Miracle-dz/UniFAR-FTUs huggingface dataset:
Darwin3: A large-scale neuromorphic chip with a Novel ISA and On-Chip Learning

Spiking Neural Networks (SNNs) are gaining increasing attention for their biological plausibility and potential for improved computational efficiency. To match the high spatial-temporal dynamics in SNNs, neuromorphic chips are highly desired to execute SNNs in hardware-based neuron and synapse circuits directly. This paper presents a large-scale neuromorphic chip named Darwin3 with a novel instruction set architecture(ISA), which comprises 10 primary instructions and a few extended instructions. It supports flexible neuron model programming and local learning rule designs. The Darwin3 chip architecture is designed in a mesh of computing nodes with an innovative routing algorithm. We used a compression mechanism to represent synaptic connections, significantly reducing memory usage. The Darwin3 chip supports up to 2.35 million neurons, making it the largest of its kind in neuron scale. The experimental results showed that code density was improved up to 28.3x in Darwin3, and neuron core fan-in and fan-out were improved up to 4096x and 3072x by connection compression compared to the physical memory depth. Our Darwin3 chip also provided memory saving between 6.8X and 200.8X when mapping convolutional spiking neural networks (CSNN) onto the chip, demonstrating state-of-the-art performance in accuracy and latency compared to other neuromorphic chips.
Automatic laminectomy cutting plane planning based on artificial intelligence in robot assisted laminectomy surgery

Objective: This study aims to use artificial intelligence to realize the automatic planning of laminectomy, and verify the method. Methods: We propose a two-stage approach for automatic laminectomy cutting plane planning. The first stage was the identification of key points. 7 key points were manually marked on each CT image. The Spatial Pyramid Upsampling Network (SPU-Net) algorithm developed by us was used to accurately locate the 7 key points. In the second stage, based on the identification of key points, a personalized coordinate system was generated for each vertebra. Finally, the transverse and longitudinal cutting planes of laminectomy were generated under the coordinate system. The overall effect of planning was evaluated. Results: In the first stage, the average localization error of the SPU-Net algorithm for the seven key points was 0.65mm. In the second stage, a total of 320 transverse cutting planes and 640 longitudinal cutting planes were planned by the algorithm. Among them, the number of horizontal plane planning effects of grade A, B, and C were 318(99.38%), 1(0.31%), and 1(0.31%), respectively. The longitudinal planning effects of grade A, B, and C were 622(97.18%), 1(0.16%), and 17(2.66%), respectively. Conclusions: In this study, we propose a method for automatic surgical path planning of laminectomy based on the localization of key points in CT images. The results showed that the method achieved satisfactory results. More studies are needed to confirm the reliability of this approach in the future.
MoD2T: Model-Data-Driven Motion-Static Object Tracking Method

The domain of Multi-Object Tracking (MOT) is of paramount significance within the realm of video analysis. However, both traditional methodologies and deep learning-based approaches within this domain exhibit inherent limitations. Deep learning methods driven exclusively by data exhibit challenges in accurately discerning the motion states of objects, while traditional methods relying on comprehensive mathematical models may suffer from suboptimal tracking precision. To address these challenges, we introduce the Model-Data-Driven Motion-Static Object Tracking Method (MoD2T). We propose a novel architecture that adeptly amalgamates traditional mathematical modeling with deep learning-based MOT frameworks, thereby effectively mitigating the limitations associated with sole reliance on established methodologies or advanced deep learning techniques. MoD2T's fusion of mathematical modeling and deep learning augments the precision of object motion determination, consequently enhancing tracking accuracy. Our empirical experiments robustly substantiate MoD2T's efficacy across a diverse array of scenarios, including UAV aerial surveillance and street-level tracking. To assess MoD2T's proficiency in discerning object motion states, we introduce MVF1 metric. This novel performance metric is designed to measure the accuracy of motion state classification, providing a comprehensive evaluation of MoD2T's performance. Meticulous experiments substantiate the rationale behind MVF1's formulation. To provide a comprehensive assessment of MoD2T's performance, we meticulously annotate diverse datasets and subject MoD2T to rigorous testing. The achieved MVF1 scores, which measure the accuracy of motion state classification, are particularly noteworthy in scenarios marked by minimal or mild camera motion, with values of 0.774 on the KITTI dataset, 0.521 on MOT17, and 0.827 on UAVDT.
Improving the Imaging Performance of Microwave Imaging Systems by Exploiting Virtual Antennas

