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Gaussian process (GP) modeling is widely used in computational science and engineering. However, fitting a GP to high-dimensional inputs remains challenging due to the curse of dimensionality. While various methods have been proposed to reduce input dimensionality, they typically follow a two-stage approach, performing dimension reduction and GP fitting separately. We introduce a Bayesian framework that seamlessly integrates dimensionality reduction with GP modeling and inference. Our approach, built on a hierarchical Bayesian model with priors on the Stiefel manifold, enforces orthonormality on the projection matrix and enables posterior inference via Hamiltonian Monte Carlo with geodesic flow. Additionally, we extend this framework by incorporating Deep Gaussian Processes (DGP) with built-in dimension reduction, providing a more flexible and powerful tool for complex datasets. Through extensive numerical studies, we demonstrate that while the proposed Bayesian method incurs higher computational costs, it improves predictive performance and uncertainty quantification, providing a principled and robust alternative to existing methods.

Background: The hazard ratio of the Cox proportional hazards model is widely used in randomized controlled trials to assess treatment effects. However, two properties of the hazard ratio including the non-collapsibility and built-in selection bias need to be further investigated. Methods: We conduct simulations to differentiate the non-collapsibility effect and built-in selection bias from the difference between the marginal and the conditional hazard ratio. Meanwhile, we explore the performance of the Cox model with inverse probability of treatment weighting for covariate adjustment when estimating the marginal hazard ratio. The built-in selection bias is further assessed in the period-specific hazard ratio. Results: The conditional hazard ratio is a biased estimate of the marginal effect due to the non-collapsibility property. In contrast, the hazard ratio estimated from the inverse probability of treatment weighting Cox model provides an unbiased estimate of the true marginal hazard ratio. The built-in selection bias only manifests in the period-specific hazard ratios even when the proportional hazards assumption is satisfied. The Cox model with inverse probability of treatment

Altermagnetic materials have attracted a lot of attention recently due to the numerous effects, which have an application potential and occur due to the spin-split band structure coexisting with the compensated magnetic order. Incorporation of such intriguing compounds into low-dimensional structures represents an important avenue towards exploiting and enhancing their functionalities. Prominent examples of this group are semiconductors well suited to the band-gap engineering strategies. Here, we present for the first time visible-light-emitting CdSe quantum wells, in which wurtzite MnSe as an alermagnetic candidate plays the role of a barrier. Photoluminescence experiments with temporal resolution demonstrate that in such quantum wells, a built-in electric field is present and strongly influences the energies of the emitted photons, the dynamics of recombination, and excitation power dependence. Numerical simulations allow us to estimate that the magnitude of the electric field is 14MV/m. We anticipate that such quantum wells offer potential to probe the barrier properties and that wurtzite MnSe is an interesting platform to study the interplay of the altermagnetism and built-in e

While gesture recognition using vision or robot skins is an active research area in Human-Robot Collaboration (HRC), this paper explores deep learning methods relying solely on a robot's built-in joint sensors, eliminating the need for external sensors. We evaluated various convolutional neural network (CNN) architectures and collected a dataset to study the impact of data representation and model architecture on the recognition accuracy. Our results show that spectrogram-based representations significantly improve accuracy, while model architecture plays a smaller role. We also tested generalization to new robot poses, where spectrogram-based models performed better. Implemented on a Franka Emika Research robot, two of our methods, STFT2DCNN and STT3DCNN, achieved over 95% accuracy in contact detection and gesture classification. These findings demonstrate the feasibility of external-sensor-free tactile recognition and promote further research toward cost-effective, scalable solutions for HRC.

