum Hall bilayers as an ideal platform for realizing and experimentally probing DQCPs under precisely tunable interactions.

It is well known that Kasner geometry with space-like singularity can be extended to bulk AdS-like geometry, furthermore one can study field theory on this Kasner space via its gravity dual. In this paper, we show that there exists a Kasner-like geometry with timelike singularity for which one can construct a dual gravity description. We then study various extremal surfaces including space-like geodesics in the dual gravity description. Finally, we compute correlators of highly massive operators in the boundary field theory with a geodesic approximation.

Determining crystal structures from X-ray diffraction data is fundamental across diverse scientific fields, yet remains a significant challenge when data is limited to low resolution. While recent deep learning models have made breakthroughs in solving the crystallographic phase problem, the resulting low-resolution electron density maps are often ambiguous and difficult to interpret. To overcome this critical bottleneck, we introduce XDXD, to our knowledge, the first end-to-end deep learning framework to determine a complete atomic model directly from low-resolution single-crystal X-ray diffraction data. Our diffusion-based generative model bypasses the need for manual map interpretation, producing chemically plausible crystal structures conditioned on the diffraction pattern. We demonstrate that XDXD achieves a 70.4\% match rate for structures with data limited to 2.0~Å resolution, with a root-mean-square error (RMSE) below 0.05. Evaluated on a benchmark of 24,000 experimental structures, our model proves to be robust and accurate. Furthermore, a case study on small peptides highlights the model's potential for extension to more complex systems, paving the way for automated structure solution in previously intractable cases.

Studies of entanglement dynamics in quantum many-body systems have focused largely on initial product states. Here, we investigate the far richer dynamics from initial entangled states, uncovering universal patterns across diverse systems ranging from many-body localization (MBL) to random quantum circuits. Our central finding is that the growth of entanglement entropy can exhibit a non-monotonic dependence on the initial entanglement in many non-ergodic systems, peaking for moderately entangled initial states. To understand this phenomenon, we introduce a conceptual framework that decomposes entanglement growth into two mechanisms: ``build'' and ``move''. The ``build'' mechanism creates new entanglement, while the ``move'' mechanism redistributes pre-existing entanglement throughout the system. We model a pure ``move'' dynamics with a random SWAP circuit, showing it uniformly distributes entanglement across all bipartitions. We find that MBL dynamics are ``move-dominated'', which naturally explains the observed non-monotonicity of the entanglement growth. This ``build-move'' framework offers a unified perspective for classifying diverse physical dynamics, deepening our understanding of entanglement propagation and information processing in quantum many-body systems.

Recent technological advances popularized the use of image generation among the general public. Crafting effective prompts can, however, be difficult for novice users. To tackle this challenge, we developed PromptMap, a new interaction style for text-to-image AI that allows users to freely explore a vast collection of synthetic prompts through a map-like view with semantic zoom. PromptMap groups images visually by their semantic similarity, allowing users to discover relevant examples. We evaluated PromptMap in a between-subject online study ($n=60$) and a qualitative within-subject study ($n=12$). We found that PromptMap supported users in crafting prompts by providing them with examples. We also demonstrated the feasibility of using LLMs to create vast example collections. Our work contributes a new interaction style that supports users unfamiliar with prompting in achieving a satisfactory image output.

Noncompact groups, similar to those that appeared in various supergravity theories in the 1970's, have been turning up in recent studies of string theory. First it was discovered that moduli spaces of toroidal compactification are given by noncompact groups modded out by their maximal compact subgroups and discrete duality groups. Then it was found that many other moduli spaces have analogous descriptions. More recently, noncompact group symmetries have turned up in effective actions used to study string cosmology and other classical configurations. This paper explores these noncompact groups in the case of toroidal compactification both from the viewpoint of low-energy effective field theory, using the method of dimensional reduction, and from the viewpoint of the string theory world sheet. The conclusion is that all these symmetries are intimately related. In particular, we find that Chern--Simons terms in the three-form field strength $H_{\mu\nu\rho}$ play a crucial role.

