DLUNet: Semi-supervised Learning based Dual-Light UNet for Multi-organ Segmentation

The manual ground truth of abdominal multi-organ is labor-intensive. In order to make full use of CT data, we developed a semi-supervised learning based dual-light UNet. In the training phase, it consists of two light UNets, which make full use of label and unlabeled data simultaneously by using consistent-based learning. Moreover, separable convolution and residual concatenation was introduced light UNet to reduce the computational cost. Further, a robust segmentation loss was applied to improve the performance. In the inference phase, only a light UNet is used, which required low time cost and less GPU memory utilization. The average DSC of this method in the validation set is 0.8718. The code is available in https://github.com/laihaoran/Semi-SupervisednnUNet.Comment: 13 page, 3 figure

A new One-time Password Method

AbstractOne-Time Passwords (OTP) can provide complete protection of the login-time authentication mechanism against replay attacks. In this paper, we propose TSOTP: a new effective simple OTP method that generates a unique passcode for each use. The calculation uses both time stamps and sequence numbers. A two-factor authentication prototype for mobile phones using this method has been developed and has been used in practice for a year

Privileged Knowledge Distillation for Sim-to-Real Policy Generalization

Reinforcement Learning (RL) has recently achieved remarkable success in robotic control. However, most RL methods operate in simulated environments where privileged knowledge (e.g., dynamics, surroundings, terrains) is readily available. Conversely, in real-world scenarios, robot agents usually rely solely on local states (e.g., proprioceptive feedback of robot joints) to select actions, leading to a significant sim-to-real gap. Existing methods address this gap by either gradually reducing the reliance on privileged knowledge or performing a two-stage policy imitation. However, we argue that these methods are limited in their ability to fully leverage the privileged knowledge, resulting in suboptimal performance. In this paper, we propose a novel single-stage privileged knowledge distillation method called the Historical Information Bottleneck (HIB) to narrow the sim-to-real gap. In particular, HIB learns a privileged knowledge representation from historical trajectories by capturing the underlying changeable dynamic information. Theoretical analysis shows that the learned privileged knowledge representation helps reduce the value discrepancy between the oracle and learned policies. Empirical experiments on both simulated and real-world tasks demonstrate that HIB yields improved generalizability compared to previous methods.Comment: 22 page

Long-tailed multi-label classification with noisy label of thoracic diseases from chest X-ray

Chest X-rays (CXR) often reveal rare diseases, demanding precise diagnosis. However, current computer-aided diagnosis (CAD) methods focus on common diseases, leading to inadequate detection of rare conditions due to the absence of comprehensive datasets. To overcome this, we present a novel benchmark for long-tailed multi-label classification in CXRs, encapsulating both common and rare thoracic diseases. Our approach includes developing the "LTML-MIMIC-CXR" dataset, an augmentation of MIMIC-CXR with 26 additional rare diseases. We propose a baseline method for this classification challenge, integrating adaptive negative regularization to address negative logits' over-suppression in tail classes, and a large loss reconsideration strategy for correcting noisy labels from automated annotations. Our evaluation on LTML-MIMIC-CXR demonstrates significant advancements in rare disease detection. This work establishes a foundation for robust CAD methods, achieving a balance in identifying a spectrum of thoracic diseases in CXRs. Access to our code and dataset is provided at:https://github.com/laihaoran/LTML-MIMIC-CXR

Nonconvex optimization for optimum retrieval of the transmission matrix of a multimode fiber

Transmission matrix (TM) allows light control through complex media such as multimode fibers (MMFs), gaining great attention in areas like biophotonics over the past decade. The measurement of a complex-valued TM is highly desired as it supports full modulation of the light field, yet demanding as the holographic setup is usually entailed. Efforts have been taken to retrieve a TM directly from intensity measurements with several representative phase retrieval algorithms, which still see limitations like slow or suboptimum recovery, especially under noisy environment. Here, a modified non-convex optimization approach is proposed. Through numerical evaluations, it shows that the nonconvex method offers an optimum efficiency of focusing with less running time or sampling rate. The comparative test under different signal-to-noise levels further indicates its improved robustness for TM retrieval. Experimentally, the optimum retrieval of the TM of a MMF is collectively validated by multiple groups of single-spot and multi-spot focusing demonstrations. Focus scanning on the working plane of the MMF is also conducted where our method achieves 93.6% efficiency of the gold standard holography method when the sampling rate is 8. Based on the recovered TM, image transmission through the MMF with high fidelity can be realized via another phase retrieval. Thanks to parallel operation and GPU acceleration, the nonconvex approach can retrieve an 8685$\times$1024 TM (sampling rate=8) with 42.3 s on a regular computer. In brief, the proposed method provides optimum efficiency and fast implementation for TM retrieval, which will facilitate wide applications in deep-tissue optical imaging, manipulation and treatment

Construction and Analysis of Ecological Security Patterns in the Southern Anhui Region of China from a Circuit Theory Perspective

