Sample-Based Online Generalized Assignment Problem with Unknown Poisson Arrivals

We study an edge-weighted online stochastic \emph{Generalized Assignment Problem} with \emph{unknown} Poisson arrivals. In this model, we consider a bipartite graph that contains offline bins and online items, where each offline bin is associated with a $D$-dimensional capacity vector and each online item is with a $D$-dimensional demand vector. Online arrivals are sampled from a set of online item types which follow independent but not necessarily identical Poisson processes. The arrival rate for each Poisson process is unknown. Each online item will either be packed into an offline bin which will deduct the allocated bin's capacity vector and generate a reward, or be rejected. The decision should be made immediately and irrevocably upon its arrival. Our goal is to maximize the total reward of the allocation without violating the capacity constraints. We provide a sample-based multi-phase algorithm by utilizing both pre-existing offline data (named historical data) and sequentially revealed online data. We establish its performance guarantee measured by a competitive ratio. In a simplified setting where $D=1$ and all capacities and demands are equal to $1$, we prove that the ratio depends on the number of historical data size and the minimum number of arrivals for each online item type during the planning horizon, from which we analyze the effect of the historical data size and the Poisson arrival model on the algorithm's performance. We further generalize the algorithm to the general multidimensional and multi-demand setting, and present its parametric performance guarantee. The effect of the capacity's (demand's) dimension on the algorithm's performance is further analyzed based on the established parametric form. Finally, we demonstrate the effectiveness of our algorithms numerically

Computational Modeling of Alloy Nanoparticle Stability

Metal nanoparticles (MNPs) are an exciting class of materials, finding applications in optical devices, electronics, drug delivery and chemical catalysis. Despite numerous applications, understanding of MNP stability is somewhat limited. First principles methods such as Density Functional Theory and semi-empirical models such as embedded atom model either suffer of high computational cost or inaccuracy. Herein, we introduce a bond-centric (BC) model to describe the cohesive energy of monometallic and bimetallic nanoparticles with arbitrary morphologies and chemical composition. We apply our BC model on a range of mono- and bi-metallic nanoparticles (nanoalloys) and demonstrate a great agreement with Density Functional Theory calculations. Moreover, we show our BC model effectively captures mixing behavior of nanoalloys through excess energy analysis. Additionally, we apply our BC model to perform energetic screening on a recently-published 23196-atom FePt nanoalloy and its homotops, offering insights of both segregation and chemical ordering behavior. The screening we performed is beyond reach of DFT because of the extremely large MNP size and number of nanoalloy conformations. Our findings are in agreement with literature. Therefore, our BC model is shown to be a powerful and computationally inexpensive tool to calculate energetics of almost any MNP, thus significantly accelerating MNP design

Induction of Maternal Immune Activation in Mice at Mid-gestation Stage with Viral Mimic Poly(I:C)

Maternal immune activation (MIA) model is increasingly well appreciated as a rodent model for the environmental risk factor of various psychiatric disorders. Numerous studies have demonstrated that MIA model is able to show face, construct, and predictive validity that are relevant to autism and schizophrenia. To model MIA, investigators often use viral mimic polyinosinic:polycytidylic acid (poly(I:C)) to activate the immune system in pregnant rodents. Generally, the offspring from immune activated dam exhibit behavioral abnormalities and physiological alterations that are associated with autism and schizophrenia. However, poly(I:C) injection with different dosages and at different time points could lead to different outcomes by perturbing brain development at different stages. Here we provide a detailed method of inducing MIA by intraperitoneal (i.p.) injection of 20 mg/kg poly(I:C) at mid-gestational embryonic 12.5 days (E12.5). This method has been shown to induce acute inflammatory response in the maternal-placental-fetal axis, which ultimately results in the brain perturbations and behavioral phenotypes that are associated with autism and schizophrenia

Broad-line region configuration of the supermassive binary black hole candidate PG1302-102 in the relativistic Doppler boosting scenario

PG1302-102 is thought to be a supermassive binary black hole (BBH) system according to the periodical variations of its optical and UV photometry, which may be interpreted as being due to the relativistic Doppler boosting of the emission mainly from the disk around the secondary black hole (BH) modulated by its orbital motion. In this paper, we investigate several broad emission lines of PG1302-102 using archived UV spectra obtained by IUE, GALEX, and Hubble, to reveal the broad-line region (BLR) emission properties of this BBH system under the Doppler boosting scenario. We find that the broad lines Ly$\alpha$, NV, CIV, and CIII] all show Gaussian profiles, and none of these lines exhibits obvious periodical variation. Adopting a simple model for the BLR, we perform Markov chain Monte Carlo fittings to these broad lines, and find that the BLR must be viewed at an orientation angle of $\sim33^{\circ}$, close to face-on. If the Doppler boosting interpretation is correct, then the BLR is misaligned with the BBH orbital plane by an angle of $\sim51^\circ$, which suggests that the Doppler boosted continuum variation has little effect on the broad-line emission and thus does not lead to periodical line variation. We further discuss the possible implications for such a BLR configuration with respect to the BBH orbital plane.Comment: 9 pages, 6 figures, matches A&A version (only minor changes

Interaction-Driven Active 3D Reconstruction with Object Interiors

We introduce an active 3D reconstruction method which integrates visual perception, robot-object interaction, and 3D scanning to recover both the exterior and interior, i.e., unexposed, geometries of a target 3D object. Unlike other works in active vision which focus on optimizing camera viewpoints to better investigate the environment, the primary feature of our reconstruction is an analysis of the interactability of various parts of the target object and the ensuing part manipulation by a robot to enable scanning of occluded regions. As a result, an understanding of part articulations of the target object is obtained on top of complete geometry acquisition. Our method operates fully automatically by a Fetch robot with built-in RGBD sensors. It iterates between interaction analysis and interaction-driven reconstruction, scanning and reconstructing detected moveable parts one at a time, where both the articulated part detection and mesh reconstruction are carried out by neural networks. In the final step, all the remaining, non-articulated parts, including all the interior structures that had been exposed by prior part manipulations and subsequently scanned, are reconstructed to complete the acquisition. We demonstrate the performance of our method via qualitative and quantitative evaluation, ablation studies, comparisons to alternatives, as well as experiments in a real environment.Comment: Accepted to SIGGRAPH Asia 2023, project page at https://vcc.tech/research/2023/InterReco