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The classical and extended deficiency one theorems by Feinberg apply to reaction networks with mass-action kinetics that have independent linkage classes or subnetworks, each with a deficiency of at most one and exactly one terminal strong component. The theorems assume the existence of a positive equilibrium and guarantee the existence of a unique positive equilibrium in every stoichiometric compatibility class. In our work, we use the $\textit{monomial dependency}$ which extends the concept of deficiency. First, we provide a dependency one theorem for parametrized systems of polynomial equations that are essentially univariate and decomposable. As our main result, we present a corresponding theorem for mass-action systems, which permits subnetworks with arbitrary deficiency and arbitrary number of terminal strong components. Finally, to complete the picture, we derive the extended deficiency one theorem as a special case of our more general dependency one theorem., Comment: 29 pages, 3 figures

A parasitic reconfigurable antenna array is a low-power approach for beamforming using passive tunable elements. Prior work on reconfigurable antennas in communication theory is based on ideal radiation pattern abstractions. It does not address the problem of physical realizability. Beamforming with parasitic elements is inherently difficult because mutual coupling creates non-linearity in the beamforming gain objective. We develop a multi-port circuit-theoretic model of the hybrid array with parasitic elements and antennas with active RF chain validated through electromagnetic simulations with a dipole array. We then derive the beamforming weight of the parasitic element using the theoretical beam pattern expression for the case of a single active antenna and multiple parasitic elements. We show that the parasitic beamforming is challenging because the weights are subject to coupled magnitude and phase constraints. We simplify the beamforming optimization problem using a shift-of-origin transformation to the typical unit-modulus beamforming weight. With this transformation, we derive a closed-form solution for the reconfigurable parasitic reactance. We generalize this solution to the multi-active multi-parasitic hybrid array operating in a multi-path channel. Our proposed hybrid architecture with parasitic elements outperforms conventional architectures in terms of energy efficiency., Comment: Submitted to IEEE Transactions on Wireless Communications

This paper demonstrates the use of voltage noise thermometry, with a cross-correlation technique, as a dissipation-free method of thermometry inside a CMOS integrated circuit (IC). We show that this technique exhibits broad agreement with the refrigerator temperature range from 300 mK to 8 K. Furthermore, it shows substantial agreement with both an independent in-IC thermometry technique and a simple thermal model as a function of power dissipation inside the IC. As the device under test (DUT) is a resistor, it is feasible to extend this technique by placing many resistors in an IC to monitor the local temperatures, without increasing IC design complexity. This could lead to better understanding of the thermal profile of ICs at cryogenic temperatures. This has its greatest potential application in quantum computing, where the temperature at the cold classical-quantum boundary must be carefully controlled to maintain qubit performance.

We prove that if a given reaction network $\mathcal{N}$ has a weakly reversible deficiency zero realization for all choice of rate constants, then there exists a $\textit{unique}$ weakly reversible deficiency zero network $\mathcal{N}'$ such that $\mathcal{N}$ is realizable by $\mathcal{N}'$. Additionally, we propose an algorithm to find this weakly reversible deficiency zero network $\mathcal{N}'$ when it exists., Comment: 21 pages, 3 figures

Currency arbitrage capitalizes on price discrepancies in currency exchange rates between markets to produce profits with minimal risk. By employing a combinatorial optimization problem, one can ascertain optimal paths within directed graphs, thereby facilitating the efficient identification of profitable trading routes. This research investigates the methodologies of quantum annealing and gate-based quantum computing in relation to the currency arbitrage problem. In this study, we implement the Quantum Approximate Optimization Algorithm (QAOA) utilizing Qiskit version 1.2. In order to optimize the parameters of QAOA, we perform simulations utilizing the AerSimulator and carry out experiments in simulation. Furthermore, we present an NchooseK-based methodology utilizing D-Wave's Ocean suite. This methodology enables a comparison of the effectiveness of quantum techniques in identifying optimal arbitrage paths. The results of our study enhance the existing literature on the application of quantum computing in financial optimization challenges, emphasizing both the prospective benefits and the present limitations of these developing technologies in real-world scenarios.

Tensor networks provide a powerful framework for compressing multi-dimensional data. The optimal tensor network structure for a given data tensor depends on both the inherent data properties and the specific optimality criteria, making tensor network structure search a crucial research problem. Existing solutions typically involve sampling and validating numerous candidate structures; this is computationally expensive, limiting their practical applications. We address this challenge by formulating tensor network structure search as a program synthesis problem and proposing a highly efficient validation method that is based on constraint solving. Specifically, we design a domain specific language: it builds the correspondence between programs and network structures, and uses a novel idea of output-directed splits to compress the search space without hindering the expressiveness. We then propose a synthesis algorithm that can prioritize promising candidates through constraint solving. % Experimental results show that our approach improves search speed by $10\times$ and achieves compression ratios by $1.5\times$ to $3\times$ better than state-of-the-art. Notably, our approach scales to larger tensors that are out of reach by prior work. Finally, we demonstrate that the discovered topologies generalize to data from the same source, achieving compression ratios up to $ 2.4\times$ better than hierarchical Tuckers while maintaining the runtime around $110$ seconds.

