Miracle-dz/UniFAR-FTUs huggingface dataset:
DTU-Net: Learning Topological Similarity for Curvilinear Structure Segmentation

Curvilinear structure segmentation is important in medical imaging, quantifying structures such as vessels, airways, neurons, or organ boundaries in 2D slices. Segmentation via pixel-wise classification often fails to capture the small and low-contrast curvilinear structures. Prior topological information is typically used to address this problem, often at an expensive computational cost, and sometimes requiring prior knowledge of the expected topology. We present DTU-Net, a data-driven approach to topology-preserving curvilinear structure segmentation. DTU-Net consists of two sequential, lightweight U-Nets, dedicated to texture and topology, respectively. While the texture net makes a coarse prediction using image texture information, the topology net learns topological information from the coarse prediction by employing a triplet loss trained to recognize false and missed splits in the structure. We conduct experiments on a challenging multi-class ultrasound scan segmentation dataset as well as a well-known retinal imaging dataset. Results show that our model outperforms existing approaches in both pixel-wise segmentation accuracy and topological continuity, with no need for prior topological knowledge.
Memory-enriched computation and learning in spiking neural networks through Hebbian plasticity

Memory is a key component of biological neural systems that enables the retention of information over a huge range of temporal scales, ranging from hundreds of milliseconds up to years. While Hebbian plasticity is believed to play a pivotal role in biological memory, it has so far been analyzed mostly in the context of pattern completion and unsupervised learning. Here, we propose that Hebbian plasticity is fundamental for computations in biological neural systems. We introduce a novel spiking neural network architecture that is enriched by Hebbian synaptic plasticity. We show that Hebbian enrichment renders spiking neural networks surprisingly versatile in terms of their computational as well as learning capabilities. It improves their abilities for out-of-distribution generalization, one-shot learning, cross-modal generative association, language processing, and reward-based learning. As spiking neural networks are the basis for energy-efﬁcient neuromorphic hardware, this also suggests that powerful cognitive neuromorphic systems can be build based on this principle.
FLEX: Feature-Logic Embedding Framework for CompleX Knowledge Graph Reasoning

Current best performing models for knowledge graph reasoning (KGR) introduce geometry objects or probabilistic distributions to embed entities and first-order logical (FOL) queries into low-dimensional vector spaces. They can be summarized as a center-size framework (point/box/cone, Beta/Gaussian distribution, etc.). However, they have limited logical reasoning ability. And it is difficult to generalize to various features, because the center and size are one-to-one constrained, unable to have multiple centers or sizes. To address these challenges, we instead propose a novel KGR framework named Feature-Logic Embedding framework, FLEX, which is the first KGR framework that can not only TRULY handle all FOL operations including conjunction, disjunction, negation and so on, but also support various feature spaces. Specifically, the logic part of feature-logic framework is based on vector logic, which naturally models all FOL operations. Experiments demonstrate that FLEX significantly outperforms existing state-of-the-art methods on benchmark datasets.
Novel Adaptive Event-Triggered Fuzzy Command Filter Control for Slowly Switched Nonlinear Systems With Constraints

This article addresses the adaptive fuzzy control problem for switched nonlinear systems with state constraints. The unified barrier function (UBF) is introduced to solve the time-varying state constraints, which removes the feasibility conditions. By integrating command filter into backstepping control to avoid the “explosion of complexity. ” In addition, a novel event-triggered strategy is designed to deal with the asynchronous switching between subsystems and controllers without limiting the maximum asynchronous time, and mitigate the communication burden. Also, a new threshold function is introduced to overcome the difficulty of discontinuous triggering error at the switching instants. Then, by combining the improved admissible edge-dependent average dwell-time (AED-ADT) method with Lyapunov stability analysis, it is proved that all system signals are bounded and do not violate the predefined constraints under given switching rule. Finally, the numerical simulation results verify the superiority of the proposed algorithm, and the algorithm is applied to a ship maneuvering system.
LiGCN: Label-interpretable Graph Convolutional Networks for Multi-label Text Classification

Multi-label text classification (MLTC) is an attractive and challenging task in natural language processing (NLP). Compared with single-label text classification, MLTC has a wider range of applications in practice. In this paper, we propose a label-interpretable graph convolutional network model to solve the MLTC problem by modeling tokens and labels as nodes in a heterogeneous graph. In this way, we are able to take into account multiple relationships including token-level relationships. Besides, the model allows better interpretability for predicted labels as the token-label edges are exposed. We evaluate our method on four real-world datasets and it achieves competitive scores against selected baseline methods. Specifically, this model achieves a gain of 0.14 on the F1 score in the small label set MLTC, and 0.07 in the large label set scenario.
Feature Learning and Signal Propagation in Deep Neural Networks

