This paper introduces Genesis, the first compiler designed to support Hamiltonian Simulation on hybrid continuous-variable (CV) and discrete-variable (DV) quantum computing systems.Genesis is a two-level compilation system.At the first level, it decomposes an input Hamiltonian into basis gates using the native instruction set of the target hybrid CV-DV quantum computer.At the second level, it tackles the mapping and routing of qumodes/qubits to implement long-range interactions for the gates decomposed from the first level.Rather than a typical implementation that relies on SWAP primitives similar to qubit-based (or DV-only) systems, we propose an integrated design of connectivity-aware gate synthesis and beamsplitter SWAP insertion tailored for hybrid CV-DV systems.We also introduce an OpenQASM-like domain-specific language (DSL) named CVDV-QASM to represent Hamiltonian in terms of Pauli-exponentials and basic gate sequences from the hybrid CV-DV gate set.Genesis has successfully compiled several important Hamiltonians, including the Bose-Hubbard model, Z 2 -Higgs model, Hubbard-Holstein model, Heisenberg model and Electron-vibration coupling Hamiltonians, which are critical in domains like quantum field theory, condensed matter physics, and quantum chemistry.Our implementation is available at Genesis-CVDV-Compiler https://

The flexibility and portability characteristics have made containers a popular serverless environment for large model training in recent years. Unfortunately, these advantages render the network support for containerized large model training extremely challenging, due to the high dynamics of containers, the complex interplay between underlay and overlay networks, and the stringent requirements on failure detection and localization. Existing data center network debugging tools, which rely on comprehensive or opportunistic monitoring, are either inefficient or inaccurate in this setting.

The surge in multimedia content has led to the development of Multi-Modal Recommender Systems (MMRecs), which use diverse modalities-like text, images, videos, and audio-for more personalized recommendations. However, MMRecs struggle with the challenge of noisy data from the misalignment among modal content and the difference between modal and recommendation semantics, while traditional denoising methods fall short in addressing these issues due to the complexity of multi-modal data. To overcome this, we propose a universal guided in-sync distillation denoising framework for multi-modal recommndation (GUIDER), aimed at improving MMRecs by denoising user feedbacks. Specifically, GUIDER employs a re-calibration strategy to identify clean and noisy interactions from modal content. Furthermore, it incorporates a Denoising Bayesian Personalized Ranking (DBPR) loss function to denoise implicit user feedback. Finally, it utilizes a denoising knowledge distillation objective based on Optimal Transport (OT) distance to guide the mapping from modality representations to recommendation semantics spaces. GUIDER can be seamlessly integrated into existing MMRecs methods as a plug-and-play solution for recommendation denoising. Experiment results on four public datasets show its effectiveness and universality across various MMRecs.

Designing an effective representation learning method for multimodal sentiment analysis is a critical research area. The primary challenge is capturing shared and private information within a comprehensive modal representation, especially when dealing with uniform multimodal labels and raw feature fusion.To overcome this challenge, we propose a novel deep modal shared information learning module that utilizes the covariance matrix to capture shared information across modalities. Additionally, we introduce a label generation module based on a self-supervised learning strategy to capture the private information specific to each modality. Our module can be easily integrated into multimodal tasks and offers flexibility by allowing parameter adjustment to control the information exchange relationship between modes, facilitating the learning of private or shared information as needed. To further enhance performance, we employ a multi-task learning strategy that enables the model to focus on modal differentiation during training. We provide a detailed formulation derivation and feasibility proof for the design of the deep modal shared information learning module.To evaluate our approach, we conduct extensive experiments on three common multimodal sentiment analysis benchmark datasets. The experimental results validate the reliability of our model, demonstrating its effectiveness in capturing nuanced information in multimodal sentiment analysis tasks.

