Generalized Bayesian Multidimensional Scaling and Model Comparison

Multidimensional scaling is widely used to reconstruct a map with the points' coordinates in a low-dimensional space from the original high-dimensional space while preserving the pairwise distances. In a Bayesian framework, the current approach using Markov chain Monte Carlo algorithms has limitations in terms of model generalization and performance comparison. To address these limitations, a general framework that incorporates non-Gaussian errors and robustness to fit different types of dissimilarities is developed. Then, an adaptive inference method using annealed Sequential Monte Carlo algorithm for Bayesian multidimensional scaling is proposed. This algorithm performs inference sequentially in time and provides an approximate posterior distribution over the points' coordinates in a low-dimensional space and an unbiased estimator for the marginal likelihood. In this study, we compare the performance of different models based on marginal likelihoods, which are produced as a byproduct of the adaptive annealed Sequential Monte Carlo algorithm. Using synthetic and real data, we demonstrate the effectiveness of the proposed algorithm. Our results show that the proposed algorithm outperforms other benchmark algorithms under the same computational budget based on common metrics used in the literature. The implementation of our proposed method and applications are available at https://github.com/nunujiarui/GBMDS

Automated calibration and in‐line measurement of product quality during therapeutic monoclonal antibody purification using Raman spectroscopy

Current manufacturing and development processes for therapeutic monoclonal antibodies demand increasing volumes of analytical testing for both real-time process controls and high-throughput process development. The feasibility of using Raman spectroscopy as an in-line product quality measuring tool has been recently demonstrated and promises to relieve this analytical bottleneck. Here, we resolve time-consuming calibration process that requires fractionation and preparative experiments covering variations of product quality attributes (PQAs) by engineering an automation system capable of collecting Raman spectra on the order of hundreds of calibration points from two to three stock seed solutions differing in protein concentration and aggregate level using controlled mixing. We used this automated system to calibrate multi-PQA models that accurately measured product concentration and aggregation every 9.3 s using an in-line flow-cell. We demonstrate the application of a nonlinear calibration model for monitoring product quality in real-time during a biopharmaceutical purification process intended for clinical and commercial manufacturing. These results demonstrate potential feasibility to implement quality monitoring during GGMP manufacturing as well as to increase chemistry, manufacturing, and controls understanding during process development, ultimately leading to more robust and controlled manufacturing processes

Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Power

The ability of graph neural networks (GNNs) to count certain graph substructures, especially cycles, is important for the success of GNNs on a wide range of tasks. It has been recently used as a popular metric for evaluating the expressive power of GNNs. Many of the proposed GNN models with provable cycle counting power are based on subgraph GNNs, i.e., extracting a bag of subgraphs from the input graph, generating representations for each subgraph, and using them to augment the representation of the input graph. However, those methods require heavy preprocessing, and suffer from high time and memory costs. In this paper, we overcome the aforementioned limitations of subgraph GNNs by proposing a novel class of GNNs -- $d$-Distance-Restricted FWL(2) GNNs, or $d$-DRFWL(2) GNNs. $d$-DRFWL(2) GNNs use node pairs whose mutual distances are at most $d$ as the units for message passing to balance the expressive power and complexity. By performing message passing among distance-restricted node pairs in the original graph, $d$-DRFWL(2) GNNs avoid the expensive subgraph extraction operations in subgraph GNNs, making both the time and space complexity lower. We theoretically show that the discriminative power of $d$-DRFWL(2) GNNs strictly increases as $d$ increases. More importantly, $d$-DRFWL(2) GNNs have provably strong cycle counting power even with $d=2$: they can count all 3, 4, 5, 6-cycles. Since 6-cycles (e.g., benzene rings) are ubiquitous in organic molecules, being able to detect and count them is crucial for achieving robust and generalizable performance on molecular tasks. Experiments on both synthetic datasets and molecular datasets verify our theory. To the best of our knowledge, our model is the most efficient GNN model to date (both theoretically and empirically) that can count up to 6-cycles

Charge-Density Wave in Overdoped Cuprates Driven by Electron-Phonon Couplings

Recent resonant x-ray scattering (RXS) experiments revealed a novel charge order in highly overdoped La$_{2-x}$Sr$_x$CuO$_4$ (LSCO). The observed charge order appears around the $(\pi/3,0)$ wavevector and remains robust from cryogenic temperatures to room temperature. To investigate the origin of this charge order in the overdoped region, we use determinant quantum Monte Carlo (DQMC) simulations to examine models with various interactions. We demonstrate that this CDW originates from remnant correlations in overdoped cuprates. The doping-independent wavevector $(\pi/3,0)$ further reflects the presence of nonlocal electron-phonon couplings. Our study reveals the importance of phonons in the cuprates, which assist correlated electrons in the formation of exotic phases.Comment: 7 pages, 4 figure

Labor leverage, financial statement comparability, and corporate employment

We examine how labor-induced operating leverage shapes managers' decision to adopt more comparable financial statement. We hypothesize that firms subject to higher labor-induced operating leverage are more likely to adopt more comparable financial statements in order to facilitate more timely employment adjustment which reduces firm risk related to labor leverage. Consistent with our hypothesis, we find that proxies for labor-induced operating leverage, such as labor unions, labor intensity, and labor share are positively related to financial statement comparability. We also find that financial statement comparability increases the sensitivity of hiring to performance change, particularly, for negative operating performance, supporting our notion that financial statement comparability helps managers' timelier labor adjustment. Last, we examine whether the improved comparability prevents massive layoffs thanks to continuously more timely employment adjustment. Consistent with our prediction, we find that comparability reduces the likelihood of large-scale layoffs

Reproducing the Velocity Vectors in the Listening Region

This paper proposes a sound field reproduction algorithm based on matching the velocity vectors in a spherical listening region. Using the concept of sound field translation, the spherical harmonic coefficients of the velocity vectors in a spherical region are derived from the desired pressure distribution. The desired pressure distribution can either correspond to sources such as plane waves and point sources, or be obtained from measurements using a spherical microphone array. Unlike previous work in which the velocity vectors are only controlled on the boundary of the listening region or at discrete sweet spots, this work directly manipulates the velocity vectors in the whole listening region, which is expected to improve the perception of the desired sound field at low frequencies.Comment:

Employees’ voluntary disclosures about business outlook and labor investment efficiency

We examine how employee business outlook affects firm-level labor investment efficiency by using data from Glassdoor. We hypothesize that due to the popularity and informativeness of employee voluntary disclosure through social media as a form of crowd wisdom in labor markets, more positive business outlook disclosed by employees can significantly reduce firms’ labor adjustment costs by attracting more job applicants in a timely matter, resulting in higher labor investment efficiency. Consistent with the hypothesis, we document that positive employee business outlook enhances labor investment efficiency by reducing both over-investment and under-investment in labor. Extending our first hypothesis, we also hypothesize and find that when peer firms’ employee business outlook is more positive than that of focal firms, focal firms’ labor adjustment costs increase because of the relative disadvantage in obtaining talented labor in labor markets, resulting in less efficient labor investment. We mitigate the endogeneity concerns by employing sub-sample analysis and using Anti-SLAPP laws as an exogenous shock