RepBNN: towards a precise Binary Neural Network with Enhanced Feature Map via Repeating

Binary neural network (BNN) is an extreme quantization version of convolutional neural networks (CNNs) with all features and weights mapped to just 1-bit. Although BNN saves a lot of memory and computation demand to make CNN applicable on edge or mobile devices, BNN suffers the drop of network performance due to the reduced representation capability after binarization. In this paper, we propose a new replaceable and easy-to-use convolution module RepConv, which enhances feature maps through replicating input or output along channel dimension by $\beta$ times without extra cost on the number of parameters and convolutional computation. We also define a set of RepTran rules to use RepConv throughout BNN modules like binary convolution, fully connected layer and batch normalization. Experiments demonstrate that after the RepTran transformation, a set of highly cited BNNs have achieved universally better performance than the original BNN versions. For example, the Top-1 accuracy of Rep-ReCU-ResNet-20, i.e., a RepBconv enhanced ReCU-ResNet-20, reaches 88.97% on CIFAR-10, which is 1.47% higher than that of the original network. And Rep-AdamBNN-ReActNet-A achieves 71.342% Top-1 accuracy on ImageNet, a fresh state-of-the-art result of BNNs. Code and models are available at:https://github.com/imfinethanks/Rep_AdamBNN.Comment: This paper has absolutely nothing to do with repvgg, rep means repeatin

Study of residual artificial neural network for particle identification in the CEPC high-granularity calorimeter prototype

Particle Identification (PID) plays a central role in associating the energy depositions in calorimeter cells with the type of primary particle in a particle flow oriented detector system. In this paper, we propose novel PID methods based on the Residual Network (ResNet) architecture which enable the training of very deep networks, bypass the need to reconstruct feature variables, and ensure the generalization ability among various geometries of detectors, to classify electromagnetic showers and hadronic showers. Using Geant4 simulation samples with energy ranging from 5 GeV to 120 GeV, the efficacy of Residual Connections is validated and the performance of our model is compared with Boosted Decision Trees (BDT) and other pioneering Artificial Neural Network (ANN) approaches. In shower classification, we observe an improvement in background rejection over a wide range of high signal efficiency ($> 95\%$). These findings highlight the prospects of ANN with Residual Blocks for imaging detectors in the PID task of particle physics experiments

Moir\'e Fractional Chern Insulators II: First-principles Calculations and Continuum Models of Rhombohedral Graphene Superlattices

The experimental discovery of fractional Chern insulators (FCIs) in rhombohedral pentalayer graphene twisted on hexagonal boron nitride (hBN) has preceded theoretical prediction. Supported by large-scale first principles relaxation calculations at the experimental twist angle of $0.77^\circ$, we obtain an accurate continuum model of $n=3,4,5,6,7$ layer rhombohedral graphene-hBN moir\'e systems. Focusing on the pentalayer case, we analytically explain the robust $|C|=0,5$ Chern numbers seen in the low-energy single-particle bands and their flattening with displacement field, making use of a minimal two-flavor continuum Hamiltonian derived from the full model. We then predict nonzero valley Chern numbers at the $\nu = -4,0$ insulators observed in experiment. Our analysis makes clear the importance of displacement field and the moir\'e potential in producing localized "heavy fermion" charge density in the top valence band, in addition to the nearly free conduction band. Lastly, we study doubly aligned devices as additional platforms for moir\'e FCIs with higher Chern number bands.Comment: Second paper in the moir\'e FCI serie