Customized built-in elements in HTML5 significantly transform web development. These elements enable developers to create unique HTML components tailored with specific design and purpose. Customized built-in elements enable developers to address the unique needs of web applications more quickly, supporting consistent user interfaces and experiences across diverse digital platforms. This study investigates the role of these features in Component-Based Software Engineering (CBSE) and Design Systems, emphasizing the benefits of code modularity, reusability, and scalability in web development. Customized built-in elements enable developers to address the unique needs of web applications more quickly, supporting consistent user interfaces and experiences across diverse digital platforms. The paper also discusses the difficulties and concerns that must be addressed when creating customized built-in elements, such as browser compatibility, performance optimization, accessibility, security, styling, and interoperability. It emphasizes the importance of standardization, developer tooling, and community interaction in order to fully realize the potential of these features. Looking ahead, cus

We present Chunked Augmented Generation (CAG), an architecture specifically designed to overcome the context window limitations of Google Chrome's built-in Gemini Nano model. While Chrome's integration of Gemini Nano represents a significant advancement in bringing AI capabilities directly to the browser, its restricted context window poses challenges for processing large inputs. CAG addresses this limitation through intelligent input chunking and processing strategies, enabling efficient handling of extensive content while maintaining the model's performance within browser constraints. Our implementation demonstrates particular efficacy in processing large documents and datasets directly within Chrome, making sophisticated AI capabilities accessible through the browser without external API dependencies. Get started now at https://github.com/vivekVells/cag-js.

Strain engineering is an efficient tool to tune and tailor the electrical and optical properties of 2D materials. The built-in strain can be tuned during the synthesis process of a two dimensional semiconductor, as molybdenum disulfide, by employing different growth substrate with peculiar thermal properties. In this work we demonstrate that the built-in strain of MoS2 monolayers, grown on SiO2/Si substrate using liquid precursors chemical vapor deposition, is mainly dominated by the size of the monolayer. In fact, we identify a critical size equal to 20 um, from which the built-in strain increases drastically. The built-in strain is maximized for 60 um sized monolayer, leading to 1.2% tensile strain with a partial release of strain close to the monolayer triangular vertexes due to formation of nanocracks. These findings also imply that the standard method for evaluation of the number of layers based on the Raman modes separation becomes unreliable for monolayer with a lateral size above 20 um.

A convincing feature of least-squares finite element methods is the built-in a posteriori error estimator for any conforming discretization. In order to generalize this property to discontinuous finite element ansatz functions, this paper introduces a least-squares principle on piecewise Sobolev functions by the example of the Poisson model problem with mixed boundary conditions. It allows for fairly general discretizations including standard piecewise polynomial ansatz spaces on triangular and polygonal meshes. The presented scheme enforces the interelement continuity of the piecewise polynomials by additional least-squares residuals. A side condition on the normal jumps of the flux variable requires a vanishing integral mean and enables the penalization of the jump with the natural power of the mesh size in the least-squares functional. This avoids over-penalization with additional regularity assumptions on the exact solution as usually present in the literature on discontinuous LSFEM. The proof of the built-in a posteriori error estimation for the over-penalized scheme is presented as well. All results in this paper are robust with respect to the size of the domain guaranteed by

Built-in electric fields across heterojunctions between semiconducting materials underpin the functionality of modern device technologies. Heterojunctions between semiconductors and epitaxially grown crystalline oxides provide a rich setting in which built-in fields can be explored. Here, we present an electrical transport and hard X-ray photoelectron spectroscopy study of epitaxial SrNbxTi1-xO3-δ / Si heterojunctions. A non-monotonic anomaly in the sheet resistance is observed near room temperature, which is accompanied by a crossover in sign of the Hall resistance. The crossover is consistent with the formation of a hole gas in the Si and the presence of a built-in field. Hard X-ray photoelectron spectroscopy measurements reveal pronounced asymmetric features in both the SrNbxTi1-xO3-δ and Si core-level spectra that we show arise from built-in fields. The extended probe depth of hard X-ray photoelectron spectroscopy enables band bending across the SrNbxTi1-xO3-δ / Si heterojunction to be spatially mapped. Band alignment at the interface and surface depletion in SrNbxTi1-xO3-δ are implicated in the formation of the hole gas and built-in fields. Control of charge transfer and built