Recent work by Baratin et al. (2021) sheds light on an intriguing pattern that occurs during the training of deep neural networks: some layers align much more with data compared to other layers (where the alignment is defined as the euclidean product of the tangent features matrix and the data labels matrix). The curve of the alignment as a function of layer index (generally) exhibits an ascent-descent pattern where the maximum is reached for some hidden layer. In this work, we provide the first explanation for this phenomenon. We introduce the Equilibrium Hypothesis which connects this alignment pattern to signal propagation in deep neural networks. Our experiments demonstrate an excellent match with the theoretical predictions.
Dropout in Training Neural Networks: Flatness of Solution and Noise Structure

It is important to understand how the popular regularization method dropout helps the neural network training ﬁnd a good generalization solution. In this work, we show that the training with dropout ﬁnds the neural network with a ﬂatter minimum compared with standard gradient descent training. We further ﬁnd that the variance of a noise induced by the dropout is larger at the sharper direction of the loss landscape and the Hessian of the loss landscape at the found minima aligns with the noise covariance matrix by experiments on various datasets, i.e., MNIST, CIFAR-10, CIFAR-100 and Multi30k, and various structures, i.e., fully-connected networks, large residual convolutional networks and transformer. For networks with piece-wise linear activation function and the dropout is only at the last hidden layer, we then theoretically derive the Hessian and the covariance of dropout randomness, where these two quantities are very similar. This similarity may be a key reason accounting for the goodness of dropout.
The effect of stimulant medication on the learning of academic curricula in children with ADHD: A randomized crossover study.

OBJECTIVE Evaluate whether stimulant medication improves acquisition of academic material in children with attention deficit hyperactivity disorder (ADHD) receiving small-group, content-area instruction in a classroom setting. METHOD Participants were 173 children between the ages of 7 and 12 years old (77% male, 86% Hispanic) who met Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria for ADHD and were participating in a therapeutic summer camp. The design was a triple-masked, within-subject, AB/BA crossover trial. Children completed two consecutive phases of daily, 25-min instruction in both (a) subject-area content (science, social studies) and (b) vocabulary. Each phase was a standard instructional unit lasting for 3 weeks. Teachers and aides taught the material to small groups in a summer classroom setting. Each child was randomized to be medicated with daily osmotic-release oral system methylphenidate (OROS-MPH) during either the first or second of the instructional phases, receiving placebo during the other. RESULTS Medication had large, salutary, statistically significant effects on children's academic seatwork productivity and classroom behavior on every single day of the instructional period. However, there was no detectable effect of medication on learning the material taught during instruction: Children learned the same amount of subject-area and vocabulary content whether they were taking OROS-MPH or placebo during the instructional period. CONCLUSIONS Acute effects of OROS-MPH on daily academic seatwork productivity and classroom behavior did not translate into improved learning of new academic material taught via small-group, evidence-based instruction. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
The Interferometric Binary ϵ Cnc in Praesepe: Precise Masses and Age

We observe the brightest member of the Praesepe cluster, ϵ Cnc, to precisely measure the characteristics of the stars in this binary system, en route to a new measurement of the cluster’s age. We present spectroscopic radial velocity measurements and interferometric observations of the sky-projected orbit to derive the masses, which we find to be M 1/M ⊙ = 2.420 ± 0.008 and M 2/M ⊙ = 2.226 ± 0.004. We place limits on the color–magnitude positions of the stars by using spectroscopic and interferometric luminosity ratios while trying to reproduce the spectral energy distribution of ϵ Cnc. We reexamine the cluster membership of stars at the bright end of the color–magnitude diagram using Gaia data and literature radial velocity information. The binary star data are consistent with an age of 637 ± 19 Myr, as determined from MIST model isochrones. The masses and luminosities of the stars appear to select models with the most commonly used amount of convective core overshooting.
The experience of an indipendent center with the MINIHIP® femoral stem

The implantation of short femoral stems has significantly increased over the past decade, thanks to their preservation of bone stock, allowing for easier potential revision of components and physiological joint reconstruction. Their main features are metaphyseal fixation and partial retention of the femoral neck which lead to biome-chanical advantages and high stability. They also guarantee the preservation of bone stock and insertion through minimally invasive approaches. Fifty-one non-consecutive patients with osteoarthritis or avascular necrosis were treated by two senior surgeons with total hip arthroplasty (THA) with anterior or anterolateral approach between April 2013 and October 2016. Cementless short femoral stem monobloc (Minihip, Corin, Cirencester, UK) was implanted in all patients who were studied retrospectively. Radiographic outcome was analyzed and clinical outcomes were assessed with Harris Hip Score (HHS), Hip handicap and Osteaorthritis Outcome Score (HOOS) and Oxford Hip Score (OHS). Based on radiological results we did not find periprosthetic osteolysis while bone resorption was evaluated in 5 implants which were classified according to Gruen. The MiniHip stem demonstrates adequate metaphyseal grip, excellent implant stability to ensure implant survival. (www.actabiomedica.it)
CIRCLE: continual repair across programming languages