Postgraduate ethics training programs: a systematic scoping review

BACKGROUND: Molding competent clinicians capable of applying ethics principles in their practice is a challenging task, compounded by wide variations in the teaching and assessment of ethics in the postgraduate setting. Despite these differences, ethics training programs should recognise that the transition from medical students to healthcare professionals entails a longitudinal process where ethics knowledge, skills and identity continue to build and deepen over time with clinical exposure. A systematic scoping review is proposed to analyse current postgraduate medical ethics training and assessment programs in peer-reviewed literature to guide the development of a local physician training curriculum. METHODS: With a constructivist perspective and relativist lens, this systematic scoping review on postgraduate medical ethics training and assessment will adopt the Systematic Evidence Based Approach (SEBA) to create a transparent and reproducible review. RESULTS: The first search involving the teaching of ethics yielded 7669 abstracts with 573 full text articles evaluated and 66 articles included. The second search involving the assessment of ethics identified 9919 abstracts with 333 full text articles reviewed and 29 articles included. The themes identified from the two searches were the goals and objectives, content, pedagogy, enabling and limiting factors of teaching ethics and assessment modalities used. Despite inherent disparities in ethics training programs, they provide a platform for learners to apply knowledge, translating it to skill and eventually becoming part of the identity of the learner. Illustrating the longitudinal nature of ethics training, the spiral curriculum seamlessly integrates and fortifies prevailing ethical knowledge acquired in medical school with the layering of new specialty, clinical and research specific content in professional practice. Various assessment methods are employed with special mention of portfolios as a longitudinal assessment modality that showcase the impact of ethics training on the development of professional identity formation (PIF). CONCLUSIONS: Our systematic scoping review has elicited key learning points in the teaching and assessment of ethics in the postgraduate setting. However, more research needs to be done on establishing Entrustable Professional Activities (EPA)s in ethics, with further exploration of the use of portfolios and key factors influencing its design, implementation and assessment of PIF and micro-credentialling in ethics practice. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12909-021-02644-5

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Leakage-based precoding for MU-MIMO VLC systems under optical power constraint

In this paper, we investigate a multiuser multiple-input multiple-output (MU-MIMO) system for indoor visible light communication (VLC), in which precoding is conducted under optical power constraint rather than electrical power constraint. Leakage-based precoding designed by maximizing signal-to-leakage-plus-noise ratio (SLNR) is adopted to suppress the multiuser interference under optical power constraint and power allocation is proposed to maximize the throughput of the system. Simulations demonstrate the performance gain of optimal power allocation and indicate that the leakage-based precoding scheme outperforms zero forcing counterpart when the channel is highly correlated and still works well when the number of transmitters is less than that of receivers

Bridging the Semantic Latent Space between Brain and Machine: Similarity Is All You Need

How our brain encodes complex concepts has been a longstanding mystery in neuroscience. The answer to this problem can lead to new understandings about how the brain retrieves information in large-scale data with high efficiency and robustness. Neuroscience studies suggest the brain represents concepts in a locality-sensitive hashing (LSH) strategy, i.e., similar concepts will be represented by similar responses. This finding has inspired the design of similarity-based algorithms, especially in contrastive learning. Here, we hypothesize that the brain and large neural network models, both using similarity-based learning rules, could contain a similar semantic embedding space. To verify that, this paper proposes a functional Magnetic Resonance Imaging (fMRI) semantic learning network named BrainSem, aimed at seeking a joint semantic latent space that bridges the brain and a Contrastive Language-Image Pre-training (CLIP) model. Given that our perception is inherently cross-modal, we introduce a fuzzy (one-to-many) matching loss function to encourage the models to extract high-level semantic components from neural signals. Our results claimed that using only a small set of fMRI recordings for semantic space alignment, we could obtain shared embedding valid for unseen categories out of the training set, which provided potential evidence for the semantic representation similarity between the brain and large neural networks. In a zero-shot classification task, our BrainSem achieves an 11.6% improvement over the state-of-the-art

Method of Calculating the Compensation for Rectifying the Horizontal Displacement of Existing Tunnels by Grouting

Sleeve valve pipe grouting, an effective method for reinforcing soil layers, is often employed to correct the deformation of subway tunnels. In order to study the effect of grouting on rectifying the displacement of existing tunnels, this paper proposes a mechanical model of the volume expansion of sleeve valve pipe grouting taking into consideration the volume expansion of the grouted soil mass. A formula for the additional stress on the soil layer caused by grouting was derived based on the principle of the mirror method. In addition, a formula for the horizontal displacement of a tunnel caused by grouting was developed through a calculation model of shearing dislocation and rigid body rotation. The results of the calculation method proposed herein were in good agreement with actual engineering data. In summary, enlarging the grouting volume within a reasonable range can effectively enhance the grouting corrective effect. Further, with an increase in the grouting distance, the influence of grouting gradually lessens. At a constant grouting length, setting the bottom of the grouting section at the same depth as the lower end of the tunnel can maximize the grouting corrective effect