A new statistical definition for the mean turbulent boundary layer thickness is introduced, based on the identification of the point where streamwise velocity skewness changes sign in the outermost region of the boundary layer. This definition is motivated by the phenomenology of streamwise velocity fluctuations near the turbulent/non-turbulent interface, whose local characteristics are shown to be universal for turbulent boundary layers under low freestream turbulence conditions (e.g., with or without pressure gradients, surface roughness, etc.). This approach provides a turbulent boundary layer thickness that is consistent with previous definitions, such as those based on Reynolds shear stress or `composite' mean velocity profiles, while being independent of arbitrary thresholds and applicable to past single-point measurements. Two methods are proposed for estimating the turbulent boundary layer thickness using this definition: one based on simple linear interpolation and the other on fitting a generalised Fourier model to the outer skewness profile. The robustness and limitations of these methods are demonstrated through analysis of several published experimental and numerical datasets, which cover a range of canonical and non-canonical turbulent boundary layers. These datasets vary in wall-normal resolution and measurement noise, particularly in the critical turbulent/non-turbulent interface region., Comment: Submitted to the Journal of Fluid Mechanics, with 23 pages and 10 figures

We present MultiChallenge, a pioneering benchmark evaluating large language models (LLMs) on conducting multi-turn conversations with human users, a crucial yet underexamined capability for their applications. MultiChallenge identifies four categories of challenges in multi-turn conversations that are not only common and realistic among current human-LLM interactions, but are also challenging to all current frontier LLMs. All 4 challenges require accurate instruction-following, context allocation, and in-context reasoning at the same time. We also develop LLM as judge with instance-level rubrics to facilitate an automatic evaluation method with fair agreement with experienced human raters. Despite achieving near-perfect scores on existing multi-turn evaluation benchmarks, all frontier models have less than 50% accuracy on MultiChallenge, with the top-performing Claude 3.5 Sonnet (June 2024) achieving just a 41.4% average accuracy.

We present a gradient-based meta-learning framework for rapid adaptation of neural state-space models (NSSMs) for black-box system identification. When applicable, we also incorporate domain-specific physical constraints to improve the accuracy of the NSSM. The major benefit of our approach is that instead of relying solely on data from a single target system, our framework utilizes data from a diverse set of source systems, enabling learning from limited target data, as well as with few online training iterations. Through benchmark examples, we demonstrate the potential of our approach, study the effect of fine-tuning subnetworks rather than full fine-tuning, and report real-world case studies to illustrate the practical application and generalizability of the approach to practical problems with physical-constraints. Specifically, we show that the meta-learned models result in improved downstream performance in model-based state estimation in indoor localization and energy systems., Comment: 30 pages

This study investigates the influence of various growth parameters on normal-state resistivity and superconducting transition temperature Tc of granular aluminum films. Specifically, we focus on the effects of oxygen flow and aluminum evaporation rate during the growth process conducted at different substrate temperatures, from 300 K down to 25 K. We report systematic correlations between the growth conditions, the normal-state resistivity, and Tc. Furthermore our findings provide insights into optimizing the superconducting characteristics of granular aluminum., Comment: 16 pages, 4 figures

The fusion energy research and development landscape has seen significant advances in recent years, with important scientific and technological breakthroughs and a rapid rise of investment in the private sector. The workforce needs of the nascent fusion industry are growing at a rate that academic workforce development programs are not currently able to match. This paper presents the findings of the Workforce Accelerator for Fusion Energy Development Conference held in Hampton, Virginia, United States of America (USA), on May 29-30 2024, which was funded by the US National Science Foundation (NSF). A major goal of the conference was to focus on bringing public and private stakeholders together to identify opportunities for partnership in fusion research and education with the goal of meeting the needs for a talented and diverse workforce. Representatives from industry, academia, and national laboratories participated in the conference through the preparation of white papers, presentations, and group discussions, and the production of recommendations to address the challenges facing the US fusion workforce.

Measurements of differential length oscillations of Fabry-Perot cavities provide a sensitive and promising approach to searching for scalar ultralight dark matter (ULDM). The initial demonstration sets direct lower bounds that are one to two orders of magnitude lower for two model ULDM distributions -- a standard galactic halo, and a relaxion star bound to Earth -- ranging over a decade of ULDM mass and Compton frequency. The demonstration suggests how a much higher sensitivity to a much larger ULDM mass range can be obtained.