Starting from the observation that the correlation coefficient defined by the scattered field data tested by two adjacent antennas decreases with the noise, it turns out that the imaging performance can be improved by adding non-redundant scattered field information through more measuring antennas.However, adding more measuring antennas faces practical challenges such as the limited antenna space, high experimental expenses, and a prolonged data collection time. Therefore, the frequency-domain zero-padding (FDZP) interpolation method is proposed to acquire scattered field data on more virtual antennas. To process the data, a linear inversion algorithm based on the modified Born approximation (MBA) and the nonlinear subspace-based optimization method (SOM) are used to image scatterers of moderate and high contrasts, respectively. The effectiveness and the reliability of the proposed approach are then assessed against synthetic data, semi-experimental data from a full-wave simulation software, and experimental data.
Empirical fits to inclusive electron-carbon scattering data obtained by deep-learning methods

Employing the neural network framework, we obtain empirical fits to the electron-scattering cross section for carbon over a broad kinematic region, extending from the quasielastic peak, through resonance excitation, to the onset of deep-inelastic scattering. We consider two different methods of obtaining such model-independent parametrizations and the corresponding uncertainties: based on the NNPDF approach [J. High Energy Phys. 2002, 062], and on the Monte Carlo dropout. In our analysis, the $\chi^2$ function defines the loss function, including point-to-point uncertainties and considering the systematic normalization uncertainties for each independent set of measurements. Our statistical approaches lead to fits of comparable quality and similar uncertainties of the order of $7\%$ and $12\%$ for the first and the second approaches, respectively. To test these models, we compare their predictions to a~test dataset, excluded from the training process, a~dataset lying beyond the covered kinematic region, and theoretical predictions obtained within the spectral function approach. The predictions of both models agree with experimental measurements and the theoretical predictions. However, the first statistical approach shows better interpolation and extrapolation abilities than the one based on the dropout algorithm.
ACAPEX – Ship-Based Ice Nuclei Collections Field Campaign Report

Measurements were sought to evaluate a hypotheses that sea-spray-sourced ice-nucleating particles (INPs) are of biological origin and represent a distinctly different INP population in comparison to long-range-transported desert or urban and regional land-sourced INP, and that the layering of marine within other aerosol layers feeding orographic storms over the mountains of California and the Western United States thereby leads to common and quantifiable scenarios that influence precipitation over the region. Aerosol collections on the National Oceanic and Atmospheric Administration (NOAA) research vessel (RV) Ronald H. Brown, for subsequent processing of INP immersion freezing activation temperature spectra and composition analyses, added a valuable measurement component to the ARM Cloud Aerosol Precipitation Experiment (ACAPEX) and related CalWater2 (NOAA) studies for use in parameterizing and modeling the impacts of marine boundary layer and other aerosols on climate and radiation via aerosol indirect effects on mixed-phase clouds. Twenty-five nominally 24-hour collections were made and have been processed for immersion freezing INP number concentrations versus temperature in the mixed-phase cloud temperature regime from -10 to -27°C. The similarity of INP number concentrations compared to typical marine boundary layer values attributed to sea-spray aerosols was noted. Nevertheless, variability of INP concentrations of up to 50 timesmore » was noted at individual temperatures over the course of the study. A particular analysis possible with this data set is to examine INP budgets over oceans inside versus outside of atmospheric river conditions. These INP measurements supplemented multiple airborne INP measurements on the ARM Aerial Facility (AAF), and others on the ground during ACAPEX and CalWater2, to provide extensive spatial and temporal analyses of INP immersion freezing spectra during winter storm periods. Future analyses will use thermal sensitivity to examine INP compositions as organic versus inorganic in these marine boundary layer samples. Data set integration is occurring under funding from an Atmospheric System Research (ASR) proposal. « less
LARP: Language-Agent Role Play for Open-World Games

Language agents have shown impressive problem-solving skills within defined settings and brief timelines. Yet, with the ever-evolving complexities of open-world simulations, there's a pressing need for agents that can flexibly adapt to complex environments and consistently maintain a long-term memory to ensure coherent actions. To bridge the gap between language agents and open-world games, we introduce Language Agent for Role-Playing (LARP), which includes a cognitive architecture that encompasses memory processing and a decision-making assistant, an environment interaction module with a feedback-driven learnable action space, and a postprocessing method that promotes the alignment of various personalities. The LARP framework refines interactions between users and agents, predefined with unique backgrounds and personalities, ultimately enhancing the gaming experience in open-world contexts. Furthermore, it highlights the diverse uses of language models in a range of areas such as entertainment, education, and various simulation scenarios. The project page is released at https://miao-ai-lab.github. io/LARP/.
Conversational Question Answering with Reformulations over Knowledge Graph