Anomaly detection, as one of the most critical tasks in hyperspectral image (HSI) processing, has been paid extensive attention in the past decades. The classical work of hyperspectral anomaly detection, sample correlation matrix R-based anomaly detection (R-AD), can achieve desirable detection accuracy, but its processing complexity remains a challenging problem. Real-time anomaly detection methods speed up the procedure through processing the data acquired by grating splitting or acousto-optic tunable filter (AOTF)-based imaging systems in the manner of pixel-by-pixel, line-by-line, or band-by-band. However, in practical industrial scenarios, the multiarray filter spectral imaging system is generally leveraged to avoid high construction and maintenance costs, which acquires multiline multiband (MLMB) data lacking a real-time processing version. In addition, the background of industrial assembly lines is chaotic, and the changes between batches are rapid, both increasing the difficulty of accurate anomaly detection online. To cope with these challenges, a real-time online processing version [real-time multiline multiband R anomaly detection (RTMLMB-RAD)] of R-AD based on MLMB data is proposed here for the first time. Specifically, a multiline multiband correlation matrix (MLMBCM) is designed to update the detector of R-AD recursively with MLMB data acquisition. Since MLMBCM relies only on the previous result and the current data, a large amount of data storage and complex matrix inversion calculation are avoided. At the same time, local anomaly detection mode is adopted to improve the sensitivity of anomalies in different batches and different types of products. The experimental results on six public hyperspectral datasets demonstrate that the proposed RTMLMB-RAD algorithm outperforms other state-of-the-art methods in terms of time cost and detection accuracy. Particularly, the proposed algorithm was tested on the tobacco primary processing line with a detection rate of 87% and a false alarm rate of 10% at a speed of 3 m/s, further verifying the algorithm’s feasibility and effectiveness in practical industrial scenarios.

Recognizing the actions performed in a video is challenging for an intelligent system since there are wide variations and enormous information in the video. Attention mechanism pays attention to key target areas, ignores irrelevant information and extracts more discriminant features. In recent years, attention mechanism has been introduced into video recognition. Although a rich literature has been spawned, most of the research on attention aims to aggregate local features by attention. Instead of feature aggregation, we propose to aggregate decisions based on local spatio-temporal attention regions for action recognition, which is inspired by ensemble learning. The proposed decision fusion module is easy to interpret and architecture-independent. In this article, the regions around the body joints are regarded as the key regions. We use the corresponding regions of the body joints in the 3-D feature maps as the basic local features for local classification. Finally, all the local classification results are combined to make a global decision. Furthermore, when training the network, we can selectively add supervision to the local and global decisions. We experimentally show that the proposed mechanism can improve the recognition performance on multiple datasets which demonstrates its effectiveness.

The technique of transforming voices in order to hide the real identity of a speaker is called voice disguise, among which automatic voice disguise (AVD) by modifying the spectral and temporal characteristics of voices with miscellaneous algorithms are easily conducted with softwares accessible to the public. AVD has posed great threat to both human listening and automatic speaker verification (ASV). In this paper, we have found that ASV is not only a victim of AVD but could be a tool to beat some simple types of AVD. Firstly, three types of AVD, pitch scaling, vocal tract length normalization (VTLN) and voice conversion (VC), are introduced as representative methods. State-of-the-art ASV methods are subsequently utilized to objectively evaluate the impact of AVD on ASV by equal error rates (EER). Moreover, an approach to restore disguised voice to its original version is proposed by minimizing a function of ASV scores w.r.t. restoration parameters. Experiments are then conducted on disguised voices from Voxceleb, a dataset recorded in real-world noisy scenario. The results have shown that, for the voice disguise by pitch scaling, the proposed approach obtains an EER around 7% comparing to the 30% EER of a recently proposed baseline using the ratio of fundamental frequencies. The proposed approach generalizes well to restore the disguise with nonlinear frequency warping in VTLN by reducing its EER from 34.3% to 18.5%. However, it is difficult to restore the source speakers in VC by our approach, where more complex forms of restoration functions or other paralinguistic cues might be necessary to restore the nonlinear transform in VC. Finally, contrastive visualization on ASV features with and without restoration illustrate the role of the proposed approach in an intuitive way.