Large Text-to-Image(T2I) diffusion models have shown a remarkable capability to produce photorealistic and diverse images based on text inputs. However, existing works only support limited language input, e.g., English, Chinese, and Japanese, leaving users beyond these languages underserved and blocking the global expansion of T2I models. Therefore, this paper presents AltDiffusion, a novel multilingual T2I diffusion model that supports eighteen different languages. Specifically, we first train a multilingual text encoder based on the knowledge distillation. Then we plug it into a pretrained English-only diffusion model and train the model with a two-stage schema to enhance the multilingual capability, including concept alignment and quality improvement stage on a large-scale multilingual dataset. Furthermore, we introduce a new benchmark, which includes Multilingual-General-18(MG-18) and Multilingual-Cultural-18(MC-18) datasets, to evaluate the capabilities of T2I diffusion models for generating high-quality images and capturing culture-specific concepts in different languages. Experimental results on both MG-18 and MC-18 demonstrate that AltDiffusion outperforms current state-of-the-art T2I models, e.g., Stable Diffusion in multilingual understanding, especially with respect to culture-specific concepts, while still having comparable capability for generating high-quality images. All source code and checkpoints could be found in https://github.com/superhero-7/AltDiffuson.

Practical Document Visual Question Answering (DocVQA) needs not only to recognize and extract the document contents, but also reason on them for answering questions. However, previous DocVQA data mainly focuses on in-line questions, where the answers could be directly extracted after locating keywords in the documents, which needs less reasoning. This paper therefore builds a large-scale dataset named Region-based Document VQA (RDVQA), which includes more practical questions for DocVQA. We then propose a novel Reason-over-In-region-Question-answering (ReIQ) model for addressing the problems. It is a pre-training-based model, where a Spatial-Token Pre-trained Model (STPM) is employed as the backbone. Two novel pre-training tasks, Masked Text Box Regression and Shuffled Triplet Reconstruction, are proposed to learn the entailment relationship between text blocks and tokens as well as contextual information, respectively. Moreover, a DocVQA State Tracking Module (DocST) is also proposed to track the DocVQA state in the fine-tuning stage. Experimental results show that our model improves the performance onRDVQA significantly, although more work should be done for practical DocVQA as shown inRDVQA.

For Virtual Desktop Infrastructure (VDI) system, effective resource management is rather important where turning off spare virtual machines would help save running cost while maintaining sufficient virtual machines is essential to secure satisfactory user experience. Current VDI resource management strategy works in apassivemanner by either reactively driving available capacity based on user demands or following manually configured schedules, which may lead to unnecessary running costs or unsatisfactory user experience. In this article, we propose a first attempt toward proactive VDI resource management, where two adaptive learning approaches for VDI workload prediction are proposed by learning from multi-grained historical features. Fornon-persistentdesktop pool, based on the aggregation session count of pool-sharing users, theCAFEapproach induces a pool-level workload predictive model by utilizing coarse-to-fine historical features extracted from aggregation workload data. Forpersistentdesktop pool, based on the session connection status of individual users within the same pool, theSOUPapproach induces user-level workload predictive model by incorporating encoded multi-grained features extracted from the logon behavior of individual users into an aggregation pool-level model. Extensive experiments on datasets of real VDI customers and electricity load evidently verify the effectiveness of the proposed adaptive approaches for VDI workload prediction as well as other workload prediction tasks.

Different to traditional on-premise VDI , the virtual desktops in DaaS (Desktop as a Service) are hosted in public cloud where virtual machines are charged based on usage. Accordingly, an adaptive power management system which can turn off spare virtual machines without sacrificing end user experience is of significant customer value as it can greatly help reduce the running cost. Generally, logon behavior modeling for VDI users serves as the key enabling-technique to fulfill intelligent power management. Prior attempts work by modeling logon behavior in a user-dependent manner with tailored single-instance feature representation, where the strong relationships among pool-sharing VDI users are ignored in the modeling framework. In this paper, a novel formulation towards VDI user logon behavior modeling is proposed by employing the multi-instance multi-label (MIML) techniques. Specifically, each user is grouped with supporting users whose behaviors are jointly modeled in the feature space with multi-instance representation as well as in the output space with multi-label prediction. The resulting MIML formulation is optimized by adapting the popular MIML boosting procedure via balanced error-rate minimization. Experimental studies on real VDI customers' data clearly validate the effectiveness of the proposed MIML-based approach against state-of-the-art VDI user logon behavior modeling techniques.

It is demonstrated that sufficient and reasonable utilization of the constrained horizontal and vertical routing resources is the key to achieving a high completion rate in this one-and-one-half layer routing approach. By using the minimum intersection graph to order the nets, the number of intersections among the nets is minimum, the number of intersection columns is as small as possible, and the total length of poly-bars used in wiring is as short as possible. From this the requirements for the horizontal and vertical routing resources are as few as possible. A reasonable assignment of the horizontal routing resources can be achieved by operating the active track matrices of the nets at the predicted columns, while the optimal assignment of the vertical routing resources is obtained by using the cluster assignment algorithm of the incidence intersection columns. Guided by the wiring tracks in the predicted columns and using the poly-bars assigned, the channel routing can be implemented with high performance by scanning the channel from left to right and column by column.>