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Online advertising has recently grown into a highly competitive and complex multi-billion-dollar industry, with advertisers bidding for ad slots at large scales and high frequencies. This has resulted in a growing need for efficient "auto-bidding" algorithms that determine the bids for incoming queries to maximize advertisers' targets subject to their specified constraints. This work explores efficient online algorithms for a single value-maximizing advertiser under an increasingly popular constraint: Return-on-Spend (RoS). We quantify efficiency in terms of regret relative to the optimal algorithm, which knows all queries a priori. We contribute a simple online algorithm that achieves near-optimal regret in expectation while always respecting the specified RoS constraint when the input sequence of queries are i.i.d. samples from some distribution. We also integrate our results with the previous work of Balseiro, Lu, and Mirrokni [BLM20] to achieve near-optimal regret while respecting both RoS and fixed budget constraints. Our algorithm follows the primal-dual framework and uses online mirror descent (OMD) for the dual updates. However, we need to use a non-canonical setup of OMD, and therefore the classic low-regret guarantee of OMD, which is for the adversarial setting in online learning, no longer holds. Nonetheless, in our case and more generally where low-regret dynamics are applied in algorithm design, the gradients encountered by OMD can be far from adversarial but influenced by our algorithmic choices. We exploit this key insight to show our OMD setup achieves low regret in the realm of our algorithm., Comment: Simplified the proof of Lemma 3.2; fixed some typos

Neural Network (Deep Learning) is a modern model in Artificial Intelligence and it has been exploited in Survival Analysis. Although several improvements have been shown by previous works, training an excellent deep learning model requires a huge amount of data, which may not hold in practice. To address this challenge, we develop a Kullback-Leibler-based (KL) deep learning procedure to integrate external survival prediction models with newly collected time-to-event data. Time-dependent KL discrimination information is utilized to measure the discrepancy between the external and internal data. This is the first work considering using prior information to deal with short data problem in Survival Analysis for deep learning. Simulation and real data results show that the proposed model achieves better performance and higher robustness compared with previous works., Comment: This paper is not complete and the results are not qualified to be public. Therefore we decided to withdraw the paper and plan to submit a newer version in the future

Large-scale vision foundation models have made significant progress in visual tasks on natural images, with vision transformers being the primary choice due to their good scalability and representation ability. However, large-scale models in remote sensing (RS) have not yet been sufficiently explored. In this paper, we resort to plain vision transformers with about 100 million parameters and make the first attempt to propose large vision models tailored to RS tasks and investigate how such large models perform. To handle the large sizes and objects of arbitrary orientations in RS images, we propose a new rotated varied-size window attention to replace the original full attention in transformers, which can significantly reduce the computational cost and memory footprint while learning better object representation by extracting rich context from the generated diverse windows. Experiments on detection tasks show the superiority of our model over all state-of-the-art models, achieving 81.24% mAP on the DOTA-V1.0 dataset. The results of our models on downstream classification and segmentation tasks also show competitive performance compared to existing advanced methods. Further experiments show the advantages of our models in terms of computational complexity and data efficiency in transferring., Comment: Accepted by IEEE TGRS. The codes and models are released at https://github.com/ViTAE-Transformer/Remote-Sensing-RVSA

FASER, the ForwArd Search ExpeRiment, is an experiment dedicated to searching for light, extremely weakly-interacting particles at CERN's Large Hadron Collider (LHC). Such particles may be produced in the very forward direction of the LHC's high-energy collisions and then decay to visible particles inside the FASER detector, which is placed 480 m downstream of the ATLAS interaction point, aligned with the beam collisions axis. FASER also includes a sub-detector, FASER$\nu$, designed to detect neutrinos produced in the LHC collisions and to study their properties. In this paper, each component of the FASER detector is described in detail, as well as the installation of the experiment system and its commissioning using cosmic-rays collected in September 2021 and during the LHC pilot beam test carried out in October 2021. FASER will start taking LHC collision data in 2022, and will run throughout LHC Run 3., Comment: 92 pages, 72 Figures