Property-based testing is a powerful method to validate program correctness. It is, however, not widely use in industry as the barrier of entry can be very high. One of the hindrances is to write the generators that are needed to generate randomised input data. Program properties often take complicated data structures as inputs and, it requires a significant amount of effort to write generators for such structures in a invariant preserving way. In this paper, we suggest and formalise a new programming language \textsf{pun}; a simple functional programming with properties as a built-in mechanism for program validation. We show how to generate input for \textsf{pun} properties automatically, thus, providing the programmer with a low barrier of entry for using property-based testing. We evaluate our work a on library for binary search trees and compare the test results to a similar library in Haskell.

Document retrieval is a key stage of standard Web search engines. Existing dual-encoder dense retrievers obtain representations for questions and documents independently, allowing for only shallow interactions between them. To overcome this limitation, recent autoregressive search engines replace the dual-encoder architecture by directly generating identifiers for relevant documents in the candidate pool. However, the training cost of such autoregressive search engines rises sharply as the number of candidate documents increases. In this paper, we find that large language models (LLMs) can follow human instructions to directly generate URLs for document retrieval. Surprisingly, when providing a few {Query-URL} pairs as in-context demonstrations, LLMs can generate Web URLs where nearly 90\% of the corresponding documents contain correct answers to open-domain questions. In this way, LLMs can be thought of as built-in search engines, since they have not been explicitly trained to map questions to document identifiers. Experiments demonstrate that our method can consistently achieve better retrieval performance than existing retrieval approaches by a significant margin on three open-d

Ultrafiltration membrane modules suffer from a permeate flow decrease arising during filtration and caused by concentration polarization and fouling in, e.g., fermentation broth purification. Such performance losses are frequently mitigated by manipulating the hydrodynamic conditions at the membrane-fluid interface using, e.g., mesh spacers acting as static mixers. This additional element increases manufacturing complexity while improving mass transport in general, yet accepting their known disadvantages such as less transport in dead zones. However, the shape of such spacers is limited to the design of commercially available spacer geometries. Here, we present a methodology to design an industrially relevant mini-module with an optimized built-in 3D spacer structure in a flat-sheet ultrafiltration membrane module to eliminate the spacer as a separate part. Therefore, the built-in structures have been conceptually implemented through an in-silico design in compliance with the specifications for an injection molding process. Ten built-in structures were investigated in a digital twin of the mini-module by 3D-CFD simulations to select two options, which were then compared to the empt

Ionic crystals terminated at oppositely charged polar surfaces are inherently unstable and expected to undergo surface reconstructions to maintain electrostatic stability. Essentially, an electric field that arises between oppositely charged atomic planes gives rise to a built-in potential that diverges with thickness. In ultra thin film form however the polar crystals are expected to remain stable without necessitating surface reconstructions, yet the built-in potential has eluded observation. Here we present evidence of a built-in potential across polar \lao ~thin films grown on \sto ~substrates, a system well known for the electron gas that forms at the interface. By performing electron tunneling measurements between the electron gas and a metallic gate on \lao ~we measure a built-in electric field across \lao ~of 93 meV/Å. Additionally, capacitance measurements reveal the presence of an induced dipole moment near the interface in \sto, illuminating a unique property of \sto ~substrates. We forsee use of the ionic built-in potential as an additional tuning parameter in both existing and novel device architectures, especially as atomic control of oxide interfaces gains widespread