A false zero resistance behavior was observed during our study on the search of superconductivity in Ge-doped GaNb4Se8. This zero resistance was proved to be caused by open-circuit in multi-phase samples comprised of metals and insulators by measuring with four-probe method. The evidence strongly suggests that the reported superconductivity in hydrides should be carefully re-checked.

We extend recent discussions about the effect of nonzero temperature on the induced electric charge, due to CP violation, of a Dirac or an 't Hooft-Polyakov monopole. In particular, we determine the fractional electric charge of a very small 't Hooft-Polyakov monopole coupled to light fermions at nonzero temperature. If dyons with fractional electric charge exist in the Weinberg-Salam model, as recently suggested in the literature, then their charge too should be temperature dependent.

Quantum field theory (QFT) for interacting many-electron systems is fundamental to condensed matter physics, yet achieving accurate solutions confronts computational challenges in managing the combinatorial complexity of Feynman diagrams, implementing systematic renormalization, and evaluating high-dimensional integrals. We present a unifying framework that integrates QFT computational workflows with an AI-powered technology stack. A cornerstone of this framework is representing Feynman diagrams as computational graphs, which structures the inherent mathematical complexity and facilitates the application of optimized algorithms developed for machine learning and high-performance computing. Consequently, automatic differentiation, native to these graph representations, delivers efficient, fully automated, high-order field-theoretic renormalization procedures. This graph-centric approach also enables sophisticated numerical integration; our neural-network-enhanced Monte Carlo method, accelerated via massively parallel GPU implementation, efficiently evaluates challenging high-dimensional diagrammatic integrals. Applying this framework to the uniform electron gas, we determine the quasiparticle effective mass to a precision significantly surpassing current state-of-the-art simulations. Our work demonstrates the transformative potential of integrating AI-driven computational advances with QFT, opening systematic pathways for solving complex quantum many-body problems across disciplines.

A novel instrument has been developed to monitor and record the ambient pa- rameters such as temperature, atmospheric pressure and relative humidity. These parameters are very essential for understanding the characteristics such as gain of gas filled detectors like Gas Electron Multiplier (GEM) and Multi Wire Propor- tional Counter (MWPC). In this article the details of the design, fabrication and operation processes of the device has been presented.

The aim of this paper is to present an analytical calculation of the chemical potential of a Lennard Jones fluid. The integration range is divided into two regions. In the small distance region,which is $r\leq\sigma$ in the usual notation,the integration range had to be cut off in order to avoid the occurence of this http URL the large distance region,the calculation is technically simpler. The calculation reported here will be useful in all kinds of studies concerning phase equilibrium in a $LJ$ fluid. Interesting kinds of such systems are the giant planets and the icy satellites in various planetary systems,but also the (so far) hypothetical quark stars.

We consider a generalized quantum teleportation protocol for an unknown qubit using non-maximally entangled state as a shared resource. Without recourse to local filtering or entanglement concentration, using standard Bell-state measurement and classical communication one cannot teleport the state with unit fidelity and unit probability. We show that using non-maximally entangled measurements one can teleport an unknown state with unit fidelity albeit with reduced probability, hence probabilistic teleportation. We also give a generalized protocol for entanglement swapping using non-maximally entangled states.

E-commerce platforms are increasingly reliant on recommendation systems to enhance user experience, retain customers, and, in most cases, drive sales. The integration of machine learning methods into these systems has significantly improved their efficiency, personalization, and scalability. This paper aims to highlight the current trends in e-commerce recommendation systems, identify challenges, and evaluate the effectiveness of various machine learning methods used, including collaborative filtering, content-based filtering, and hybrid models. A systematic literature review (SLR) was conducted, analyzing 38 publications from 2013 to 2025. The methods used were evaluated and compared to determine their performance and effectiveness in addressing e-commerce challenges.