Deep neural networks currently provide the most advanced and accurate machine learning models to distinguish between structural MRI scans of subjects with Alzheimer's disease and healthy controls. Unfortunately, the subtle brain alterations captured by these models are difficult to interpret because of the complexity of these multi-layer and non-linear models. Several heatmap methods have been proposed to address this issue and analyze the imaging patterns extracted from the deep neural networks, but no quantitative comparison between these methods has been carried out so far. In this work, we explore these questions by deriving heatmaps from Convolutional Neural Networks (CNN) trained using T1 MRI scans of the ADNI data set, and by comparing these heatmaps with brain maps corresponding to Support Vector Machines (SVM) coefficients. Three prominent heatmap methods are studied: Layer-wise Relevance Propagation (LRP), Integrated Gradients (IG), and Guided Grad-CAM (GGC). Contrary to prior studies where the quality of heatmaps was visually or qualitatively assessed, we obtained precise quantitative measures by computing overlap with a ground-truth map from a large meta-analysis that combined 77 voxel-based morphometry (VBM) studies independently from ADNI. Our results indicate that all three heatmap methods were able to capture brain regions covering the meta-analysis map and achieved better results than SVM coefficients. Among them, IG produced the heatmaps with the best overlap with the independent meta-analysis.

Inspired by the recent measurement of $CP$ asymmetry in the individual mode on LHCb, we study $CP$ asymmetry of the $D\to \pi\pi$ system in the isospin and topological analysis. The ratio between penguin and tree amplitudes $P/(T+C)$ in the $D\to \pi\pi$ system is found to be greater than two in most values of the relative strong phase. And $D^0\to \pi^0\pi^0$ is a potential mode to reveal the $CP$ asymmetry of the order of $10^{-3}$, which would be observed by Belle II in the future. The large $CP$ asymmetry in the $D\to \pi\pi$ system might be understood in the $t$-channel final-state interaction., Comment: 11 pages, 7 figures, published version

We study the design of prior-independent auctions in a setting with heterogeneous bidders. In particular, we consider the setting of selling to $n$ bidders whose values are drawn from $n$ independent but not necessarily identical distributions. We work in the robust auction design regime, where we assume the seller has no knowledge of the bidders' value distributions and must design a mechanism that is prior-independent. While there have been many strong results on prior-independent auction design in the i.i.d. setting, not much is known for the heterogeneous setting, even though the latter is of significant practical importance. Unfortunately, no prior-independent mechanism can hope to always guarantee any approximation to Myerson's revenue in the heterogeneous setting; similarly, no prior-independent mechanism can consistently do better than the second-price auction. In light of this, we design a family of (parametrized) randomized auctions which approximates at least one of these benchmarks: For heterogeneous bidders with regular value distributions, our mechanisms either achieve a good approximation of the expected revenue of an optimal mechanism (which knows the bidders' distributions) or exceeds that of the second-price auction by a certain multiplicative factor. The factor in the latter case naturally trades off with the approximation ratio of the former case. We show that our mechanism is optimal for such a trade-off between the two cases by establishing a matching lower bound. Our result extends to selling $k$ identical items to heterogeneous bidders with an additional $O\big(\ln^2 k\big)$-factor in our trade-off between the two cases., Comment: Full version of a paper in the Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)

KEK electron positron injector LINAC initiated the injection operation into Photon Factory (PF) light source in 1982. Since then for 39 years, it has served for multiple projects, namely, TRISTAN, PF-AR, KEKB, and SuperKEKB. Its total operation time has accumulated 200 thousand hours on May 7, 2020. We are extremely proud of the achievement following continuous efforts by our seniors. The construction of the injector LINAC started in 1978, and it was commissioned for PF with 2.5 GeV electron in 1982. In parallel, the positron generator linac was constructed for the TRISTAN collider project. The slow positron facility was also commissioned in 1992. After the KEKB asymmetric-energy collider project was commissioned in 1998 with direct energy injections, the techniques such as two-bunch acceleration and simultaneous injection were developed. As the soft structure design of the LINAC was too weak against the great east Japan earthquake, it took three years to recover. Then the construction and commissioning for the SuperKEKB project went on, and the simultaneous top-up injection into four storage rings contributes to the both elementary particle physics and photon science., Comment: 6 figures, contribution to IPAC2022, Bangkok, THPOST012

In this note, we investigate a kind of double centralizer property for general linear supergroups. For the super space $V=\mathbb{K}^{m\mid n}$ over an algebraically closed field $\mathbb{K}$ whose characteristic is not equal to $2$, we consider its $\mathbb{Z}_2$-homogeneous one-dimensional extension $\underline V=V\oplus\mathbb{K}v$, and the natural action of the supergroup $\tilde G:=\text{GL}(V)\times \textbf{G}_m$ on $\underline V$. Then we have the tensor product supermodule ($\underline{V}^{\otimes r}$, $\rho_r$) of $\tilde G$. We present a kind of generalized Schur-Sergeev duality which is said that the Schur superalgebras $S'(m|n,r)$ of $\tilde G$ and a so-called weak degenerate double Hecke algebra $\underline{\mathcal{H}}_r$ are double centralizers. The weak degenerate double Hecke algebra is an infinite dimensional algebra, which has a natural representation on the tensor product space. This notion comes from \cite{B-Y-Y2020}, with a little modification.