Conversational question answering (convQA) over knowledge graphs (KGs) involves answering multi-turn natural language questions about information contained in a KG. State-of-the-art methods of ConvQA often struggle with inexplicit question-answer pairs. These inputs are easy for human beings to understand given a conversation history, but hard for a machine to interpret, which can degrade ConvQA performance. To address this problem, we propose a reinforcement learning (RL) based model, CornNet, which utilizes question reformulations generated by large language models (LLMs) to improve ConvQA performance. CornNet adopts a teacher-student architecture where a teacher model learns question representations using human writing reformulations, and a student model to mimic the teacher model's output via reformulations generated by LLMs. The learned question representation is then used by an RL model to locate the correct answer in a KG. Extensive experimental results show that CornNet outperforms state-of-the-art convQA models.
Data Augmentation for Supervised Graph Outlier Detection with Latent Diffusion Models

Graph outlier detection is a prominent task of research and application in the realm of graph neural networks. It identifies the outlier nodes that exhibit deviation from the majority in the graph. One of the fundamental challenges confronting supervised graph outlier detection algorithms is the prevalent issue of class imbalance, where the scarcity of outlier instances compared to normal instances often results in suboptimal performance. Conventional methods mitigate the imbalance by reweighting instances in the estimation of the loss function, assigning higher weights to outliers and lower weights to inliers. Nonetheless, these strategies are prone to overfitting and underfitting, respectively. Recently, generative models, especially diffusion models, have demonstrated their efficacy in synthesizing high-fidelity images. Despite their extraordinary generation quality, their potential in data augmentation for supervised graph outlier detection remains largely underexplored. To bridge this gap, we introduce GODM, a novel data augmentation for mitigating class imbalance in supervised Graph Outlier detection with latent Diffusion Models. Specifically, our proposed method consists of three key components: (1) Variantioanl Encoder maps the heterogeneous information inherent within the graph data into a unified latent space. (2) Graph Generator synthesizes graph data that are statistically similar to real outliers from latent space, and (3) Latent Diffusion Model learns the latent space distribution of real organic data by iterative denoising. Extensive experiments conducted on multiple datasets substantiate the effectiveness and efficiency of GODM. The case study further demonstrated the generation quality of our synthetic data. To foster accessibility and reproducibility, we encapsulate GODM into a plug-and-play package and release it at the Python Package Index (PyPI).
Distance Guided Generative Adversarial Network for Explainable Binary Classifications

Despite the potential benefits of data augmentation for mitigating the data insufficiency, traditional augmentation methods primarily rely on the prior intra-domain knowledge. On the other hand, advanced generative adversarial networks (GANs) generate inter-domain samples with limited variety. These previous methods make limited contributions to describing the decision boundaries for binary classification. In this paper, we propose a distance guided GAN (DisGAN) which controls the variation degrees of generated samples in the hyperplane space. Specifically, we instantiate the idea of DisGAN by combining two ways. The first way is vertical distance GAN (VerDisGAN) where the inter-domain generation is conditioned on the vertical distances. The second way is horizontal distance GAN (HorDisGAN) where the intra-domain generation is conditioned on the horizontal distances. Furthermore, VerDisGAN can produce the class-specific regions by mapping the source images to the hyperplane. Experimental results show that DisGAN consistently outperforms the GAN-based augmentation methods with explainable binary classification. The proposed method can apply to different classification architectures and has potential to extend to multi-class classification.
A Comparison of Full Information Maximum Likelihood and Machine Learning Missing Data Analytical Methods in Growth Curve Modeling

Missing data are inevitable in longitudinal studies. Traditional methods, such as the full information maximum likelihood (FIML), are commonly used to handle ignorable missing data. However, they may lead to biased model estimation due to missing not at random data that often appear in longitudinal studies. Recently, machine learning methods, such as random forests (RF) and K-nearest neighbors (KNN) imputation methods, have been proposed to cope with missing values. Although machine learning imputation methods have been gaining popularity, few studies have investigated the tenability and utility of these methods in longitudinal research. Through Monte Carlo simulations, this study evaluates and compares the performance of traditional and machine learning approaches (FIML, RF, and KNN) in growth curve modeling. The effects of sample size, the rate of missingness, and the missing data mechanism on model estimation are investigated. Results indicate that FIML is a better choice than the two machine learning imputation methods in terms of model estimation accuracy and efficiency.
Quantum jumps in driven-dissipative disordered many-body systems

