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Recent Large Multi-Modal Models (LMMs) have made significant advancements in multi-modal alignment by employing lightweight connection modules to facilitate the representation and fusion of knowledge from existing pre-trained uni-modal models. However, these methods still rely on modality-specific and direction-specific connectors, leading to compartmentalized knowledge representations and reduced computational efficiency, which limits the model's ability to form unified multi-modal representations. To address these issues, we introduce a novel training framework, Alt-MoE, which employs the Mixture of Experts (MoE) as a unified multi-directional connector across modalities, and employs a multi-step sequential alternating unidirectional alignment strategy, which converges to bidirectional alignment over iterations. The extensive empirical studies revealed the following key points: 1) Alt-MoE achieves competitive results by integrating diverse knowledge representations from uni-modal models. This approach seamlessly fuses the specialized expertise of existing high-performance uni-modal models, effectively synthesizing their domain-specific knowledge into a cohesive multi-modal representation. 2) Alt-MoE efficiently scales to new tasks and modalities without altering its model architecture or training strategy. Furthermore, Alt-MoE operates in latent space, supporting vector pre-storage and real-time retrieval via lightweight multi-directional MoE, thereby facilitating massive data processing. Our methodology has been validated on several well-performing uni-modal models (LLAMA3, Qwen2, and DINOv2), achieving competitive results on a wide range of downstream tasks and datasets., Comment: work in progress

The range and speed of a moving object can be ascertained using the sensing technique known as light detection and ranging (LiDAR). It has recently been suggested that quantum LiDAR, which uses entangled states of light, can enhance the capabilities of LiDAR. Entangled pulsed light is used in prior quantum LiDAR approaches to assess both range and velocity at the same time using the pulses' time of flight and Doppler shift. The entangled pulsed light generation and detection, which are crucial for pulsed quantum LiDAR, are often inefficient. Here, we study a quantum LiDAR that operates on a frequency-modulated continuous wave (FMCW), as opposed to pulses. We first outline the design of the quantum FMCW LiDAR using entangled frequency-modulated photons in a Mach-Zehnder interferometer, and we demonstrate how it can increase accuracy and resolution for range and velocity measurements by $\sqrt{n}$ and $n$, respectively, with $n$ entangled photons. We also demonstrate that quantum FMCW LiDAR may perform simultaneous measurements of the range and velocity without the need for quantum pulsed compression, which is necessary in pulsed quantum LiDAR. Since the generation of entangled photons is the only inefficient nonlinear optical process needed, the quantum FMCW LiDAR is better suited for practical implementations. Additionally, most measurements in the quantum FMCW LiDAR can be carried out electronically by down-converting optical signal to microwave region.

Deep learning based semantic communication(DLSC) systems have shown great potential of making wireless networks significantly more efficient by only transmitting the semantics of the data. However, the open nature of wireless channel and fragileness of neural models cause DLSC systems extremely vulnerable to various attacks. Traditional wireless physical layer key (PLK), which relies on reciprocal channel and randomness characteristics between two legitimate users, holds the promise of securing DLSC. The main challenge lies in generating secret keys in the static environment with ultra-low/zero rate. Different from prior efforts that use relays or reconfigurable intelligent surfaces (RIS) to manipulate wireless channels, this paper proposes a novel physical layer semantic encryption scheme by exploring the randomness of bilingual evaluation understudy (BLEU) scores in the field of machine translation, and additionally presents a novel semantic obfuscation mechanism to provide further physical layer protections. Specifically, 1) we calculate the BLEU scores and corresponding weights of the DLSC system. Then, we generate semantic keys (SKey) by feeding the weighted sum of the scores into a hash function. 2) Equipped with the SKey, our proposed subcarrier obfuscation is able to further secure semantic communications with a dynamic dummy data insertion mechanism. Experiments show the effectiveness of our method, especially in the static wireless environment.

