Semi-supervised Deep Generative Modelling of Incomplete Multi-Modality Emotional Data

There are threefold challenges in emotion recognition. First, it is difficult to recognize human's emotional states only considering a single modality. Second, it is expensive to manually annotate the emotional data. Third, emotional data often suffers from missing modalities due to unforeseeable sensor malfunction or configuration issues. In this paper, we address all these problems under a novel multi-view deep generative framework. Specifically, we propose to model the statistical relationships of multi-modality emotional data using multiple modality-specific generative networks with a shared latent space. By imposing a Gaussian mixture assumption on the posterior approximation of the shared latent variables, our framework can learn the joint deep representation from multiple modalities and evaluate the importance of each modality simultaneously. To solve the labeled-data-scarcity problem, we extend our multi-view model to semi-supervised learning scenario by casting the semi-supervised classification problem as a specialized missing data imputation task. To address the missing-modality problem, we further extend our semi-supervised multi-view model to deal with incomplete data, where a missing view is treated as a latent variable and integrated out during inference. This way, the proposed overall framework can utilize all available (both labeled and unlabeled, as well as both complete and incomplete) data to improve its generalization ability. The experiments conducted on two real multi-modal emotion datasets demonstrated the superiority of our framework.Comment: arXiv admin note: text overlap with arXiv:1704.07548, 2018 ACM Multimedia Conference (MM'18

Ad-UDDI: An Active and Distributed Service Registry

Abstract. In SOA (Service Oriented Architecture), web service providers use service registries to publish services and requestors use registries to find them. The major current service registry specifications, UDDI (Universal Description, Discovery and Integration), has the following drawbacks. First, it replicates all public service publications in all UBR (Universal Business Registry) nodes, which is not scalable and efficient, and second, it collects service information in a passive manner, which means it waits for service publication, updating or discovery request passively and thus cannot guarantee the real-time validity of the services information. In this paper, we propose an active and distributed UDDI architecture called Ad-UDDI, which extends and organizes the private or semi-private UDDIs based on industry classifications. Further, Ad-UDDI adopts an active monitoring mechanism, so that service information can be updated automatically and the service requestors may find the latest service information conveniently. We evaluate Ad-UDDI by comprehensive simulations and experimental results show that it outperforms existing approaches significantly.

What Makes for Good Visual Instructions? Synthesizing Complex Visual Reasoning Instructions for Visual Instruction Tuning

Visual instruction tuning is an essential approach to improving the zero-shot generalization capability of Multi-modal Large Language Models (MLLMs). A surge of visual instruction datasets with various focuses and characteristics have been proposed recently, enabling MLLMs to achieve surprising results on evaluation benchmarks. To develop more capable MLLMs, in this paper, we aim to investigate a more fundamental question: ``what makes for good visual instructions?''. By conducting a comprehensive empirical study, we find that instructions focused on complex visual reasoning tasks are particularly effective in improving the performance of MLLMs on evaluation benchmarks. Building upon this finding, we design a systematic approach to automatically creating high-quality complex visual reasoning instructions. Our approach employs a synthesis-complication-reformulation paradigm, leveraging multiple stages to gradually increase the complexity of the instructions while guaranteeing quality. Based on this approach, we create the synthetic visual reasoning instruction dataset consisting of 32K examples, namely ComVint, and fine-tune four MLLMs on it. Experimental results demonstrate that our dataset consistently enhances the performance of all the compared MLLMs, e.g., improving the performance of MiniGPT-4 and BLIP-2 on MME-Cognition by 32.6% and 28.8%, respectively. Our code and data are publicly available at the link: https://github.com/RUCAIBox/ComVint.Comment: Work in progres

Observation of first-order quantum phase transitions and ferromagnetism in twisted double bilayer graphene

Twisted graphene multilayers are highly tunable flatband systems for developing new phases of matter. Thus far, while orbital ferromagnetism has been observed in valley polarized phases, the long-range orders of other correlated phases as well as the quantum phase transitions between different orders mostly remain unknown. Here, we report an observation of Coulomb interaction driven first-order quantum phase transitions and ferromagnetism in twisted double bilayer graphene (TDBG). At zero magnetic field, the transitions are revealed in a series of step-like abrupt resistance jumps with prominent hysteresis loop when either the displacement field (D) or the carrier density (n) is tuned across symmetry-breaking boundary near half filling, indicating a formation of ordered domains. It is worth noting that the good turnability and switching of these states gives a rise to a memory performance with a large on/off ratio. Moreover, when both spin and valley play the roles at finite magnetic field, we observe abundant first-order quantum phase transitions among normal metallic states from charge neutral point, orbital ferromagnetic states from quarter filling, and spin-polarized states from half filling. We interpret these first-order phase transitions in the picture of phase separations and spin domain percolations driven by multi-field tunable Coulomb interactions, in agreement with Lifshitz transition from Hartree-Fock calculations. The observed multi-filed tunable domain structure and its hysteresis resembles the characteristics of multiferroics, revealing intriguing magnetoelectric properties. Our result enriches the correlated phase diagram in TDBG for discovering novel exotic phases and quantum phase transitions, and it would benefit other twisted moir\'e systems as well

