Search

The $J/\psi$ and $\psi(2S)$ charmonium states, composed of $c\bar{c}$ quark pairs and known since the 1970s, are widely believed to serve as ideal probes to test quantum chromodynamics in high-energy hadronic interactions. However, there is not yet a complete understanding of the charmonium-production mechanism. Recent measurements of $J/\psi$ production as a function of event charged-particle multiplicity at the collision energies of both the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider (RHIC) show enhanced $J/\psi$ production yields with increasing multiplicity. One potential explanation for this type of dependence is multiparton interactions (MPI). We carry out the first measurements of self-normalized $J/\psi$ yields and the $\psi(2S)$ to $J/\psi$ ratio at both forward and backward rapidities as a function of self-normalized charged-particle multiplicity in $p$$+$$p$ collisions at $\sqrt{s}=200$ GeV. In addition, detailed {\sc pythia} studies tuned to RHIC energies were performed to investigate the MPI impacts. We find that the PHENIX data at RHIC are consistent with recent LHC measurements and can only be described by {\sc pythia} calculations that include MPI effects. The forward and backward $\psi(2S)$ to $J/\psi$ ratio, which serves as a unique and powerful approach to study final-state effects on charmonium production, is found to be less dependent on the charged-particle multiplicity., Comment: 301 authors from 69 institutions, 8 pages, 3 figures. v1 is version submitted to Physical Review D Letters. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

Consider a non-linear operator equation $x - K(x) = f$, where $f$ is given and $K$ is a Urysohn integral operator with Green's function type kernel defined on $L^\infty [0, 1]$. We apply methods of approximation based on interpolatory projections (where interpolation points are not necessarily Gauss points) and get solutions with higher accuracy than the collocation solution of the above equation. Numerical examples are given to support our theoretical results.

The imposition of a global constraint of the conservation of total kinetic energy on a forced-dissipative Burgers equation yields a governing equation that is invariant under the time-reversal symmetry operation, $\{\mathcal{T}: t \to -t; u \to -u \}$, where $u$ is the velocity field. Moreover, the dissipation term gets strongly modified, as the viscosity is no longer a constant, but a fluctuating, state dependent quantity, which can even become negative in certain dynamical regimes. Despite these differences, the statistical properties of different dynamical regimes of the time-reversible Burgers equation and the standard forced-dssipative Burgers equation are equivalent, \`a la Gallavotti's conjecture of \textit{equivalence of nonequilibrium ensembles}. We show that the negative viscosity events occur only in the thermalized regime described by the time-reversible equation. This quasi-equilibrium regime is examined by calculating the local Lyapunov spectra and fluctuation relations. A pairing symmetry among the spectra is observed, indicating that the dynamics is chaotic and has an attractor spanning the entire phase space of the system. The violations of the second law of thermodynamics are found to be in accordance with the fluctuation relations, namely the Gallavotti-Cohen relation based on the phase-space contraction rate and the Cohen-Searles fluctuation relation based on the energy production rate. It is also argued that these violations are associated with the effects of the Galerkin-truncation, the latter is responsible for the thermalization.

In the pursuit of an effective spam detection system, the focus has often been on identifying known spam patterns either through rule-based detection systems or machine learning (ML) solutions that rely on keywords. However, both systems are susceptible to evasion techniques and zero-day attacks that can be achieved at low cost. Therefore, an email that bypassed the defense system once can do it again in the following days, even though rules are updated or the ML models are retrained. The recurrence of failures to detect emails that exhibit layout similarities to previously undetected spam is concerning for customers and can erode their trust in a company. Our observations show that threat actors reuse email kits extensively and can bypass detection with little effort, for example, by making changes to the content of emails. In this work, we propose an email visual similarity detection approach, named Pisco, to improve the detection capabilities of an email threat defense system. We apply our proof of concept to some real-world samples received from different sources. Our results show that email kits are being reused extensively and visually similar emails are sent to our customers at various time intervals. Therefore, this method could be very helpful in situations where detection engines that rely on textual features and keywords are bypassed, an occurrence our observations show happens frequently., Comment: To be published in the proceedings of the ACM Conference on Computer and Communications Security (ACM CCS 2024)

