Lifelong-MonoDepth: Lifelong Learning for Multi-Domain Monocular Metric Depth Estimation

With the rapid advancements in autonomous driving and robot navigation, there is a growing demand for lifelong learning models capable of estimating metric (absolute) depth. Lifelong learning approaches potentially offer significant cost savings in terms of model training, data storage, and collection. However, the quality of RGB images and depth maps is sensor-dependent, and depth maps in the real world exhibit domain-specific characteristics, leading to variations in depth ranges. These challenges limit existing methods to lifelong learning scenarios with small domain gaps and relative depth map estimation. To facilitate lifelong metric depth learning, we identify three crucial technical challenges that require attention: i) developing a model capable of addressing the depth scale variation through scale-aware depth learning, ii) devising an effective learning strategy to handle significant domain gaps, and iii) creating an automated solution for domain-aware depth inference in practical applications. Based on the aforementioned considerations, in this paper, we present i) a lightweight multi-head framework that effectively tackles the depth scale imbalance, ii) an uncertainty-aware lifelong learning solution that adeptly handles significant domain gaps, and iii) an online domain-specific predictor selection method for real-time inference. Through extensive numerical studies, we show that the proposed method can achieve good efficiency, stability, and plasticity, leading the benchmarks by 8% to 15%

Explicit Attention-Enhanced Fusion for RGB-Thermal Perception Tasks

Recently, RGB-Thermal based perception has shown significant advances. Thermal information provides useful clues when visual cameras suffer from poor lighting conditions, such as low light and fog. However, how to effectively fuse RGB images and thermal data remains an open challenge. Previous works involve naive fusion strategies such as merging them at the input, concatenating multi-modality features inside models, or applying attention to each data modality. These fusion strategies are straightforward yet insufficient. In this paper, we propose a novel fusion method named Explicit Attention-Enhanced Fusion (EAEF) that fully takes advantage of each type of data. Specifically, we consider the following cases: i) both RGB data and thermal data, ii) only one of the types of data, and iii) none of them generate discriminative features. EAEF uses one branch to enhance feature extraction for i) and iii) and the other branch to remedy insufficient representations for ii). The outputs of two branches are fused to form complementary features. As a result, the proposed fusion method outperforms state-of-the-art by 1.6\% in mIoU on semantic segmentation, 3.1\% in MAE on salient object detection, 2.3\% in mAP on object detection, and 8.1\% in MAE on crowd counting. The code is available at https://github.com/FreeformRobotics/EAEFNet

Towards Better Accuracy-efficiency Trade-offs: Divide and Co-training

The width of a neural network matters since increasing the width will necessarily increase the model capacity. However, the performance of a network does not improve linearly with the width and soon gets saturated. In this case, we argue that increasing the number of networks (ensemble) can achieve better accuracy-efficiency trade-offs than purely increasing the width. To prove it, one large network is divided into several small ones regarding its parameters and regularization components. Each of these small networks has a fraction of the original one's parameters. We then train these small networks together and make them see various views of the same data to increase their diversity. During this co-training process, networks can also learn from each other. As a result, small networks can achieve better ensemble performance than the large one with few or no extra parameters or FLOPs. Small networks can also achieve faster inference speed than the large one by concurrent running on different devices. We validate our argument with 8 different neural architectures on common benchmarks through extensive experiments. The code is available at \url{https://github.com/mzhaoshuai/Divide-and-Co-training}

Pegylated derivatives of recombinant human arginase (rhArg1) for sustained in vivo activity in cancer therapy: preparation, characterization and analysis of their pharmacodynamics in vivo and in vitro and action upon hepatocellular carcinoma cell (HCC)

<p>Abstract</p> <p>Background</p> <p>Protein used in medicine, e.g. interferon, are immunogenic and quickly broken down by the body. Pegylation is a recognized way of preserving their integrity and reducing immune reactions, and works well with enzymes used to degrade amino acids, a recent focus of attention in controlling cancer growth. Of the two arginine-degrading enzymes being explored clinically, arginine deiminase is a decidedly foreign mycoplasm-derived enzyme, whereas human arginase 1 is a native liver enzyme. Both have been pegylated, the former with adjuncts of 20 kD, the latter with 5 kD PEG. Pegylation is done by several different methods, not all of which are satisfactory or desirable.</p> <p>Methods</p> <p>The preparation of novel polyethylene glycol (PEG) derivatives for modifying proteins is described, but directed specifically at pegylation of recombinant human arginase 1 (rhArg1). rhArg1 expressed in <it>Escherichia coli </it>was purified and coupled in various ways with 5 different PEG molecules to compare their protective properties and the residual enzyme activity, using hepatocellular cell lines both in vitro and in vivo.</p> <p>Results</p> <p>Methoxypolyethylene glycol-succinimidyl propionate (mPEG-SPA 5,000) coupled with very high affinity under mild conditions. The resulting pegylated enzyme (rhArg1-peg_{5,000 mw}) had up to 6 PEG chains of 5K length which not only protected it from degradation and any residual immunogenicity, but most importantly let it retain >90% of its native catalytic activity. It remained efficacious in depleting arginine in rats after a single ip injection of 1,500 U of the conjugate as the native enzyme, plasma arginine falling to >0.05 μM from ~170 μM within 20 min and lasting 6 days. The conjugate had almost the same efficacy as unpegylated rhArg1 on 2 cultured human liver cancer (HCC) cell lines. It was considerably more effective than 4 other pegylated conjugates prepared.</p> <p>Conclusion</p> <p>Valuable data on the optimization of the pegylation procedure and choice of ligand that best stabilizes the enzyme arginase 1 are presented, a protocol that should equally fit many other enzymes and proteins. It is a long lasting arginine-depleting enzyme in vivo which will greatly improve its use in anti-cancer therapy.</p

Wigner Crystals Phases in Bilayer Quantum Hall Systems

(This is a substantially shortened version of the original abstract:) The Wigner crystal phase diagram of the bilayer systems have been studied using variational methods. Five crystal phases are obtained. As the layer spacing increases, the system will undergo a sequence of phase transitions. A common feature of most bilayer Wigner crystals is that they have mixed (pseudo-spin) ferromagnetic and antiferromagnetic order.Comment: 19 figures. Figures will be provided upon request. Submitted in PRB in Nov 94