We demonstrate trapping of individual rubidium (Rb) and cesium (Cs) atoms in an interleaved array of bright tweezers and dark bottle-beam traps, using a microfabricated optical element illuminated by a single laser beam. This trapping approach is simple and robust compared to common approaches that use active optoelectronic components and multiple laser wavelengths, and can be easily adapted for other atomic species., Comment: Minor changes to the previous version: Added a couple of references, fixed a minor error on Fig 6, added info about equipment

The increase in wall-pressure fluctuations with increasing friction Reynolds number ($Re_{\tau}$) of a turbulent boundary layer (TBL) is well known in the literature. However, very few studies have investigated the $Re_{\tau}$-variation of the source terms of the pressure fluctuations, which are solely a function of the spatial velocity gradients within the TBL. This study quantifies the pressure source terms in a zero-pressure gradient TBL by utilizing a published direct numerical simulation (DNS; Sillero et al. 2013, Phys. Fluids) database across 1000 $\lesssim$ $Re_{\tau}$ $\lesssim$ 2000. It is found that the magnitude of all source terms increases with $Re_{\tau}$ across the entire TBL thickness, with the turbulence-turbulence (non-linear) interaction terms growing faster than the mean-shear (linear) source terms. Further, we use the simulation database to mimic the scenario of particle image velocimetry (PIV) experiments that are typically spatially under-resolved compared to DNS data. It is used to quantify the effect of spatial resolution on the accuracy of pressure source terms, which are estimated here for two common PIV scenarios: (i) planar PIV in the streamwise-wall-normal plane, and (ii) stereo-PIV in the spanwise-wall-normal plane of a ZPG TBL. This exercise reveals significant attenuation of all pressure source terms compared to those estimated from the original DNS, highlighting the challenges of accurately estimating these source terms in a high $Re_{\tau}$ PIV experiment., Comment: Accepted in the Proceedings of the 11th International and 51st National Conference on Fluid Mechanics and Fluid Power (FMFP)

As database query processing techniques are being used to handle diverse workloads, a key emerging challenge is how to efficiently handle multi-way join queries containing multiple many-to-many joins. While uncommon in traditional enterprise settings that have been the focus of much of the query optimization work to date, such queries are seen frequently in other contexts such as graph workloads. This has led to much work on developing join algorithms for handling cyclic queries, on compressed (factorized) representations for more efficient storage of intermediate results, and on use of semi-joins or predicate transfer to avoid generating large redundant intermediate results. In this paper, we address a core query optimization problem in this context. Specifically, we introduce an improved cost model that more accurately captures the cost of a query plan in such scenarios, and we present several optimization algorithms for query optimization that incorporate these new cost functions. We present an extensive experimental evaluation, that compares the factorized representation approach with a full semi-join reduction approach as well as to an approach that uses bitvectors to eliminate tuples early through sideways information passing. We also present new analyses of robustness of these techniques to the choice of the join order, potentially eliminating the need for more complex query optimization and selectivity estimation techniques.

The LLM-as-judge paradigm is increasingly being adopted for automated evaluation of model outputs. While LLM judges have shown promise on constrained evaluation tasks, closed source LLMs display critical shortcomings when deployed in real world applications due to challenges of fine grained metrics and explainability, while task specific evaluation models lack cross-domain generalization. We introduce GLIDER, a powerful 3B evaluator LLM that can score any text input and associated context on arbitrary user defined criteria. GLIDER shows higher Pearson's correlation than GPT-4o on FLASK and greatly outperforms prior evaluation models, achieving comparable performance to LLMs 17x its size. GLIDER supports fine-grained scoring, multilingual reasoning, span highlighting and was trained on 685 domains and 183 criteria. Extensive qualitative analysis shows that GLIDER scores are highly correlated with human judgments, with 91.3% human agreement. We have open-sourced GLIDER to facilitate future research.

In deep-inelastic positron-proton scattering, the lepton-jet azimuthal angular asymmetry is measured using data collected with the H1 detector at HERA. When the average transverse momentum of the lepton-jet system, $\lvert \vec{P}_\perp \rvert $, is much larger than the total transverse momentum of the system, $\lvert \vec{q}_\perp \rvert$, the asymmetry between parallel and antiparallel configurations, $\vec{P}_\perp$ and $\vec{q}_\perp$, is expected to be generated by initial and final state soft gluon radiation and can be predicted using perturbation theory. Quantifying the angular properties of the asymmetry therefore provides an additional test of the strong force. Studying the asymmetry is important for future measurements of intrinsic asymmetries generated by the proton's constituents through Transverse Momentum Dependent (TMD) Parton Distribution Functions (PDFs), where this asymmetry constitutes a dominant background. Moments of the azimuthal asymmetries are measured using a machine learning method for unfolding that does not require binning., Comment: 15 pages, 4 figures, 2 tables, updated affiliations and acknowledgements

We report two-dimensional particle image velocimetry experiments in high Reynolds number turbulent boundary layers imposed with a moderately strong streamwise pressure gradient. The unique aspect of these data are the highly resolved measurements across the outer region of a physically thick boundary layer, enabling accurate detection of turbulent/non-turbulent interfaces (TNTI). The present dataset is used to detect the TNTI of an adverse pressure gradient turbulent boundary layer and compare its characteristics with that for a zero-pressure gradient boundary layer, at a nominally similar friction Reynolds number. It is found that the TNTI exists across a broader range of wall-normal distance in presence of an adverse pressure gradient, as compared to the zero-pressure gradient case. Implications on conditionally averaged turbulence statistics are discussed based on detection of the TNTI., Comment: Accepted in the Proceedings of the IUTAM Symposium on Turbulent/Non-Turbulent Interface in Turbulent Shear Flows