Space debris has become a critical issue, with debris in orbit surpassing active satellites, posing significant risks to space sustainability. Payloads or rocket bodies discarded post-mission in LEO without orbital control are major sources. The IADC guidelines recommend limiting post-mission presence in protected regions to 25 years. The FCC recently introduced stricter regulations, reducing the allowable post-mission stay for LEO satellites to 5 years. These changes necessitate integrating deorbiting systems into satellite designs. However, adding extra fuel and engines for active deorbiting presents challenges due to LEO satellites' mass and volume limitations, especially for large constellations or CubeSats. This often leads to prioritizing mission-critical components over deorbiting systems. Thus, alternative approaches like passive deorbiting techniques or international regulations are explored. Drag sails are a cost-effective passive solution for small and medium-sized LEO satellites. This paper proposes a plug-and-play drag sail module using COTS components for CubeSats and sub-mass satellites. The scalable design is derived from mission requirements and trajectory analysis. The technique includes active control for quicker deorbiting at specific orbital LTAN. Inspired by JAXA's IKAROS mission, the deployment mechanism uses residual angular momentum and follows a standard sequence. A cost analysis estimates the system's breakeven point. A prototype with a 3D-printed deployment system and inverted stepper motor was tested and compared to a numerical model. A tension model for sail extension petals was developed using curve fitting from test data. SIMULINK multibody models are available for simulations. Further experimentation and prototype development are required to assess real-world performance, with a control system identified as crucial.

We report on a new quantum state characterisation method, which we call Rabi-based Quantum State Tomography (RQST), that we have validated on single-qubit quantum states, in particular on the electron and nuclear spins of a single nitrogen-vacancy (NV) centre in diamond, demonstrating high fidelities. The difference of RQST with conventional tomography methods is in the implementation of rotation operators and construction of density matrix from the measured data sets. We demonstrate efficient quantum state control of the electron spin at room temperature with an average fidelity of 0.995 over more than 40 measurements on different states on the Bloch sphere with a maximum fidelity of 0.99992. Also, we apply the methodology to the dark NV nuclear spin state. The state is read via the electron spin using the C-NOT two-qubit entanglement gate and demonstrate fidelities of the same order., Comment: 8 pages, 6 figures. arXiv admin note: substantial text overlap with arXiv:2312.14310

The jet cross-section and jet-substructure observables in $p$$+$$p$ collisions at $\sqrt{s}=200$ GeV were measured by the PHENIX Collaboration at the Relativistic Heavy Ion Collider (RHIC). Jets are reconstructed from charged-particle tracks and electromagnetic-calorimeter clusters using the anti-$k_{t}$ algorithm with a jet radius $R=0.3$ for jets with transverse momentum within $8.0

A face morph is created by combining the face images usually pertaining to two distinct identities. The goal is to generate an image that can be matched with two identities thereby undermining the security of a face recognition system. To deal with this problem, several morph attack detection techniques have been developed. But these methods do not extract any information about the underlying bonafides used to create them. Demorphing addresses this limitation. However, current demorphing techniques are mostly reference-based, i.e, they need an image of one of the identities to recover the other. In this work, we treat demorphing as an ill-posed decomposition problem. We propose a novel method that is reference-free and recovers the bonafides with high accuracy. Our method decomposes the morph into several identity-preserving feature components. A merger network then weighs and combines these components to recover the bonafides. Our method is observed to reconstruct high-quality bonafides in terms of definition and fidelity. Experiments on the CASIA-WebFace, SMDD and AMSL datasets demonstrate the effectiveness of our method.

Without adhering to any specific model, we have presented 4 X 4 quark mixing matrix as an extension of the 3 X 3 PDG parametrization of the CKM matrix. Using unitarity constraints as well as the hierarchy among the elements of the 3 X 3 CKM matrix, we have found the hierarchy among the 4th row and 4th column elements of the 4 X 4 quark mixing matrix. Further, for the fourth generation case, we have explicitly found the 9 independent rephasing invariant parameters J_4X4. Also, using phenomenological estimates of the 4th row and 4th column elements, we have numerically evaluated these 9 parameters., Comment: 16 pages

Recently, transition metal dichalcogenides (TMD) have attracted a lot of attention because they can host a number of novel phases, including charge density wave and topological superconductivity. This class of compounds is significant from application and fundamental standpoints. Here we report on the synthesis and characterization of Janus TMD compound TiSeS, which is a subclass of TMDs. We observe characters of filamentary superconductivity in TiSeS, which was not reported in earlier studies. Magnetization measurements (ZFCW& FCW) as well as isothermal magnetization indicates towards type-II superconductivity whereas transport measurements lacks of zero resistivity due to possible weak inter-grain connectivity. The observed behavior can be safely attributed to filamentary superconductivity in TiSeS. Further studies need to be carried out to determine the origin of this superconducting phase.