We discuss how quantum jumps affect localized regimes in driven-dissipative disordered many-body systems featuring a localization transition. We introduce a deformation of the Lindblad master equation that interpolates between the standard Lindblad and the no-jump non-Hermitian dynamics of open quantum systems. As a platform, we use a disordered chain of hard-core bosons with nearest-neighbor interactions and subject to coherent drive and dissipation at alternate sites. We probe both the statistics of complex eigenvalues of the deformed Liouvillian and dynamical observables of physical relevance. We show that reducing the number of quantum jumps, achievable through realistic post-selection protocols, can promote the emergence of the localized phase. Our findings are based on exact diagonalization and time-dependent matrix-product states techniques.
QGFace: Quality-Guided Joint Training For Mixed-Quality Face Recognition

The quality of a face crop in an image is decided by many factors such as camera resolution, distance, and illumination condition. This makes the discrimination of face images with different qualities a challenging problem in realistic applications. However, most existing approaches are designed specifically for high-quality (HQ) or low-quality (LQ) images, and the performances would degrade for the mixed-quality images. Besides, many methods ask for pre-trained feature extractors or other auxiliary structures to support the training and the evaluation. In this paper, we point out that the key to better understand both the HQ and the LQ images simultaneously is to apply different learning methods according to their qualities. We propose a novel quality-guided joint training approach for mixed-quality face recognition, which could simultaneously learn the images of different qualities with a single encoder. Based on quality partition, classification-based method is employed for HQ data learning. Meanwhile, for the LQ images which lack identity information, we learn them with self-supervised image-image contrastive learning. To effectively catch up the model update and improve the discriminability of contrastive learning in our joint training scenario, we further propose a proxy-updated real-time queue to compose the contrastive pairs with features from the genuine encoder. Experiments on the low-quality datasets SCface and Tinyface, the mixed-quality dataset IJB-B, and five high-quality datasets demonstrate the effectiveness of our proposed approach in recognizing face images of different qualities.
Online minimum search for Brownian motion and the Cauchy process: Multiple approaches

The distribution for the minimum of Brownian motion or the Cauchy process is well-known using the reflection principle. Here we consider the problem of finding the sample-by-sample minimum, which we call the online minimum search. We consider the possibility of the golden search method, but we show quantitatively that the bisection method is more efficient. In the bisection method there is a hierarchical parameter, which tunes the depth to which each sub-search is conducted, somewhat similarly to how a depth-first search works to generate a topological ordering on nodes. Finally, we consider the possibility of using harmonic measure, which is a novel idea that has so far been unexplored.
Virtual Constraints on Lie groups

This paper studies virtual constraints on Lie groups. Virtual constraints are invariant relations imposed on a control system via feedback. In this work, we introduce the notion of \textit{virtual constraints on Lie groups}, in particular, \textit{virtual nonholonomic constraints on Lie groups}, in a geometric framework. More precisely, this object is a controlled invariant subspace associated with an affine connection mechanical control system on the Lie algebra associated with the Lie group which is the configuration space of the system. We demonstrate the existence and uniqueness of a control law defining a virtual nonholonomic constraint and we characterize the trajectories of the closed-loop system as solutions of a mechanical system associated with an induced constrained connection. Moreover, we characterize the reduced dynamics for nonholonomic systems in terms of virtual nonholonomic constraints, i.e., we characterize when can we obtain reduced nonholonomic dynamics from virtual nonholonomic constraints. We illustrate the theory with some examples and present simulation results for an application.
Phytochemical Screening and Mosquito Repellent Activity of the Stem Bark Extracts of Euphorbia Balsamifera (Ait)

The phytochemical screening of extracts from Euphorbia balsamifera was carried out, and the results revealed that the stem bark contains alkaloids, tannins, flavonoids, saponins, glycosides, terpenoids and sterols. The ethanol extract of the stem bark of Euphorbia balsamifera (commonly known as Ayyara in Hausa) was sequentially partitioned with petroleum ether, chloroform, ethyl acetate and methanol. The extracts were respectively labeled EB1, EB2, EB3, and EB4, with the ethanolic extract labeled EB. The extracts, 12.5% and 25% w/v, were tested for repellency against Anopheles gambiae, and the repellent activity was assessed using the human-bait technique. Five volunteers participated in the laboratory tests to ascertain the repellent activity of the extracts, and each volunteer was test with one extract, with each test repeated in triplicate. The chloroform extract labeled EB1-02 was found to be the most active, (97.2% and 100% repellency), while the extract labeled EB1-03 recorded the least activity of 32.4% and 21.6%. It can be concluded that the chloroform extract labeled EB1-02 is responsible for repellent activity of Euphorbia balsamifera.