Quantized anomalous Hall effects (QAHEs) occur in remarkable electronic states which possess not only quantized Hall signals but in some cases regions of dissipationless electron transport. The initial demonstrations of a QAHE in a magnetically-doped topological insulator (TI) required temperatures below 100 mK, and since then a major focus of the field has been to increase the temperature scale. Here, we report quantized Hall signals up to 10 K (in what is known as the parity anomaly state) in TI/magnet bilayers made by mechanical assembly, rather than by conventional deposition techniques. This is a factor of 100 higher temperature than any previous realization of a QAHE in a proximity-coupled TI/magnet heterostructure made by deposition, and approximately twice the previous record for any QAHE system.

Let $G$ be a connected reductive algebraic group over an algebraically closed field $k$ of characteristic $p > 0$ and let $\ell$ be a prime number different from $p$. Let $U \subseteq G$ be a maximal unipotent subgroup, $T$ a maximal torus normalizing $U$ and $W$ the Weyl group of $G$. Let $\mathcal{L}$ be a non-degenerate multiplicative $\overline{\mathbb{Q}}_{\ell} $-local system on $U$. R. Bezrukavnikov and the second author have proved that the bi-Whittaker category, namely the triangulated monoidal category of $(U, \mathcal{L})$-biequivariant $\overline{\mathbb{Q}}_{\ell}$-complexes on $G$ is monoidally equivalent to an explicit thick triangulated monoidal subcategory $\mathscr{D}_{W}^{\circ}(T) \subseteq \mathscr{D}_{W}(T)$ of "central sheaves" on the torus. In particular it has the structure of a symmetric monoidal category coming from the symmetric monoidal structure on $\mathscr{D}_W(T)$. In this paper, we give another construction of a symmetric monoidal structure on the above category and prove that it agrees with the one coming from the above construction. For this, among other things, we generalize a proof by Gelfand for finite groups to the geometric setup.

Hepatocellular carcinoma (HCC) is a common type of liver cancer whose early-stage diagnosis is a common challenge, mainly due to the manual assessment of hematoxylin and eosin-stained whole slide images, which is a time-consuming process and may lead to variability in decision-making. For accurate detection of HCC, we propose a hybrid deep learning-based architecture that uses transfer learning to extract the features from pre-trained convolutional neural network (CNN) models and a classifier made up of a sequence of fully connected layers. This study uses a publicly available The Cancer Genome Atlas Hepatocellular Carcinoma (TCGA-LIHC)database (n=491) for model development and database of Kasturba Gandhi Medical College (KMC), India for validation. The pre-processing step involves patch extraction, colour normalization, and augmentation that results in 3920 patches for the TCGA dataset. The developed hybrid deep neural network consisting of a CNN-based pre-trained feature extractor and a customized artificial neural network-based classifier is trained using five-fold cross-validation. For this study, eight different state-of-the-art models are trained and tested as feature extractors for the proposed hybrid model. The proposed hybrid model with ResNet50-based feature extractor provided the sensitivity, specificity, F1-score, accuracy, and AUC of 100.00%, 100.00%, 100.00%, 100.00%, and 1.00, respectively on the TCGA database. On the KMC database, EfficientNetb3 resulted in the optimal choice of the feature extractor giving sensitivity, specificity, F1-score, accuracy, and AUC of 96.97, 98.85, 96.71, 96.71, and 0.99, respectively. The proposed hybrid models showed improvement in accuracy of 2% and 4% over the pre-trained models in TCGA-LIHC and KMC databases., Comment: 14 figure, 9 tables

Under mass-action kinetics, complex-balanced systems emerge from biochemical reaction networks and exhibit stable and predictable dynamics. For a reaction network $G$, the associated dynamical system is called $\textit{disguised toric}$ if it can yield a complex-balanced realization on a possibly different network $G_1$. This concept extends the robust properties of toric systems to those that are not inherently toric. In this work, we study the $\textit{disguised toric locus}$ of a reaction network - i.e., the set of positive rate constants that make the corresponding mass-action system disguised toric. Our primary focus is to compute the exact dimension of this locus. We subsequently apply our results to Thomas-type and circadian clock models., Comment: 43 pages, 4 figures

This study explores the application of deep learning for rainfall prediction, leveraging the Spinning Enhanced Visible and Infrared Imager (SEVIRI) High rate information transmission (HRIT) data as input and the Operational Program on the Exchange of weather RAdar information (OPERA) ground-radar reflectivity data as ground truth. We use the mean of 4 InfraRed frequency channels as the input. The radiance images are forecasted up to 4 hours into the future using a dense optical flow algorithm. A conditional generative adversarial network (GAN) model is employed to transform the predicted radiance images into rainfall images which are aggregated over the 4 hour forecast period to generate cumulative rainfall values. This model scored a value of approximately 7.5 as the Continuous Ranked Probability Score (CRPS) in the Weather4Cast 2024 competition and placed 1st on the core challenge leaderboard.