Capitalizing on the recent availability of ERA5 monthly averaged long-term data records of mean atmospheric and climate fields based on high-resolution reanalysis, deep-learning architectures offer an alternative to physics-based daily numerical weather predictions for subseasonal to seasonal (S2S) and annual means. A novel Deep UNet++-based Ensemble (DUNE) neural architecture is introduced, employing multi-encoder-decoder structures with residual blocks. When initialized from a prior month or year, this architecture produced the first AI-based global monthly, seasonal, or annual mean forecast of 2-meter temperatures (T2m) and sea surface temperatures (SST). ERA5 monthly mean data is used as input for T2m over land, SST over oceans, and solar radiation at the top of the atmosphere for each month of 40 years to train the model. Validation forecasts are performed for an additional two years, followed by five years of forecast evaluations to account for natural annual variability. AI-trained inference forecast weights generate forecasts in seconds, enabling ensemble seasonal forecasts. Root Mean Squared Error (RMSE), Anomaly Correlation Coefficient (ACC), and Heidke Skill Score (HSS) statistics are presented globally and over specific regions. These forecasts outperform persistence, climatology, and multiple linear regression for all domains. DUNE forecasts demonstrate comparable statistical accuracy to NOAA's operational monthly and seasonal probabilistic outlook forecasts over the US but at significantly higher resolutions. RMSE and ACC error statistics for other recent AI-based daily forecasts also show superior performance for DUNE-based forecasts. The DUNE model's application to an ensemble data assimilation cycle shows comparable forecast accuracy with a single high-resolution model, potentially eliminating the need for retraining on extrapolated datasets., Comment: Excluding Appendix: 18 pages, 14 figures

The present study investigates the catalytic performance of a $hg-C_{3}N_{4}$ quantum dot aimed at enhancing electrochemical water splitting, using the first-principles density functional theory. The size of the considered quantum dot lies within the range reported experimentally (2nm - 4nm) [Zhou et al. ACS Nano 9, 12480 (2015)]. The nitrogen vacancies are created in the considered $hg-C_{3}N_{4}$ structure to simulate the realistic scenario, as the presence of nitrogen and carbon defects are reported in the synthesized $hg-C_{3}N_{4}$ quantum dots. First, the structural and vibrational properties are computed to ensure the stability of the nitrogen deficient $hg-C_{3}N_{4}$ quantum dots, and subsequently, their electronic and hydrogen evolution reaction (HER) properties are investigated. The calculate HER parameters, i.e., adsorption energies, Gibbs free energies, and overpotentials demonstrate that the considered $hg-C_{3}N_{4}$ quantum dot with nitrogen vacancies can be used as a moderately effective electrocatalyst for HER performance. We also considered the quantum dot to be dissolved in water and ethanol, and find that the overpotential gets drastically reduced to 16 mV for the alcohol dissolved quantum dot, while some significant reduction is seen for the aqueous solution also. As a result, this study suggests that the nitrogen-deficient $hg-C_{3}N_{4}$ quantum dots dissolved in ethanol are excellent candidates for catalysis aimed at sustainable hydrogen production via electrochemical water splitting., Comment: 36 pages, 9 figures