Fully-supervised salient object detection (SOD) methods have made great progress, but such methods often rely on a large number of pixel-level annotations, which are time-consuming and labour-intensive. In this paper, we focus on a new weakly-supervised SOD task under hybrid labels, where the supervision labels include a large number of coarse labels generated by the traditional unsupervised method and a small number of real labels. To address the issues of label noise and quantity imbalance in this task, we design a new pipeline framework with three sophisticated training strategies. In terms of model framework, we decouple the task into label refinement sub-task and salient object detection sub-task, which cooperate with each other and train alternately. Specifically, the R-Net is designed as a two-stream encoder-decoder model equipped with Blender with Guidance and Aggregation Mechanisms (BGA), aiming to rectify the coarse labels for more reliable pseudo-labels, while the S-Net is a replaceable SOD network supervised by the pseudo labels generated by the current R-Net. Note that, we only need to use the trained S-Net for testing. Moreover, in order to guarantee the effectiveness and efficiency of network training, we design three training strategies, including alternate iteration mechanism, group-wise incremental mechanism, and credibility verification mechanism. Experiments on five SOD benchmarks show that our method achieves competitive performance against weakly-supervised/unsupervised methods both qualitatively and quantitatively., Comment: Accepted by IEEE Transactions on Circuits and Systems for Video Technology 2022

This work theoretically study the Bose-Hubbard model in a ring geometry in a rotating frame. We obtain an effective Hamiltonian by using unitary transformation, where the effect of the rotating reference frame is introducing additional phases to the hopping constant. Within the mean-field theory, the phase transition edge of the Bose-Hubbard model not only depends on the particle numbers and the ring radius, but also depends on the rotation velocity. Therefore, we propose a sensing method of the rotation velocity using the phase transition edge of the Bose-Hubbard model. At the exact phase transition edge where this sensing method is most sensitive, the resolution depends on the rotation velocity, the particle numbers and the ring radius, while is independent of the parameters in the Bose-Hubbard model such as the hopping constant and the on-site interaction., Comment: 6 pages, 6 figures

Recent work has shown that Just-In-Time (JIT) compilation can introduce timing side-channels to constant-time programs, which would otherwise be a principled and effective means to counter timing attacks. In this paper, we propose a novel approach to eliminate JIT-induced leaks from these programs. Specifically, we present an operational semantics and a formal definition of constant-time programs under JIT compilation, laying the foundation for reasoning about programs with JIT compilation. We then propose to eliminate JIT-induced leaks via a fine-grained JIT compilation for which we provide an automated approach to generate policies and a novel type system to show its soundness. We develop a tool DeJITLeak for Java based on our approach and implement the fine-grained JIT compilation in HotSpot. Experimental results show that DeJITLeak can effectively and efficiently eliminate JIT-induced leaks on three datasets used in side-channel detection

We present a simple yet effective method to address loop closure detection in simultaneous localisation and mapping using local 3D deep descriptors (L3Ds). L3Ds are emerging compact representations of patches extracted from point clouds that are learnt from data using a deep learning algorithm. We propose a novel overlap measure for loop detection by computing the metric error between points that correspond to mutually-nearest-neighbour descriptors after registering the loop candidate point cloud by its estimated relative pose. This novel approach enables us to accurately detect loops and estimate six degrees-of-freedom poses in the case of small overlaps. We compare our L3D-based loop closure approach with recent approaches on LiDAR data and achieve state-of-the-art loop closure detection accuracy. Additionally, we embed our loop closure approach in RESLAM, a recent edge-based SLAM system, and perform the evaluation on real-world RGBD-TUM and synthetic ICL datasets. Our approach enables RESLAM to achieve a better localisation accuracy compared to its original loop closure strategy. Our project page is available at github.com/yiming107/l3d_loop_closure., Comment: This work is accepted for publication in IEEE Robotics and Automation Letters

In this work, a hybrid integration of few-layer transition metal dichalcogenides (TMDCs) and ferroelectric polymer is designed to achieve passive control of optical properties in-situ. The electrical polarization in ferroelectric P(VDF-TrFE) polymer can regulate the photoluminescence (PL) in few-layer TMDCs. The total PL intensity is substantially suppressed or enhanced under opposite polarization in bilayer WSe2. This is because electrons transfer between valley K and {\Lambda} in the conduction band induced by the built-in electric field in P(VDF-TrFE) polymer. This charge transfer further changes the competing dynamics between direct and indirect exciton recombination path and overall optical radiation efficiency. We also illustrate that the engineered PL originates from external electric field dependent transferred electron effect. The theoretical result matches the experimental data well. This work demonstrates a device platform in which passive regulation is achieved using 2D TMDCs modulated by polarized ferroelectric materials.