In recent times, online education and the usage of video-conferencing platforms have experienced massive growth. Due to the limited scope of a virtual classroom, it may become difficult for instructors to analyze learners' attention and comprehension in real time while teaching. In the digital mode of education, it would be beneficial for instructors to have an automated feedback mechanism to be informed regarding learners' attentiveness at any given time. This research presents a novel computer vision-based approach to analyze and quantify learners' attentiveness, engagement, and other affective states within online learning scenarios. This work presents the development of a multiclass multioutput classification method using convolutional neural networks on a publicly available dataset - DAiSEE. A machine learning-based algorithm is developed on top of the classification model that outputs a comprehensive attentiveness index of the learners. Furthermore, an end-to-end pipeline is proposed through which learners' live video feed is processed, providing detailed attentiveness analytics of the learners to the instructors. By comparing the experimental outcomes of the proposed method against those of previous methods, it is demonstrated that the proposed method exhibits better attentiveness detection than state-of-the-art methods. The proposed system is a comprehensive, practical, and real-time solution that is deployable and easy to use. The experimental results also demonstrate the system's efficiency in gauging learners' attentiveness.

Dynamic metasurface antennas (DMAs) beamform through low-powered components that enable reconfiguration of each radiating element. Previous research on a single-user multiple-input-single-output (MISO) system with a dynamic metasurface antenna at the transmitter has focused on maximizing the beamforming gain at a fixed operating frequency. The DMA, however, has a frequency-selective response that leads to magnitude degradation for frequencies away from the resonant frequency of each element. This causes reduction in beamforming gain if the DMA only operates at a fixed frequency. We exploit the frequency reconfigurability of the DMA to dynamically optimize both the operating frequency and the element configuration, maximizing the beamforming gain. We leverage this approach to develop a single-shot beam training procedure using a DMA sub-array architecture that estimates the receiver's angular direction with a single OFDM pilot signal. We evaluate the beamforming gain performance of the DMA array using the receiver's angular direction estimate obtained from beam training. Our results show that it is sufficient to use a limited number of resonant frequency states to do both beam training and beamforming instead of using an infinite resolution DMA beamformer.

Frequency upconversion, which converts low-energy photons into higher-energy ones, typically requires intense coherent illumination to drive nonlinear processes or the use of externally driven optoelectronic devices. Here, we demonstrate a high-resolution upconversion imaging system that converts low-intensity (down to 50 nW/cm$^2$) incoherent near-infrared (NIR) light into the visible, reaching intensities perceptible by the human eye, without the use of any external power input. Our upconverting element is enabled by the following ingredients: (1) photon upconversion via triplet-triplet annihilation in a bulk heterojunction of the organic semiconductors Y6 and rubrene; (2) plasmonic enhancement of absorption and field intensity in the heterojunction layer; (3) collection enhancement using a dichroic thin-film assembly. The upconverting element is inserted at an intermediate image plane of a dual-wavelength telescope system, which preserves the relative directionality of rays between the incident NIR light and output visible light. Our all-passive upconversion imaging system will enable NIR imaging and sensing in low-light environments under energy constraints., Comment: Main text + supplementary

We propose to measure the beam normal single spin asymmetry in elastic scattering of transversely polarized electron from target nuclei with 12 $\leq Z \leq$ 90 at Q$^2$ = 0.0092 GeV$^2$ to study its nuclear dependence. While the theoretical calculations based on two-photon exchange suggest no nuclear dependence at this kinematics, the results of 208Pb from Jefferson Lab show a striking disagreement from both theoretical predictions and light nuclei measurements. The proposed measurements will provide new data for intermediate to heavy nuclei where no data exists for $Z \geq$ 20 in the kinematics of previous high-energy experiments. It will allow one to investigate the missing contributions that are not accounted in the current theoretical models., Comment: Submitted to Jefferson Lab PAC52

Current implementations of Bayesian Additive Regression Trees (BART) are based on axis-aligned decision rules that recursively partition the feature space using a single feature at a time. Several authors have demonstrated that oblique trees, whose decision rules are based on linear combinations of features, can sometimes yield better predictions than axis-aligned trees and exhibit excellent theoretical properties. We develop an oblique version of BART that leverages a data-adaptive decision rule prior that recursively partitions the feature space along random hyperplanes. Using several synthetic and real-world benchmark datasets, we systematically compared our oblique BART implementation to axis-aligned BART and other tree ensemble methods, finding that oblique BART was competitive with -- and sometimes much better than -- those methods.