One of the current goals of neutrino experiments is to precisely determine standard unknown oscillation parameters such as the leptonic CP phase and mass hierarchy. Lorentz invariance violation represents a potential physics factor that could influence the experiment's ability to achieve these precise determinations. This study investigates the influence of Lorentz invariance violation (LIV) on oscillation dynamics, particularly through non-isotropic CPT-violating ($a^{X}_{e\mu}$, $a^{X}_{e\tau}$, $a^{X}_{\mu\tau}$) and CPT-conserving ($c^{XY}_{e\mu}$, $c^{XY}_{e \tau}$, $c^{XY}_{\mu \tau}$) parameters within the Deep Underground Neutrino Experiment (DUNE). We analyze the impact of these parameters on the mass hierarchy (MH) and Dirac CP phase sensitivity measurements. Our findings indicate that while MH sensitivity remains relatively unaffected, only the presence of $c^{XY}_{\mu \tau}$ significantly deteriorates MH sensitivity, albeit remaining above the $5 \sigma$ threshold. Additionally, we observe a substantial compromise in CP sensitivity due to the $c^{XY}_{e \mu}$ and $c^{XY}_{e \tau}$ parameters.

The hierarchy amongst the CKM matrix elements, highlighted recently by Luo and Xing, has been rigorously revisited using the PDG parameterization incorporating unitarity constraints. Further, we have explored the evaluation of the CP violating parameter \epsilon_k for the 9 possible unitarity ensured equivalent independent parametrizations of CKM matrix. Interestingly, we find that not all of these reproduce the value of parameter \epsilon_k, this being presumably due to the hierarchical nature of the CKM matrix elements. This situation is echoing similar conclusions regarding the evaluation of Jarlskog's rephasing invariant parameter J through unitarity ensured equivalent possibilities., Comment: 13 pages. arXiv admin note: text overlap with arXiv:2310.11152

Multiphysics problems that are characterized by complex interactions among fluid dynamics, heat transfer, structural mechanics, and electromagnetics, are inherently challenging due to their coupled nature. While experimental data on certain state variables may be available, integrating these data with numerical solvers remains a significant challenge. Physics-informed neural networks (PINNs) have shown promising results in various engineering disciplines, particularly in handling noisy data and solving inverse problems. However, their effectiveness in forecasting nonlinear phenomena in multiphysics regimes is yet to be fully established. This study introduces NeuroSEM, a hybrid framework integrating PINNs with the high-fidelity Spectral Element Method (SEM) solver, Nektar++. NeuroSEM leverages strengths of both PINNs and SEM, providing robust solutions for multiphysics problems. PINNs are trained to assimilate data and model physical phenomena in specific subdomains, which are then integrated into Nektar++. We demonstrate the efficiency and accuracy of NeuroSEM for thermal convection in cavity flow and flow past a cylinder. The framework effectively handles data assimilation by addressing those subdomains and state variables where data are available. We applied NeuroSEM to the Rayleigh-B\'enard convection system, including cases with missing thermal boundary conditions. Our results indicate that NeuroSEM accurately models the physical phenomena and assimilates the data within the specified subdomains. The framework's plug-and-play nature facilitates its extension to other multiphysics or multiscale problems. Furthermore, NeuroSEM is optimized for an efficient execution on emerging integrated GPU-CPU architectures. This hybrid approach enhances the accuracy and efficiency of simulations, making it a powerful tool for tackling complex engineering challenges in various scientific domains.

Time series forecasting in the air cargo industry presents unique challenges due to volatile market dynamics and the significant impact of accurate forecasts on generated revenue. This paper explores a comprehensive approach to demand forecasting at the origin-destination (O\&D) level, focusing on the development and implementation of machine learning models in decision-making for the air cargo industry. We leverage a mixture of experts framework, combining statistical and advanced deep learning models to provide reliable forecasts for cargo demand over a six-month horizon. The results demonstrate that our approach outperforms industry benchmarks, offering actionable insights for cargo capacity allocation and strategic decision-making in the air cargo industry. While this work is applied in the airline industry, the methodology is broadly applicable to any field where forecast-based decision-making in a volatile environment is crucial., Comment: The 10th Mining and Learning from Time Series Workshop: From Classical Methods to LLMs. SIGKDD, Barcelona, Spain, 6 page

In the context of four-dimensional type II supergravities, the successive application of various S/T-dualities leads to a generalized notion of fluxes, which includes certain (non-)geometric fluxes along with the standard RR and NS-NS p-form fluxes. These fluxes induce a diverse set of superpotential couplings leading to scalar potentials with a very rich structure, which may possibly result in a vast landscape of physical vacua. However such scalar potentials typically consist of a huge number of terms and in order to make any attempt for phenomenological model building with some analytic understanding/control it is necessary to formulate them in some compact and concise form. Along these lines we review various equivalent methods of deriving the same scalar potential through a set of master formulae, which may open up a new avenue for model building using non-geometric fluxes., Comment: 26 pages; Contribution to Proceedings for the "Workshop on the Standard Model and Beyond", Corfu Summer Institute, 2023 (CORFU2023-SM). arXiv admin note: substantial text overlap with arXiv:2308.15529