Layer-by-Layer Epitaxy of Multilayer MoS2 Wafers

Two-dimensional (2D) semiconductor of MoS2 has great potential for advanced electronics technologies beyond silicon1-9. So far, high-quality monolayer MoS2 wafers10-12 are already available and various demonstrations from individual transistors to integrated circuits have also been shown13-15. In addition to the monolayer, multilayers have narrower band gaps but improved carrier mobilities and current capacities over the monolayer5,16-18. However, achieving high-quality multilayer MoS2 wafers remains a challenge. Here we report the growth of high quality multilayer MoS2 4-inch wafers via the layer-by-layer epitaxy process. The epitaxy leads to well-defined stacking orders between adjacent epitaxial layers and offers a delicate control of layer numbers up to 6. Systematic evaluations on the atomic structures and electronic properties were carried out for achieved wafers with different layer numbers. Significant improvements on device performances were found in thicker-layer field effect transistors (FETs), as expected. For example, the average field-effect mobility ({\mu}FE) at room temperature (RT) can increase from ~80 cm2V-1s-1 for monolayer to ~110/145 cm2V-1s-1 for bilayer/trilayer devices. The highest RT {\mu}FE=234.7 cm2V-1s-1 and a record-high on-current densities of 1.704 mA{\mu}m-1 at Vds=2 V were also achieved in trilayer MoS2 FETs with a high on/off ratio exceeding 107. Our work hence moves a step closer to practical applications of 2D MoS2 in electronics.Comment: 13 pages,4 Figure

Room-temperature correlated states in twisted bilayer MoS2_2

Moir\'e superlattices have emerged as an exciting condensed-matter quantum simulator for exploring the exotic physics of strong electronic correlations. Notable progress has been witnessed, but such correlated states are achievable usually at low temperatures. Here, we report the transport evidences of room-temperature correlated electronic states and layer-hybridized SU(4) Hubbard model simulator in AB-stacked MoS$_2$ homo-bilayer moir\'e superlattices. Correlated insulating states at moir\'e band filling factors v = 1, 2, 3 are unambiguously established in twisted bilayer MoS$_2$. Remarkably, the correlated electronic states can persist up to a record-high critical temperature of over 285 K. The realization of room-temperature correlated states in twisted bilayer MoS$_2$ can be understood as the cooperation effects of the stacking-specific atomic reconstruction and the resonantly enhanced interlayer hybridization, which largely amplify the moir\'e superlattice effects on electronic correlations. Furthermore, extreme large non-linear Hall responses up to room-temperature are uncovered near correlated insulating states, demonstrating the quantum geometry of moir\'e flat conduction band.Comment: 13 pages, 3 figure

The oyster genome reveals stress adaptation and complexity of shell formation

The Pacific oyster Crassostrea gigas belongs to one of the most species-rich but genomically poorly explored phyla, the Mollusca. Here we report the sequencing and assembly of the oyster genome using short reads and a fosmid-pooling strategy, along with transcriptomes of development and stress response and the proteome of the shell. The oyster genome is highly polymorphic and rich in repetitive sequences, with some transposable elements still actively shaping variation. Transcriptome studies reveal an extensive set of genes responding to environmental stress. The expansion of genes coding for heat shock protein 70 and inhibitors of apoptosis is probably central to the oyster's adaptation to sessile life in the highly stressful intertidal zone. Our analyses also show that shell formation in molluscs is more complex than currently understood and involves extensive participation of cells and their exosomes. The oyster genome sequence fills a void in our understanding of the Lophotrochozoa. © 2012 Macmillan Publishers Limited. All rights reserved

CROWN FlowEngine: A GPEL-Based Grid Workflow Engine

Abstract. Currently some complex grid applications developing often need orchestrate multiple diverse grid services into a workflow of tasks that can submit for executing on the grid environment. In this paper, we present CROWN FlowEngine—a GPEL-based grid workflow engine for executing grid workflow instances. Besides basic functions of a conventional BPEL4WSbased workflow engine, CROWN FlowEngine has many features including hierarchical processing mechanism, multiple types of task scheduling, transaction processing, etc, which are of paramount importance to supporting workflow instances using GPEL language. CROWN FlowEngine will be adopted and widely deployed in CROWN Grid environment to support a wide range of service grid applications integration. We conduct several experiments to evaluate the performance of CROWN FlowEngine, and the results of comparing our work with GWES are presented as well.

RestrictionDigest: A powerful Perl module for simulating genomic restriction digests

Background: Reduced-representation sequencing technology is widely used in genotyping for its economical and efficient features. A popular way to construct the reduced-representation sequencing libraries is to digest the genomic DNA with restriction enzymes. A key factor of this method is to determine the restriction enzyme(s). But there are few computer programs which can evaluate the usability of restriction enzymes in reduced-representation sequencing. SimRAD is an R package which can simulate the digestion of DNA sequence by restriction enzymes and return enzyme loci number as well as fragment number. But for linkage mapping analysis, enzyme loci distribution is also an important factor to evaluate the enzyme. For phylogenetic studies, comparison of the enzyme performance across multiple genomes is important. It is strongly needed to develop a simulation tool to implement these functions. Results: Here, we introduce a Perl module named RestrictionDigest with more functions and improved performance. It can analyze multiple genomes at one run and generate concise comparison of enzyme performance across the genomes. It can simulate single-enzyme digestion, double-enzyme digestion and size selection process and generate comprehensive information of the simulation including enzyme loci number, fragment number, sequences of the fragments, positions of restriction sites on the genome, the coverage of digested fragments on different genome regions and detailed fragment length distribution. Conclusions: RestrictionDigest is an easy-to-use Perl module with flexible parameter settings. With the help of the information produced by the module, researchers can easily determine the most appropriate enzymes to construct the reduced-representation libraries to meet their experimental requirements