A solid electrolyte having the ionic conductivity comparable to that of conventional liquid electrolyte can be used in All Solid State Batteries (ASSB's). The series Li6.75+xLa3-xSrxZr1.75Ta0.25O12 (x = 0 to 0.20) was synthesized to improve the ionic conductivity of garnet Li7La3Zr2O12 (LLZO). The structural, physical and morphological investigations have been carried out for all the synthesized samples using X ray diffraction, density measurement and scanning electron microscopy respectively. The results of electrochemical analysis showed that the maximum room temperature ionic conductivity of 3.5 x 10-4 S/cm and minimum activation energy of 0.29 eV is achieved by the 0.05 Sr ceramic sample sintered at 1050^oC. The DC conductivity measurement confirmed the dominance of ionic conduction in the prepared ceramic samples. The highest ionic conductivity with the minimum activation energy makes the 0.05 Sr ceramic sample a suitable choice as solid electrolyte for All Solid State Lithium Ion Batteries (ASSLIB's).

Although it is an extremely effective, easy-to-use, and increasingly popular tool for nonparametric regression, the Bayesian Additive Regression Trees (BART) model is limited by the fact that it can only produce discontinuous output. Initial attempts to overcome this limitation were based on regression trees that output Gaussian Processes instead of constants. Unfortunately, implementations of these extensions cannot scale to large datasets. We propose ridgeBART, an extension of BART built with trees that output linear combinations of ridge functions (i.e., a composition of an affine transformation of the inputs and non-linearity); that is, we build a Bayesian ensemble of localized neural networks with a single hidden layer. We develop a new MCMC sampler that updates trees in linear time and establish nearly minimax-optimal posterior contraction rates for estimating Sobolev and piecewise anisotropic Sobolev functions. We demonstrate ridgeBART's effectiveness on synthetic data and use it to estimate the probability that a professional basketball player makes a shot from any location on the court in a spatially smooth fashion.

In this work, we investigate the feasibility of defining centrality in electron-ion collisions at the Electron-Ion Collider (EIC) by examining the correlation between the impact parameter and several observables, including total energy, total transverse momentum, and total number of particles. Using the BeAGLE Monte Carlo generator, we simulate e+Au and e+Ru collisions at different energies and analyze the correlation between the impact parameter and these observables across different kinematic regions. Our findings indicate that the correlation is weak in the central rapidity region but becomes stronger in the forward and far-forward rapidity regions. However, the correlation is not sufficiently robust to allow for precise centrality determination. We conclude that defining centrality in electron-ion collisions is more challenging than in ion-ion collisions, necessitating further studies to develop a robust centrality definition for the EIC., Comment: 9 pages, 9 figures

This paper presents a novel approach for vehicle localization by leveraging the ambient magnetic field within a given environment. Our approach involves introducing a global mathematical function for magnetic field mapping, combined with Euclidean distance-based matching technique for accurately estimating vehicle position in suburban settings. The mathematical function based map structure ensures efficiency and scalability of the magnetic field map, while the batch processing based localization provides continuity in pose estimation. Additionally, we establish a bias estimation pipeline for an onboard accelerometer by utilizing the updated poses obtained through magnetic field matching. Our work aims to showcase the potential utility of magnetic fields as supplementary aids to existing localization methods, particularly beneficial in scenarios where Global Positioning System (GPS) signal is restricted or where cost-effective navigation systems are required.

Classical optimization of parameterized quantum circuits is a widely studied methodology for the preparation of complex quantum states, as well as the solution of machine learning and optimization problems. However, it is well known that many proposed parameterized quantum circuit architectures suffer from drawbacks which limit their utility, such as their classical simulability or the hardness of optimization due to a problem known as "barren plateaus". We propose and study a class of dynamic parameterized quantum circuit architectures. These are parameterized circuits containing intermediate measurements and feedforward operations. In particular, we show that these architectures: 1. Provably do not suffer from barren plateaus. 2. Are expressive enough to describe arbitrarily deep unitary quantum circuits. 3. Are competitive with state of the art methods for the preparation of ground states and facilitate the representation of nontrivial thermal states. These features make the proposed architectures promising candidates for a variety of applications., Comment: 50 pages, 11 figures

Agricultural landscapes are quite complex, especially in the Global South where fields are smaller, and agricultural practices are more varied. In this paper we report on our progress in digitizing the agricultural landscape (natural and man-made) in our study region of India. We use high resolution imagery and a UNet style segmentation model to generate the first of its kind national-scale multi-class panoptic segmentation output. Through this work we have been able to identify individual fields across 151.7M hectares, and delineating key features such as water resources and vegetation. We share how this output was validated by our team and externally by downstream users, including some sample use cases that can lead to targeted data driven decision making. We believe this dataset will contribute towards digitizing agriculture by generating the foundational baselayer., Comment: 34 pages, 7 tables, 15 figs

We introduce a theory of "odd viscodiffusive fluids," which exhibit three-dimensional odd transport phenomena through the coupling of viscous and diffusive transport. In these fluids, diffusive fluxes may arise from orthogonal velocity gradients and, reciprocally, stresses may arise from concentration gradients. We examine microscopic fluctuations using the recently proposed "flux hypothesis" to derive Green-Kubo and reciprocal relations for the governing transport coefficients. These relations suggest that only parity symmetry, and not time-reversal symmetry, must be broken at the microscopic scale to observe these couplings. Chiral liquids, whether passive or active, are therefore a natural choice as viscodiffusive fluids. We then introduce two analytically tractable model systems, namely a generator and a corresponding reciprocal engine, which illustrate the nature of viscodiffusive cross-coupling in chiral matter and enable the experimental measurement of the novel transport coefficients. Finally, we make the case for chiral bacterial suspensions to be odd viscodiffusive fluids, and use our theory to predict the behaviors exhibited in prior experimental microfluidic studies involving bacterial migration in response to shearing flows.