The IceCube Neutrino Observatory is an optical Cherenkov detector instrumenting one cubic kilometer of ice at the South Pole. The Cherenkov photons emitted following a neutrino interaction are detected by digital optical modules deployed along vertical strings within the ice. The densely instrumented bottom central region of the IceCube detector, known as DeepCore, is optimized to detect GeV-scale atmospheric neutrinos. As upward-going atmospheric neutrinos pass through Earth, matter effects alter their oscillation probabilities due to coherent forward scattering with ambient electrons. These matter effects depend upon the energy of neutrinos and the density distribution of electrons they encounter during their propagation. Using simulated data at the IceCube Deepcore equivalent to its 9.3 years of observation, we demonstrate that atmospheric neutrinos can be used to probe the broad features of the Preliminary Reference Earth Model. In this contribution, we present the preliminary sensitivities for establishing the Earth matter effects, validating the non-homogeneous distribution of Earth's electron density, and measuring the mass of Earth. Further, we also show the DeepCore sensitivity to perform the correlated density measurement of different layers incorporating constraints on Earth's mass and moment of inertia.

The ability of large language models (LLMs) to interpret visual representations of data is crucial for advancing their application in data analysis and decision-making processes. This paper presents a novel synthetic dataset designed to evaluate the proficiency of LLMs in interpreting various forms of data visualizations, including plots like time series, histograms, violins, boxplots, and clusters. Our dataset is generated using controlled parameters to ensure comprehensive coverage of potential real-world scenarios. We employ multimodal text prompts with questions related to visual data in images to benchmark several state-of-the-art models like ChatGPT or Gemini, assessing their understanding and interpretative accuracy. To ensure data integrity, our benchmark dataset is generated automatically, making it entirely new and free from prior exposure to the models being tested. This strategy allows us to evaluate the models' ability to truly interpret and understand the data, eliminating possibility of pre-learned responses, and allowing for an unbiased evaluation of the models' capabilities. We also introduce quantitative metrics to assess the performance of the models, providing a robust and comprehensive evaluation tool. Benchmarking several state-of-the-art LLMs with this dataset reveals varying degrees of success, highlighting specific strengths and weaknesses in interpreting diverse types of visual data. The results provide valuable insights into the current capabilities of LLMs and identify key areas for improvement. This work establishes a foundational benchmark for future research and development aimed at enhancing the visual interpretative abilities of language models. In the future, improved LLMs with robust visual interpretation skills can significantly aid in automated data analysis, scientific research, educational tools, and business intelligence applications.

We theoretically investigate the generation and Josephson current signatures of Floquet Majorana end modes (FMEMs) in a periodically driven altermagnet (AM) heterostructure. Considering a one-dimensional (1D) Rashba nanowire (RNW) proximitized to a regular $s$-wave superconductor and a $d$-wave AM, we generate both $0$- and $\pi$-FMEMs by driving the nontopological phase of the static system. While the static counterpart hosts both topological Majorana zero modes (MZMs) and non-topological accidental zero modes (AZMs), the drive can gap out the static AZMs and generate robust $\pi$-FMEMs, termed as topological AZMs (TAZMs). We topologically characterize the emergent FMEMs via dynamical winding numbers exploiting chiral symmetry of the system. Moreover, we consider a periodically driven Josephson junction comprising of RNW/AM-based 1D topological superconduting setup. We identify the signature of MZMs and FMEMs utilizing $4\pi$-periodic Josephson effect, distinguishing them from trivial AZMs exhibiting $2\pi$-periodicty, in both static and driven platforms. This Josephson current signal due to Majorana modes survives even in presence of finite disorder. Our work establishes a route to realize and identify FMEMs in AM-based platforms through Floquet engineering and Josephson current response.

The sensitivity of low dimensional superconductors to fluctuations gives rise to emergent behaviors beyond the conventional Bardeen Cooper Schrieffer framework. Anisotropy is one such manifestation, often linked to spatially modulated electronic states and unconventional pairing mechanisms. Pronounced in plane anisotropy recently reported at KTaO3 based oxide interfaces points to the emergence of a stripe order in superconducting phase, yet its microscopic origin and formation pathway remain unresolved. Here, we show that controlled interfacial disorder in MgO/KTaO3(111) heterostructures drives a percolative evolution from localized Cooper-pair islands to superconducting puddles and eventually to stripes. The extracted stripe width matches the spin precession length, suggesting a self organized modulation governed by spin orbit coupling and lattice-symmetry breaking. These findings identify disorder as both a tuning parameter and a diagnostic probe for emergent superconductivity in two dimensional quantum materials.