Heisenberg uncertainty relation is at the origin of understanding minimum uncertainty states and squeezed states of light. In the recent past, sum uncertainty relation was formulated by Maccone and Pati [Phys. Rev. Lett. 113, 260401 (2014)] which is claimed to be stronger than the existing Heisenberg-Robertson product uncertainty relation for the set of two incompatible observables. We deduce a different sum uncertainty relation that is weaker than the previous but necessary and sufficient to define MUS for sum uncertainty relations. We claim that the MUS for the sum uncertainty relation is always the MUS for the traditional product uncertainty relation. This means that the definition of squeezed states remains completely unaffected by the stronger sum uncertainty relation., Comment: 4 pages, 2 figures

In the context of four-dimensional ${\cal N} = 1$ type IIB superstring compactifications, the U-dual completion of the holomorphic flux superpotential leads to four S-dual pairs of fluxes, namely $(F, H), (Q, P), (P', Q')$ and $(H', F')$. It has been observed that the scalar potentials induced by such generalized superpotentials typically have an enormous amount of terms, making it hard to study any phenomenological aspects such as moduli stabilization. In this regard, we present a set of generic master formulae which not only formulate the scalar potential in a compact way but also enable one to {\it read-off} the various scalar potential pieces by simply knowing a set of topological data of the compactifying Calabi Yau and its mirror threefold. We demonstrate the applicability of our master formulae by {\it reading-off} the scalar potentials for five explicit models, and using a set of {\it axionic flux} combinations we show that 76276 terms arising from the flux superpotential in a ${\mathbb T}^6/({\mathbb Z}_2\times{\mathbb Z}_2)$-based model can be equivalently expressed by using 2816 terms, while 11212 terms arising from the flux superpotential in a Quintic-based model can be equivalently expressed by 668 terms! We argue that the master formulae presented in this work can be useful in an analytic exploration of the rich landscape of the non-geometric flux vacua., Comment: 43 pages, 5 tables, typos fixed

The PHENIX experiment measured the centrality dependence of two-pion Bose-Einstein correlation functions in $\sqrt{s_{_{NN}}}=200$~GeV Au$+$Au collisions at the Relativistic Heavy Ion Collider at Brookhaven National Laboratory. The data are well represented by L\'evy-stable source distributions. The extracted source parameters are the correlation-strength parameter $\lambda$, the L\'evy index of stability $\alpha$, and the L\'evy-scale parameter $R$ as a function of transverse mass $m_T$ and centrality. The $\lambda(m_T)$ parameter is constant at larger values of $m_T$, but decreases as $m_T$ decreases. The L\'evy scale parameter $R(m_T)$ decreases with $m_T$ and exhibits proportionality to the length scale of the nuclear overlap region. The L\'evy exponent $\alpha(m_T)$ is independent of $m_T$ within uncertainties in each investigated centrality bin, but shows a clear centrality dependence. At all centralities, the L\'evy exponent $\alpha$ is significantly different from that of Gaussian ($\alpha=2$) or Cauchy ($\alpha=1$) source distributions. Comparisons to the predictions of Monte-Carlo simulations of resonance-decay chains show that in all but the most peripheral centrality class (50%-60%), the obtained results are inconsistent with the measurements, unless a significant reduction of the in-medium mass of the $\eta'$ meson is included. In each centrality class, the best value of the in-medium $\eta'$ mass is compared to the mass of the $\eta$ meson, as well as to several theoretical predictions that consider restoration of $U_A(1)$ symmetry in hot hadronic matter., Comment: 401 authors from 75 institutions, 20 pages, 15 figures, 2 tables. v1 is version submitted to Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