To construct higher-dimensional counterparts of the Kerr-Newman black holes, we consider Einstein's equations sourced by a vector field and a negative cosmological constant. In contrast to the four-dimensional case, the Maxwell's equations are modified by sources generated by topological Chern-Simons couplings, the situation already encountered in the minimal five dimensional supergravity. After constructing explicit geometries in all odd dimensions, we demonstrate that the Klein-Gordon equation on the new backgrounds is fully separable., Comment: 25 pages, references added

This study investigates the spatial evolution of a zero pressure gradient turbulent boundary layer (TBL) imposed by a square-wave (SqW) of steady spanwise wall-forcing, which varies along the streamwise direction ($x$). The SqW wall-forcing is imposed experimentally via a series of streamwise periodic belts running in opposite spanwise directions, following the methodology of Knoop et al., (Exp Fluids, vol 65, 2024), with the streamwise extent increased to beyond $\sim 11$ times the boundary layer thickness (${\delta}_o$) in the present study. This unique setup is leveraged to investigate the influence of viscous-scaled wavelength of SqW wall-forcing on the turbulent drag reduction (DR) efficacy, for $\lambda^+_x = $ 471 (sub-optimal), 942 (near-optimal), and 1884 (post-optimal conditions), at fixed viscous-scaled wall-forcing amplitude, $A^+ = 12$, and friction Reynolds number, $Re_\tau = 960$. The DR mechanisms associated with SqW wall-forcing are elucidated by comparing against established DR mechanisms for traditionally studied sinusoidal forcing (SinW), based on analysis of the streamwise-phase variation of the Stokes strain rate (SSR). The analysis reveals SqW forcing to be a combination of two distinctly different/decoupled SSR states: (i) strong impulses of SSR at half- ($\lambda_x$/2) and full-phase ($\lambda_x$) locations associated with reversal in spanwise forcing directions leading to significant turbulence attenuation, and (ii) near-zero SSR over the remainder of forcing phase that enables turbulence recovery (when wall-forcing magnitudes and direction remain constant). While present experiments are limited to low Reynolds number TBLs, the results are extrapolated to depict a conceptual picture of SqW forcing efficacy in high Reynolds number TBLs (corresponding to a broader scale hierarchy).

Recently, it was discovered that the proton structure at high energies exhibits maximal entanglement. This leads to a simple relation between the proton's parton distributions and the entropy of hadrons produced in high-energy inelastic interactions that has been experimentally confirmed. In this letter, we extend this approach to the production of jets. Here, the maximal entanglement predicts a relation between the jet fragmentation function and the entropy of hadrons produced in jet fragmentation. We test this relation using the ATLAS Collaboration data on jet production at the Large Hadron Collider and find good agreement between the prediction based on maximal entanglement within the jet and the data. This study represents the first use of the quantum entanglement framework in the experimental study of the hadronization process, offering a new perspective on the transition from perturbative to non-perturbative QCD. Our results open the door to a more comprehensive understanding of the quantum nature of hadronization., Comment: 8 pages, 6 figures

Interrupted X-ray computed tomography (X-CT) has been the common way to observe the deformation of materials during an experiment. While this approach is effective for quasi-static experiments, it has never been possible to reconstruct a full 3d tomography during a dynamic experiment which cannot be interrupted. In this work, we propose that neural rendering tools can be used to drive the paradigm shift to enable 3d reconstruction during dynamic events. First, we derive theoretical results to support the selection of projections angles. Via a combination of synthetic and experimental data, we demonstrate that neural radiance fields can reconstruct data modalities of interest more efficiently than conventional reconstruction methods. Finally, we develop a spatio-temporal model with spline-based deformation field and demonstrate that such model can reconstruct the spatio-temporal deformation of lattice samples in real-world experiments., Comment: 24 pages, 14 figures. Submitted to NeurIPS 2024 ML4PS. For associated visualizations, see https://neural-xray.github.io/nerfxray