Recent learning-based image fusion methods have marked numerous progress in pre-registered multi-modality data, but suffered serious ghosts dealing with misaligned multi-modality data, due to the spatial deformation and the difficulty narrowing cross-modality discrepancy. To overcome the obstacles, in this paper, we present a robust cross-modality generation-registration paradigm for unsupervised misaligned infrared and visible image fusion (IVIF). Specifically, we propose a Cross-modality Perceptual Style Transfer Network (CPSTN) to generate a pseudo infrared image taking a visible image as input. Benefiting from the favorable geometry preservation ability of the CPSTN, the generated pseudo infrared image embraces a sharp structure, which is more conducive to transforming cross-modality image alignment into mono-modality registration coupled with the structure-sensitive of the infrared image. In this case, we introduce a Multi-level Refinement Registration Network (MRRN) to predict the displacement vector field between distorted and pseudo infrared images and reconstruct registered infrared image under the mono-modality setting. Moreover, to better fuse the registered infrared images and visible images, we present a feature Interaction Fusion Module (IFM) to adaptively select more meaningful features for fusion in the Dual-path Interaction Fusion Network (DIFN). Extensive experimental results suggest that the proposed method performs superior capability on misaligned cross-modality image fusion.

Flavor $SU(3)$ symmetry is a powerful tool to analyze charmed baryon decays. In this work, we propose an approach to generate $SU(3)$ sum rules for the singly and doubly charmed baryon decays without writing the Wigner-Eckhart invariants explicitly. The $SU(3)$ sum rules are computed routinely in several master formulas. Hundreds of $SU(3)$ sum rules are found to serve as test of the flavor symmetry in the charmed baryon decays., Comment: 41 pages, Version published in JHEP

We point out the $CP$ asymmetry in the chain decay of $\Lambda_c^+\to \Delta^+K(t)(\to \pi^+\pi^-)$ is determined by the branching fractions of $\Lambda^+_c\to \Delta^{++} K^-$, $\Lambda^+_c\to \Delta^{+} K^0_{S,L}$ and $\Lambda^+_c\to \Delta^{0} K^+$ modes under the isospin symmetry. The ambiguities from loop-induced quantities and $SU(3)_F$ breaking hadronic effects are avoided. Once the $CP$ asymmetry in the $\Lambda_c^+$ decays into $\Delta^+$ and neutral kaons is confirmed by experiments, we can check if it is beyond the Standard Model, or verify the $CP$-violating effect resulted from the interference between the Cabibbo-favored and the doubly Cabibbo-suppressed amplitudes with the neutral kaon mixing. Future measurements of branching fractions play a critical role in reducing the uncertainties., Comment: 13 pages, 1 figure, published version

Deep learning has largely reshaped remote sensing (RS) research for aerial image understanding and made a great success. Nevertheless, most of the existing deep models are initialized with the ImageNet pretrained weights. Since natural images inevitably present a large domain gap relative to aerial images, probably limiting the finetuning performance on downstream aerial scene tasks. This issue motivates us to conduct an empirical study of remote sensing pretraining (RSP) on aerial images. To this end, we train different networks from scratch with the help of the largest RS scene recognition dataset up to now -- MillionAID, to obtain a series of RS pretrained backbones, including both convolutional neural networks (CNN) and vision transformers such as Swin and ViTAE, which have shown promising performance on computer vision tasks. Then, we investigate the impact of RSP on representative downstream tasks including scene recognition, semantic segmentation, object detection, and change detection using these CNN and vision transformer backbones. Empirical study shows that RSP can help deliver distinctive performances in scene recognition tasks and in perceiving RS related semantics such as "Bridge" and "Airplane". We also find that, although RSP mitigates the data discrepancies of traditional ImageNet pretraining on RS images, it may still suffer from task discrepancies, where downstream tasks require different representations from scene recognition tasks. These findings call for further research efforts on both large-scale pretraining datasets and effective pretraining methods. The codes and pretrained models will be released at https://github.com/ViTAE-Transformer/ViTAE-Transformer-Remote-Sensing., Comment: Accepted by IEEE TGRS, codes and pretrained models are moved to https://github.com/ViTAE-Transformer/RSP