Enterprise chatbots, powered by generative AI, are emerging as key applications to enhance employee productivity. Retrieval Augmented Generation (RAG), Large Language Models (LLMs), and orchestration frameworks like Langchain and Llamaindex are crucial for building these chatbots. However, creating effective enterprise chatbots is challenging and requires meticulous RAG pipeline engineering. This includes fine-tuning embeddings and LLMs, extracting documents from vector databases, rephrasing queries, reranking results, designing prompts, honoring document access controls, providing concise responses, including references, safeguarding personal information, and building orchestration agents. We present a framework for building RAG-based chatbots based on our experience with three NVIDIA chatbots: for IT/HR benefits, financial earnings, and general content. Our contributions are three-fold: introducing the FACTS framework (Freshness, Architectures, Cost, Testing, Security), presenting fifteen RAG pipeline control points, and providing empirical results on accuracy-latency tradeoffs between large and small LLMs. To the best of our knowledge, this is the first paper of its kind that provides a holistic view of the factors as well as solutions for building secure enterprise-grade chatbots.", Comment: 8 pages, 6 figures, 2 tables, Preprint submission to ACM CIKM 2024

Deep learning methods have achieved a lot of success in various applications involving converting wearable sensor data to actionable health insights. A common application areas is activity recognition, where deep-learning methods still suffer from limitations such as sensitivity to signal quality, sensor characteristic variations, and variability between subjects. To mitigate these issues, robust features obtained by topological data analysis (TDA) have been suggested as a potential solution. However, there are two significant obstacles to using topological features in deep learning: (1) large computational load to extract topological features using TDA, and (2) different signal representations obtained from deep learning and TDA which makes fusion difficult. In this paper, to enable integration of the strengths of topological methods in deep-learning for time-series data, we propose to use two teacher networks, one trained on the raw time-series data, and another trained on persistence images generated by TDA methods. The distilled student model utilizes only the raw time-series data at test-time. This approach addresses both issues. The use of KD with multiple teachers utilizes complementary information, and results in a compact model with strong supervisory features and an integrated richer representation. To assimilate desirable information from different modalities, we design new constraints, including orthogonality imposed on feature correlation maps for improving feature expressiveness and allowing the student to easily learn from the teacher. Also, we apply an annealing strategy in KD for fast saturation and better accommodation from different features, while the knowledge gap between the teachers and student is reduced. Finally, a robust student model is distilled, which uses only the time-series data as an input, while implicitly preserving topological features., Comment: Engineering Applications of Artificial Intelligence 130, 107719

In this work, we propose a novel variational quantum approach for solving a class of nonlinear optimal control problems. Our approach integrates Dirac's canonical quantization of dynamical systems with the solution of the ground state of the resulting non-Hermitian Hamiltonian via a variational quantum eigensolver (VQE). We introduce a new perspective on the Dirac bracket formulation for generalized Hamiltonian dynamics in the presence of constraints, providing a clear motivation and illustrative examples. Additionally, we explore the structural properties of Dirac brackets within the context of multidimensional constrained optimization problems. Our approach for solving a class of nonlinear optimal control problems employs a VQE-based approach to determine the eigenstate and corresponding eigenvalue associated with the ground state energy of a non-Hermitian Hamiltonian. Assuming access to an ideal VQE, our formulation demonstrates excellent results, as evidenced by selected computational examples. Furthermore, our method performs well when combined with a VQE-based approach for non-Hermitian Hamiltonian systems. Our VQE-based formulation effectively addresses challenges associated with a wide range of optimal control problems, particularly in high-dimensional scenarios. Compared to standard classical approaches, our quantum-based method shows significant promise and offers a compelling alternative for tackling complex, high-dimensional optimization challenges., Comment: 39 pages

Surface plasmons (SP) and their mediated effects have been widely used to manipulate micro- and nanoscale objects of dielectric and metallic nature. In this work, we show how SP excitation can be used to induce thermofluidic and thermoelectric effects to manipulate colloidal dynamics on a large scale. In an evanescent plasmonic trap, temperature gradients induce fluid flow that can facilitate particle accumulation. However, large out-of-plane flows expel particles from the trap, resulting in a shallow trap potential. Here, we numerically demonstrate how adding thermoelectric fields can overpower the optical and hydrodynamic forces to achieve a stable nanoparticle assembly at low excitation powers. We calculate the corresponding optical, fluidic, and thermoelectric trapping forces and potentials. These potentials can be enabled without resonant SP excitation, which requires careful optical alignment. Thus, we explain the mechanism of how, despite weak optical intensities and forces, sufficient trapping force can be supplied via the evanescent optothermoelectric trap to obtain large-scale reversible nanoparticle assemblies, irrespective of their shape, size, or material., Comment: 21 pages, 4 figures