Industry actors in the United States have gained extensive influence in conversations about the regulation of general-purpose artificial intelligence (AI) systems. Although industry participation is an important part of the policy process, it can also cause regulatory capture, whereby industry co-opts regulatory regimes to prioritize private over public welfare. Capture of AI policy by AI developers and deployers could hinder such regulatory goals as ensuring the safety, fairness, beneficence, transparency, or innovation of general-purpose AI systems. In this paper, we first introduce different models of regulatory capture from the social science literature. We then present results from interviews with 17 AI policy experts on what policy outcomes could compose regulatory capture in US AI policy, which AI industry actors are influencing the policy process, and whether and how AI industry actors attempt to achieve outcomes of regulatory capture. Experts were primarily concerned with capture leading to a lack of AI regulation, weak regulation, or regulation that over-emphasizes certain policy goals over others. Experts most commonly identified agenda-setting (15 of 17 interviews), advocacy (13), academic capture (10), information management (9), cultural capture through status (7), and media capture (7) as channels for industry influence. To mitigate these particular forms of industry influence, we recommend systemic changes in developing technical expertise in government and civil society, independent funding streams for the AI ecosystem, increased transparency and ethics requirements, greater civil society access to policy, and various procedural safeguards., Comment: 39 pages (14 pages main text), 3 figures, 9 tables. To be published in the Proceedings of the 2024 AAAI/ACM Conference on AI, Ethics, & Society (AIES)

As the number of devices getting connected to the vehicular network grows exponentially, addressing the numerous challenges of effectively allocating spectrum in dynamic vehicular environment becomes increasingly difficult. Traditional methods may not suffice to tackle this issue. In vehicular networks safety critical messages are involved and it is important to implement an efficient spectrum allocation paradigm for hassle free communication as well as manage the congestion in the network. To tackle this, a Deep Q Network (DQN) model is proposed as a solution, leveraging its ability to learn optimal strategies over time and make decisions. The paper presents a few results and analyses, demonstrating the efficacy of the DQN model in enhancing spectrum sharing efficiency. Deep Reinforcement Learning methods for sharing spectrum in vehicular networks have shown promising outcomes, demonstrating the system's ability to adjust to dynamic communication environments. Both SARL and MARL models have exhibited successful rates of V2V communication, with the cumulative reward of the RL model reaching its maximum as training progresses.

In this paper, we introduce a novel system designed to enhance customer service in the financial and retail sectors through a context-aware 3D virtual agent, utilizing Mixed Reality (MR) and Vision Language Models (VLMs). Our approach focuses on enabling data-driven and empathetic interactions that ensure customer satisfaction by introducing situational awareness of the physical location, personalized interactions based on customer profiles, and rigorous privacy and security standards. We discuss our design considerations critical for deployment in real-world customer service environments, addressing challenges in user data management and sensitive information handling. We also outline the system architecture and key features unique to banking and retail environments. Our work demonstrates the potential of integrating MR and VLMs in service industries, offering practical insights in customer service delivery while maintaining high standards of security and personalization., Comment: to appear at 1st Workshop on Intelligent XR: Harnessing AI for Next-Generation XR User Experiences at International Symposium on Mixed and Augmented Reality (ISMAR) 2024

This paper presents a physics-based compact model for circuit simulations in a SPICE environment for HfO2-based ferroelectric capacitors (FeCaps). The model has been calibrated based on experimental data obtained from HfO2-based FeCaps. A thermal model with an accurate description of the device parasitics is included to derive precise device characteristics based on first principles. The model incorporates statistical data that enables Monte Carlo analysis based on realistic distributions, thereby making it particularly well-suited for design-technology co-optimization (DTCO). Furthermore, the model is demonstrated in circuit simulations using an integrated circuit with current programming, wherein partial switching of the ferroelectric polarization is observed. Finally, the model was benchmarked in an array simulation, reaching convergence in 1.8 s with an array size of 100 kb., Comment: 6 pages, 7 figures

We present the emergence of topological phase transition in the minimal model of two dimensional rock-paper-scissors cycle in the form of a doublet chain. The evolutionary dynamics of the doublet chain is obtained by solving the anti-symmetric Lotka-Volterra equation. We show that the mass decays exponentially towards edges and robust against small perturbation in the rate of change of mass transfer, a signature of a topological phase. For one of the configuration of our doublet chain, the mass is transferred towards both edges and the bulk is gaped. Further, we confirm this phase transition within the framework of topological band theory. For this we calculate the winding number which change from zero to one for trivial and a non-trivial topological phases respectively., Comment: 9 pages, 7 figures

Evaluating policies using off-policy data is crucial for applying reinforcement learning to real-world problems such as healthcare and autonomous driving. Previous methods for off-policy evaluation (OPE) generally suffer from high variance or irreducible bias, leading to unacceptably high prediction errors. In this work, we introduce STAR, a framework for OPE that encompasses a broad range of estimators -- which include existing OPE methods as special cases -- that achieve lower mean squared prediction errors. STAR leverages state abstraction to distill complex, potentially continuous problems into compact, discrete models which we call abstract reward processes (ARPs). Predictions from ARPs estimated from off-policy data are provably consistent (asymptotically correct). Rather than proposing a specific estimator, we present a new framework for OPE and empirically demonstrate that estimators within STAR outperform existing methods. The best STAR estimator outperforms baselines in all twelve cases studied, and even the median STAR estimator surpasses the baselines in seven out of the twelve cases., Comment: Accepted at the Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024)