Automatic Program Repair (APR) aims at fixing buggy source code with less manual debugging efforts, which plays a vital role in improving software reliability and development productivity. Recent APR works have achieved remarkable progress via applying deep learning (DL), particularly neural machine translation (NMT) techniques. However, we observe that existing DL-based APR models suffer from at least two severe drawbacks: (1) Most of them can only generate patches for a single programming language, as a result, to repair multiple languages, we have to build and train many repairing models. (2) Most of them are developed offline. Therefore, they won’t function when there are new-coming requirements. To address the above problems, a T5-based APR framework equipped with continual learning ability across multiple programming languages is proposed, namely ContInual Repair aCross Programming LanguagEs (CIRCLE). Specifically, (1) CIRCLE utilizes a prompting function to narrow the gap between natural language processing (NLP) pre-trained tasks and APR. (2) CIRCLE adopts a difficulty-based rehearsal strategy to achieve lifelong learning for APR without access to the full historical data. (3) An elastic regularization method is employed to strengthen CIRCLE’s continual learning ability further, preventing it from catastrophic forgetting. (4) CIRCLE applies a simple but effective re-repairing method to revise generated errors caused by crossing multiple programming languages. We train CIRCLE for four languages (i.e., C, JAVA, JavaScript, and Python) and evaluate it on five commonly used benchmarks. The experimental results demonstrate that CIRCLE not only effectively and efficiently repairs multiple programming languages in continual learning settings, but also achieves state-of-the-art performance (e.g., fixes 64 Defects4J bugs) with a single repair model.
KOLD: Korean Offensive Language Dataset

Recent directions for offensive language detection are hierarchical modeling, identifying the type and the target of offensive language, and interpretability with offensive span annotation and prediction. These improvements are focused on English and do not transfer well to other languages because of cultural and linguistic differences. In this paper, we present the Korean Offensive Language Dataset (KOLD) comprising 40,429 comments, which are annotated hierarchically with the type and the target of offensive language, accompanied by annotations of the corresponding text spans. We collect the comments from NAVER news and YouTube platform and provide the titles of the articles and videos as the context information for the annotation process. We use these annotated comments as training data for Korean BERT and RoBERTa models and find that they are effective at offensiveness detection, target classification, and target span detection while having room for improvement for target group classification and offensive span detection. We discover that the target group distribution differs drastically from the existing English datasets, and observe that providing the context information improves the model performance in offensiveness detection (+0.3), target classification (+1.5), and target group classification (+13.1). We publicly release the dataset and baseline models.
DagSim: Combining DAG-based model structure with unconstrained data types and relations for flexible, transparent, and modularized data simulation

Data simulation is fundamental for machine learning and causal inference, as it allows exploration of scenarios and assessment of methods in settings with full control of ground truth. Directed acyclic graphs (DAGs) are well established for encoding the dependence structure over a collection of variables in both inference and simulation settings. However, while modern machine learning is applied to data of an increasingly complex nature, DAG-based simulation frameworks are still confined to settings with relatively simple variable types and functional forms. We here present DagSim, a Python-based framework for DAG-based data simulation without any constraints on variable types or functional relations. A succinct YAML format for defining the simulation model structure promotes transparency, while separate user-provided functions for generating each variable based on its parents ensure simulation code modularization. We illustrate the capabilities of DagSim through use cases where metadata variables control shapes in an image and patterns in bio-sequences. DagSim is available as a Python package at PyPI. Source code and documentation are available at: https://github.com/uio-bmi/dagsim
Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey

Multi-object tracking (MOT) aims to associate target objects across video frames in order to obtain entire moving trajectories. With the advancement of deep neural networks and the increasing demand for intelligent video analysis, MOT has gained significantly increased interest in the computer vision community. Embedding methods play an essential role in object location estimation and temporal identity association in MOT. Unlike other computer vision tasks, such as image classification, object detection, re-identification, and segmentation, embedding methods in MOT have large variations, and they have never been systematically analyzed and summarized. In this survey, we first conduct a comprehensive overview with in-depth analysis for embedding methods in MOT from seven different perspectives, including patch-level embedding, single-frame embedding, cross-frame joint embedding, correlation embedding, sequential embedding, tracklet embedding, and cross-track relational embedding. We further summarize the existing widely used MOT datasets and analyze the advantages of existing state-of-the-art methods according to their embedding strategies. Finally, some critical yet under-investigated areas and future research directions are discussed.
Deeper vs Wider: A Revisit of Transformer Configuration