Platinum, Silver-Platinum embedded Zirconia (Pt/Ag-Pt ZrO2) thin films have been fabricated on silicon wafers and glass substrates using the plasma co-sputtering method. Zirconia thin films are of significant technological importance due to their remarkable electrical, optical, and mechanical properties, as well as their high melting temperature of 2715{\deg}C, which makes them increasingly attractive for various applications. In this study, ZrO2 thin films were deposited for 3 minutes, followed by the deposition of Pt-Ag/Pt onto the fabricated zirconia thin films, with deposition times ranging from 15 to 60 seconds. The varying deposition times of Pt-Ag/Pt influenced the optical and electronic properties of the thin films due to alterations in their surface roughness. The characteristics of the grown zirconia and Pt/Ag-Pt sputtered zirconia nanostructures were investigated using Atomic Force Microscopy (AFM), Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD), UV-visible spectroscopy, and Photoluminescence spectroscopy. The optical transmittance of these thin films was examined across the visible and near-infrared spectral ranges. The investigation revealed various properties, such as enhanced photoluminescence and the emergence of new peaks in the visible range spectra. Plasmonic peaks were induced, and an increase in the sharpness of these peaks was observed between 403.15 nm and 512.10 nm for the Pt/Ag-Pt deposited samples. This enhancement in photoluminescence is attributed to the plasmonic properties of Pt-Ag nanoparticles on the zirconia thin film. The study demonstrates that these optically tuned thin film coatings, with their enhanced photoluminescence properties, can significantly improve the heat-resistance capacity of devices, mitigating issues related to overheating and device shutdown., Comment: 8 figures, 1 table

This paper presents an enhancement to Grover's search algorithm for instances where the number of items (or the size of the search problem) $N$ is not a power of 2. By employing an efficient algorithm for the preparation of uniform quantum superposition states over a subset of the computational basis states, we demonstrate that a considerable reduction in the number of oracle calls (and Grover's iterations) can be achieved in many cases. For special cases (i.e., when $N$ is of the form such that it is slightly greater than an integer power of 2), the reduction in the number of oracle calls (and Grover's iterations) asymptotically approaches 29.33\%. This improvement is significant compared to the traditional Grover's algorithm, which handles such cases by rounding $N$ up to the nearest power of 2. The key to this improvement is our algorithm for the preparation of uniform quantum superposition states over a subset of the computational basis states, which requires gate complexity and circuit depth of only $ O (\log_2 (N)) $, without using any ancilla qubits., Comment: 6 pages

Consider a linear operator equation $x - Kx = f$, where $f$ is given and $K$ is a Fredholm integral operator with a Green's function type kernel defined on $C[0, 1]$. For $r \geq 0$, we employ the interpolatory projection at $2r + 1$ collocation points (not necessarily Gauss points) onto a space of piecewise polynomials of degree $\leq 2r$ with respect to a uniform partition of $[0, 1]$. Previous researchers have established that, in the case of smooth kernels with piecewise polynomials of even degree, iteration in the collocation method and its variants improves the order of convergence by projection methods. In this article, we demonstrate the improvement in order of convergence by modified collocation method when the kernel is of Green's function type.

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated., Comment: Prepared for submission to JINST

In this manuscript, a modified $R_I$ type recurrence relation is considered whose recurrence coefficients are perturbed by addition or multiplication of a constant. The perturbed system of recurrence coefficients is represented by Toda lattice equations, which are derived. These equations are then represented in a matrix form. With the help of this matrix representation, a known Lax pair is recovered. Inferences about the stability of resulting perturbed system of Toda equations are drawn based on numerical experiments., Comment: 15 pages, 19 figures