The reduced-rank vector autoregressive (VAR) model can be interpreted as a supervised factor model, where two factor modelings are simultaneously applied to response and predictor spaces. This article introduces a new model, called vector autoregression with common response and predictor factors, to explore further the common structure between the response and predictors in the VAR framework. The new model can provide better physical interpretations and improve estimation efficiency. In conjunction with the tensor operation, the model can easily be extended to any finite-order VAR model. A regularization-based method is considered for the high-dimensional estimation with the gradient descent algorithm, and its computational and statistical convergence guarantees are established. For data with pervasive cross-sectional dependence, a transformation for responses is developed to alleviate the diverging eigenvalue effect. Moreover, we consider additional sparsity structure in factor loading for the case of ultra-high dimension. Simulation experiments confirm our theoretical findings and a macroeconomic application showcases the appealing properties of the proposed model in structural analysis and forecasting., Comment: 87 pages, 6 figures

Deep Learning (DL) models have achieved superior performance. Meanwhile, computing hardware like NVIDIA GPUs also demonstrated strong computing scaling trends with 2x throughput and memory bandwidth for each generation. With such strong computing scaling of GPUs, multi-tenant deep learning inference by co-locating multiple DL models onto the same GPU becomes widely deployed to improve resource utilization, enhance serving throughput, reduce energy cost, etc. However, achieving efficient multi-tenant DL inference is challenging which requires thorough full-stack system optimization. This survey aims to summarize and categorize the emerging challenges and optimization opportunities for multi-tenant DL inference on GPU. By overviewing the entire optimization stack, summarizing the multi-tenant computing innovations, and elaborating the recent technological advances, we hope that this survey could shed light on new optimization perspectives and motivate novel works in future large-scale DL system optimization., Comment: Accepted in MLSys'22 Workshop on Cloud Intelligence / AIOps

Evaluation trials are used to probe performance of automatic speaker verification (ASV) systems. In spite of the clear importance and impact, evaluation trials have not been seriously treated in research and engineering practice. This paper firstly presents a theoretical analysis on evaluation trials and highlights potential bias with the most popular cross-pairing approach used in trials design. To interpret and settle this problem, we define the concept of trial config and C-P map derived from it. The C-P map measures the performance of an ASV system on various trial configs in a 2-dimensional map. On the map, each location represents a particular trial config and its corresponding color represents the system performance. Experiments conducted on representative ASV systems show that the proposed C-P map offers a powerful evaluation toolkit for ASV performance analysis and comparison. The source code for C-P map has been release at https://gitlab.com/csltstu/sunine., Comment: Submitted to Odyssey 2022

The doubly charmed baryon $\Xi^{++}_{cc}$ was observed by LHCb cooperation in 2017. The branching fractions of two-body doubly charmed baryon decays were predicted in the framework of rescattering mechanism, and some $SU(3)_F$ relations were investigated in the topological amplitudes. In this work, we study the correlation between topological diagram at quark level and rescattering triangle diagram at hadron level in the doubly charmed baryon decay. The completeness of our framework is confirmed from the fact that all the twelve possible structures of meson-baryon scattering appear once each in the the intermediate form between topological diagram and triangle diagram, topological-scattering diagram. It is found the triangle diagrams derived from the topological diagrams are consistent with the ones derived directly from the chiral Lagrangian. The relative magnitudes of rescattering contributions in the $C$, $C^\prime$, $E$, $E^\prime$, $P$ and $P^\prime$ diagrams extracted from $SU(3)_F$ symmetry are consistent with the numerical analysis in literature. Taking $\Xi^{++}_{cc}\to \Xi^+_c\pi^+$, $\Xi^{+}_{cc}\to \Xi^0_c\pi^+$ and $\Xi^{+}_{cc}\to \Xi^+_c\pi^0$ modes as examples, we show the isospin relation is satisfied in terms of triangle diagrams., Comment: 14 pages, 10 figures