Transformer-based models have delivered impressive results on many tasks, partic-ularly vision and language tasks. In many model training situations, conventional conﬁgurations are typically adopted. For example, we often set the base model with hidden dimensions ( i.e., model width) to be 768 and the number of transformer layers ( i.e., model depth) to be 12. In this paper, we revisit these conventional conﬁgurations. Through theoretical analysis and experimental evaluation, we show that the masked autoencoder is effective in alleviating the over-smoothing issue in deep transformer training. Based on this ﬁnding, we propose Bamboo, an idea of using deeper and narrower transformer conﬁgurations, for masked autoencoder training. On ImageNet, with such a simple change in conﬁguration, re-designed model achieves 87.1% top-1 accuracy and outperforms SoTA models like MAE and BEiT. On language tasks, re-designed model outperforms BERT with default setting by 1.1 points on average, on GLUE datasets.
Faculty motivations for leading clinical clerkship electives: A qualitative study

Abstract Purpose Faculty are motivated to pursue clinician-educator careers out of a sense of purpose, duty, connectedness, satisfaction, and mastery. Yet, many suffer from burnout due to a lack of funding, resources, and competing clinical demands. Reasons for clinician-educator participation in unfunded educational leadership positions are underexplored. This study examined faculty members’ reasons for volunteering and remaining as clerkship elective directors, an unfunded leadership position. Methods In this qualitative study, the authors conducted 17 semi-structured interviews with clerkship elective directors in March 2021. The authors conducted a thematic analysis of deidentified transcripts using motivation theories as a lens. Results Directors’ motivations to engage in this unfunded educational leadership position stemmed from their existing clinician-educator identity and a sense of purpose and duty. Directors are sustained by the satisfaction derived from witnessing the positive impact they have on learners’ career development and skills building, the impact of learners on the clinical environment, as well as personal benefits in the mastery of educator skills and enhanced visibility as educators. Conclusions Unfunded educational leadership positions can advance clinician-educators’ commitment to learners and alter the learning environment. Strategies for faculty recruitment and retention in unfunded leadership positions include ensuring meaningful contact with learners, as well as opportunities for personal career development through skills building and enhanced visibility through recognition.
Gradient Concealment: Free Lunch for Defending Adversarial Attacks

Recent studies show that the deep neural networks (DNNs) have achieved great success in various tasks. However, even the \emph{state-of-the-art} deep learning based classifiers are extremely vulnerable to adversarial examples, resulting in sharp decay of discrimination accuracy in the presence of enormous unknown attacks. Given the fact that neural networks are widely used in the open world scenario which can be safety-critical situations, mitigating the adversarial effects of deep learning methods has become an urgent need. Generally, conventional DNNs can be attacked with a dramatically high success rate since their gradient is exposed thoroughly in the white-box scenario, making it effortless to ruin a well trained classifier with only imperceptible perturbations in the raw data space. For tackling this problem, we propose a plug-and-play layer that is training-free, termed as \textbf{G}radient \textbf{C}oncealment \textbf{M}odule (GCM), concealing the vulnerable direction of gradient while guaranteeing the classification accuracy during the inference time. GCM reports superior defense results on the ImageNet classification benchmark, improving up to 63.41\% top-1 attack robustness (AR) when faced with adversarial inputs compared to the vanilla DNNs. Moreover, we use GCM in the CVPR 2022 Robust Classification Challenge, currently achieving \textbf{2nd} place in Phase II with only a tiny version of ConvNext. The code will be made available.
Data-aided Active User Detection with a User Activity Extraction Network for Grant-free SCMA Systems

In grant-free sparse code multiple access (GF-SCMA) system, active user detection (AUD) is a major performance bottleneck as it involves complex combinatorial problem, which makes joint design of contention resources for users and AUD at the receiver a crucial but a challenging problem. To this end, we consider an autoencoder (AE)-based joint optimization of both preamble generation networks (PGNs) in the encoder side and data-aided AUD in the decoder side. The core architecture of the proposed AE is a novel user activity extraction network (UAEN) in the decoder that extracts a priori user activity information from the SCMA codeword data for the data-aided AUD. An end-to-end training of the proposed AE enables joint optimization of the contention resources, i.e., preamble sequences, each associated with one of the codebooks, and extraction of user activity information from both preamble and SCMA-based data transmission. Furthermore, we propose a self-supervised pretraining scheme for the UAEN prior to the end-to-end training, ensuring the convergence of the UAEN which lies deep inside the AE network. Simulation results demonstrated that the proposed AUD scheme achieved a gain of 3 to 5dB at a target activity detection error rate of $10^{-3}$ compared to the state-of-the-art DL-based AUD schemes.