Despite being more secure and strongly promoted, two-factor (2FA) or multi-factor (MFA) schemes either fail to protect against recent phishing threats such as real-time MITM, controls/relay MITM, malicious browser extension-based phishing attacks, and/or need the users to purchase and carry other hardware for additional account protection. Leveraging the unprecedented popularity of NFC and BLE-enabled smartphones, we explore a new horizon for designing an MFA scheme. This paper introduces an advanced authentication method for user verification that utilizes the user's real-time facial biometric identity, which serves as an inherent factor, together with BLE- NFC-enabled mobile devices, which operate as an ownership factor. We have implemented a prototype authentication system on a BLE-NFC-enabled Android device, and initial threat modeling suggests that it is safe against known phishing attacks. The scheme has been compared with other popular schemes using the Bonneau et al. assessment framework in terms of usability, deployability, and security.

High-momentum two-particle correlations are a useful tool for studying jet-quenching effects in the quark-gluon plasma. Angular correlations between neutral-pion triggers and charged hadrons with transverse momenta in the range 4--12~GeV/$c$ and 0.5--7~GeV/$c$, respectively, have been measured by the PHENIX experiment in 2014 for Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$~GeV. Suppression is observed in the yield of high-momentum jet fragments opposite the trigger particle, which indicates jet suppression stemming from in-medium partonic energy loss, while enhancement is observed for low-momentum particles. The ratio and differences between the yield in Au$+$Au collisions and $p$$+$$p$ collisions, $I_{AA}$ and $\Delta_{AA}$, as a function of the trigger-hadron azimuthal separation, $\Delta\phi$, are measured for the first time at the Relativistic Heavy Ion Collider. These results better quantify how the yield of low-$p_T$ associated hadrons is enhanced at wide angle, which is crucial for studying energy loss as well as medium-response effects., Comment: 534 authors from 83 institutions, 12 pages, 7 figures. v1 is version submitted to Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

Imagine a robot that can assemble a functional product from the individual parts presented in any configuration to the robot. Designing such a robotic system is a complex problem which presents several open challenges. To bypass these challenges, the current generation of assembly systems is built with a lot of system integration effort to provide the structure and precision necessary for assembly. These systems are mostly responsible for part singulation, part kitting, and part detection, which is accomplished by intelligent system design. In this paper, we present autonomous assembly of a gear box with minimum requirements on structure. The assembly parts are randomly placed in a two-dimensional work environment for the robot. The proposed system makes use of several different manipulation skills such as sliding for grasping, in-hand manipulation, and insertion to assemble the gear box. All these tasks are run in a closed-loop fashion using vision, tactile, and Force-Torque (F/T) sensors. We perform extensive hardware experiments to show the robustness of the proposed methods as well as the overall system. See supplementary video at https://www.youtube.com/watch?v=cZ9M1DQ23OI., Comment: Under submission

Kolmogorov-Arnold Networks (KANs) were recently introduced as an alternative representation model to MLP. Herein, we employ KANs to construct physics-informed machine learning models (PIKANs) and deep operator models (DeepOKANs) for solving differential equations for forward and inverse problems. In particular, we compare them with physics-informed neural networks (PINNs) and deep operator networks (DeepONets), which are based on the standard MLP representation. We find that although the original KANs based on the B-splines parameterization lack accuracy and efficiency, modified versions based on low-order orthogonal polynomials have comparable performance to PINNs and DeepONet although they still lack robustness as they may diverge for different random seeds or higher order orthogonal polynomials. We visualize their corresponding loss landscapes and analyze their learning dynamics using information bottleneck theory. Our study follows the FAIR principles so that other researchers can use our benchmarks to further advance this emerging topic.

For a positive integer $k$, the $k$-cut complex of a graph $G$ is the simplicial complex whose facets are the $(|V(G)|-k)$-subsets $\sigma$ of the vertex set $V(G)$ of $G$ such that the induced subgraph of $G$ on $V(G) \setminus \sigma$ is disconnected. These complexes first appeared in the master thesis of Denker and were further studied by Bayer et al.\ in [Topology of cut complexes of graphs, SIAM Journal on Discrete Mathematics, 2024]. In the same article, Bayer et al.\ conjectured that for $k \geq 3$, the $k$-cut complexes of squared cycle graphs are shellable. Moreover, they also conjectured about the Betti numbers of these complexes when $k=3$. In this article, we prove these conjectures for $k=3$., Comment: 23 pages