The nanowire-supported quantum dot (NWQD) of GaN is an unconventional nanostructure, which is extremely promising for realization of UV photonics in general, and room-temperature single photon generation, in particular. While GaN-NWQDs have several promising attributes, the crucial challenge in exploiting their full potential, is to reduce the lateral dimensions of the QDs, to the order of the exciton Bohr-radius in GaN. Also critical is to suppress the built-in electric field due to spontaneous and piezoelectric polarization, which adversely affects the radiative recombination lifetime. We report here the innovation of a simple yet powerful single-step epitaxial growth technique, to achieve both of these targets. By combining controlled and on-demand thermal decomposition of GaN nanowires, with our previously-developed strategy of inhibiting the same via AlN-capping, we demonstrate that the NWQD-diameter can indeed be reduced to the truly strong-quantum-confinement limit. In these ultra-scaled GaN QDs, we show that the built-in electric fields are almost completely suppressed. The NWQD fabrication-strategy developed in this work may pave the way for fabrication of highly efficient

We analyse a sequential contest with two players in darts where one of the contestants enjoys a technical advantage. Using methods from the causal machine learning literature, we analyse the built-in advantage, which is the first-mover having potentially more but never less moves. Our empirical findings suggest that the first-mover has an 8.6 percentage points higher probability to win the match induced by the technical advantage. Contestants with low performance measures and little experience have the highest built-in advantage. With regard to the fairness principle that contestants with equal abilities should have equal winning probabilities, this contest is ex-ante fair in the case of equal built-in advantages for both competitors and a randomized starting right. Nevertheless, the contest design produces unequal probabilities of winning for equally skilled contestants because of asymmetries in the built-in advantage associated with social pressure for contestants competing at home and away.

Momentum resolved 4D-STEM, also called center of mass (CoM) analysis, has been used to measure the long range built-in electric field of a silicon p-n junction. The effect of different STEM modes and the trade-off between spatial resolution and electric field sensitivity are studied. Two acquisition modes are compared: nanobeam and low magnification (LM) modes. A thermal noise free Medipix3 direct electron detector with high speed acquisition has been used to study the influence of low electron beam current and millisecond dwell times on the measured electric field and standard deviation. It is shown that LM conditions can underestimate the electric field values due to a bigger probe size used but provide an improvement of almost one order of magnitude on the signal-to-noise ratio, leading to a detection limit of 0.011MV/cm. It is observed that the CoM results do not vary with acquisition time or electron dose as low as 24 $e^-/A^2$, showing that the electron beam does not influence the built-in electric field and that this method can be robust for studying beam sensitive materials, where a low dose is needed.

The contribution of a built-in electric field to ferroelectric phase transition in asymmetric ferroelectric tunnel junctions is studied using a multiscale thermodynamic model. It is demonstrated in details that there exists a critical thickness at which an unusual ferroelectric-\'\' polar non-ferroelectric\rq\rq phase transition occurs in asymmetric ferroelectric tunnel junctions. In the \'\' polar non-ferroelectric\rq\rq phase, there is only one non-switchable polarization which is caused by the competition between the depolarizing field and the built-in field, and closure-like domains are proposed to form to minimize the system energy. The transition temperature is found to decrease monotonically as the ferroelectric barrier thickness is decreased and the reduction becomes more significant for the thinner ferroelectric layers. As a matter of fact, the built-in electric field does not only result in smearing of phase transition but also forces the transition to take place at a reduced temperature. Such findings may impose a fundamental limit on the work temperature and thus should be further taken into account in the future ferroelectric tunnel junction-type or ferroelectric capac

We report on observation of a light-induced switching of the conductance in the back-gated organic field-effect transistors (OFETs) with built-in conduction channel. In the studied devices, the built-in channel is formed owing to the self-sensitized photo-oxidation of rubrene surface. In the dark, the back gate controls the charge injection from metal contacts into the built-in channel: the high-current ON state corresponds to zero or negative back-gate voltage; the low-current OFF state - to a positive back-gate voltage that blocks the Schottky contacts. Illumination of the OFET in the OFF state with a short pulse of light switches the device into the ON state that persists in the dark for days. The OFF state can be restored by cycling the back gate voltage. The observed effect can be explained by screening of the back-gate electric field by the charges photo-generated in the bulk of organic semiconductor.