Located in an important biodiversity conservation area in the Yangtze River Delta, the habitats of many species have been severely eroded because of human activities such as tourism development. There is no relevant species conservation plan in place in the region, and scientific guidance on ecosystem change and corridor construction is urgently needed. In this study, we first assess ecosystem service functions based on the InVEST model; then, we assess ecological sensitivity and identify landscape resistance surfaces by constructing ecosystem sensitivity indicators; finally, we construct ecological security patterns by combining landscape resistance surfaces and circuit theory identification. The main results are as follows: (1) The high value area of ecosystem services is located in the southwest, while the northeast part of the study area has lower ecosystem services, and there is a trade-off between the ecosystem services in the study area. (2) There are 38 ecological sources in southern Anhui, with a total area of more than 5742.79 km2, that are the basic guarantees of ecological security, mainly located in the northeast of the study area, and woodland and grassland are the most important components, accounting for 18.4% of the total study area. (3) The ecological security pattern in the study area consists of 63 ecological sources, 37 important corridors, and 26 potential corridors, of which there are 28 pinch point areas and 6 barrier point patches in the study area, mainly located within Huangshan City and Xuancheng City. We recommend that when implementing restoration and rehabilitation measures in the future, policy makers should give priority to pinch points and barrier areas.</p

Secrets of RLHF in Large Language Models Part I: PPO

Large language models (LLMs) have formulated a blueprint for the advancement of artificial general intelligence. Its primary objective is to function as a human-centric (helpful, honest, and harmless) assistant. Alignment with humans assumes paramount significance, and reinforcement learning with human feedback (RLHF) emerges as the pivotal technological paradigm underpinning this pursuit. Current technical routes usually include \textbf{reward models} to measure human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize policy model outputs, and \textbf{process supervision} to improve step-by-step reasoning capabilities. However, due to the challenges of reward design, environment interaction, and agent training, coupled with huge trial and error cost of large language models, there is a significant barrier for AI researchers to motivate the development of technical alignment and safe landing of LLMs. The stable training of RLHF has still been a puzzle. In the first report, we dissect the framework of RLHF, re-evaluate the inner workings of PPO, and explore how the parts comprising PPO algorithms impact policy agent training. We identify policy constraints being the key factor for the effective implementation of the PPO algorithm. Therefore, we explore the PPO-max, an advanced version of PPO algorithm, to efficiently improve the training stability of the policy model. Based on our main results, we perform a comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT. The absence of open-source implementations has posed significant challenges to the investigation of LLMs alignment. Therefore, we are eager to release technical reports, reward models and PPO code

Topologically-protected refraction of robust kink states in valley photonic crystals

Recently discovered valley photonic crystals (VPCs) mimic many of the unusual properties of two-dimensional gapped valleytronic materials such as bilayer graphene or MoS2. Of the utmost interest to optical communications is their ability to support topologically protected chiral edge (kink) states at the internal domain wall between two VPCs with spectrally overlapping bandgap zones and opposite half-integer valley-Chern indices. We experimentally demonstrate the robustness of the kink states in VPCs that support degenerate transverse-electric-like (TE) and transverse-magnetic-like (TM) topological phases, thus enabling polarization multiplexing in a single topological waveguide. The propagation direction of the kink states is locked to the valleys of the reverse Brave lattice and, therefore, cannot be reversed in the absence of inter-valley scattering. At the intersection between the internal domain wall and the external edge separating the VPCs from free space, the kink states are shown to exhibit >97% out-coupling efficiency into directional free-space beams. This constitutes the first experimental demonstration of meron-like valley-projected topological phases with half-integer valley-Chern indices.Comment: 19 pages, 4 figure

Graphene-Based Nanocomposites for Energy Storage

Since the first report of using micromechanical cleavage method to produce graphene sheets in 2004, graphene/graphene-based nanocomposites have attracted wide attention both for fundamental aspects as well as applications in advanced energy storage and conversion systems. In comparison to other materials, graphene-based nanostructured materials have unique 2D structure, high electronic mobility, exceptional electronic and thermal conductivities, excellent optical transmittance, good mechanical strength, and ultrahigh surface area. Therefore, they are considered as attractive materials for hydrogen (H2) storage and high-performance electrochemical energy storage devices, such as supercapacitors, rechargeable lithium (Li)-ion batteries, Li–sulfur batteries, Li–air batteries, sodium (Na)-ion batteries, Na–air batteries, zinc (Zn)–air batteries, and vanadium redox flow batteries (VRFB), etc., as they can improve the efficiency, capacity, gravimetric energy/power densities, and cycle life of these energy storage devices. In this article, recent progress reported on the synthesis and fabrication of graphene nanocomposite materials for applications in these aforementioned various energy storage systems is reviewed. Importantly, the prospects and future challenges in both scalable manufacturing and more energy storage-related applications are discussed