Miracle-dz/UniFAR-FTUs huggingface dataset:
DeSCo: Towards Generalizable and Scalable Deep Subgraph Counting

We introduce DeSCo, a scalable neural deep subgraph counting pipeline, designed to accurately predict both the count and occurrence position of queries on target graphs post single training. Firstly, DeSCo uses a novel canonical partition and divides the large target graph into small neighborhood graphs, greatly reducing the count variation while guaranteeing no missing or double-counting. Secondly, neighborhood counting uses an expressive subgraph-based heterogeneous graph neural network to accurately count in each neighborhood. Finally, gossip propagation propagates neighborhood counts with learnable gates to harness the inductive biases of motif counts. DeSCo is evaluated on eight real-world datasets from various domains. It outperforms state-of-the-art neural methods with 137x improvement in the mean squared error of count prediction, while maintaining the polynomial runtime complexity. Our open source project is at https://github.com/fuvty/DeSCo.
Comprehensive Analysis of Network Robustness Evaluation Based on Convolutional Neural Networks with Spatial Pyramid Pooling

Connectivity robustness, a crucial aspect for understanding, optimizing, and repairing complex networks, has traditionally been evaluated through time-consuming and often impractical simulations. Fortunately, machine learning provides a new avenue for addressing this challenge. However, several key issues remain unresolved, including the performance in more general edge removal scenarios, capturing robustness through attack curves instead of directly training for robustness, scalability of predictive tasks, and transferability of predictive capabilities. In this paper, we address these challenges by designing a convolutional neural networks (CNN) model with spatial pyramid pooling networks (SPP-net), adapting existing evaluation metrics, redesigning the attack modes, introducing appropriate filtering rules, and incorporating the value of robustness as training data. The results demonstrate the thoroughness of the proposed CNN framework in addressing the challenges of high computational time across various network types, failure component types and failure scenarios. However, the performance of the proposed CNN model varies: for evaluation tasks that are consistent with the trained network type, the proposed CNN model consistently achieves accurate evaluations of both attack curves and robustness values across all removal scenarios. When the predicted network type differs from the trained network, the CNN model still demonstrates favorable performance in the scenario of random node failure, showcasing its scalability and performance transferability. Nevertheless, the performance falls short of expectations in other removal scenarios. This observed scenario-sensitivity in the evaluation of network features has been overlooked in previous studies and necessitates further attention and optimization. Lastly, we discuss important unresolved questions and further investigation.
View Consistent Purification for Accurate Cross-View Localization

This paper proposes a fine-grained self-localization method for outdoor robotics that utilizes a flexible number of onboard cameras and readily accessible satellite images. The proposed method addresses limitations in existing cross-view localization methods that struggle to handle noise sources such as moving objects and seasonal variations. It is the first sparse visual-only method that enhances perception in dynamic environments by detecting view-consistent key points and their corresponding deep features from ground and satellite views, while removing off-the-ground objects and establishing homography transformation between the two views. Moreover, the proposed method incorporates a spatial embedding approach that leverages camera intrinsic and extrinsic information to reduce the ambiguity of purely visual matching, leading to improved feature matching and overall pose estimation accuracy. The method exhibits strong generalization and is robust to environmental changes, requiring only geo-poses as ground truth. Extensive experiments on the KITTI and Ford Multi-AV Seasonal datasets demonstrate that our proposed method outperforms existing state-of-the-art methods, achieving median spatial accuracy errors below 0.5 meters along the lateral and longitudinal directions, and a median orientation accuracy error below 2°1.
Improving CTC-AED model with integrated-CTC and auxiliary loss regularization

Connectionist temporal classification (CTC) and attention-based encoder decoder (AED) joint training has been widely applied in automatic speech recognition (ASR). Unlike most hybrid models that separately calculate the CTC and AED losses, our proposed integrated-CTC utilizes the attention mechanism of AED to guide the output of CTC. In this paper, we employ two fusion methods, namely direct addition of logits (DAL) and preserving the maximum probability (PMP). We achieve dimensional consistency by adaptively affine transforming the attention results to match the dimensions of CTC. To accelerate model convergence and improve accuracy, we introduce auxiliary loss regularization for accelerated convergence. Experimental results demonstrate that the DAL method performs better in attention rescoring, while the PMP method excels in CTC prefix beam search and greedy search.
Diagnosing Human-object Interaction Detectors

We have witnessed significant progress in human-object interaction (HOI) detection. The reliance on mAP (mean Average Precision) scores as a summary metric, however, does not provide sufficient insight into the nuances of model performance (e.g., why one model is better than another), which can hinder further innovation in this field. To address this issue, in this paper, we introduce a diagnosis toolbox to provide detailed quantitative break-down analysis of HOI detection models, inspired by the success of object detection diagnosis toolboxes. We first conduct holistic investigations in the pipeline of HOI detection. By defining a set of errors and the oracles to fix each of them, we can have a quantitative analysis of the significance of different errors according to the mAP improvement obtained from fixing each error. We then delve into two sub-tasks of HOI detection: human-object pair detection and interaction classification, respectively. For the first detection task, we compute the coverage of ground-truth human-object pairs as well as the noisiness level in the detection results. For the second classification task, we measure a model's performance of differentiating positive and negative detection results and also classifying the actual interactions when the human-object pairs are correctly detected. We analyze eight state-of-the-art HOI detection models and provide valuable diagnosis insights to foster future research. For instance, our diagnosis shows that state-of-the-art model RLIPv2 outperforms others mainly because it significantly improves the multi-label interaction classification accuracy. Our toolbox is applicable for different methods across different datasets and available at https://github.com/neu-vi/Diag-HOI.
HyperSNN: A new efficient and robust deep learning model for resource constrained control applications

In light of the increasing adoption of edge computing in areas such as intelligent furniture, robotics, and smart homes, this paper introduces HyperSNN, an innovative method for control tasks that uses spiking neural networks (SNNs) in combination with hyperdimensional computing. HyperSNN substitutes expensive 32-bit floating point multiplications with 8-bit integer additions, resulting in reduced energy consumption while enhancing robustness and potentially improving accuracy. Our model was tested on AI Gym benchmarks, including Cartpole, Acrobot, MountainCar, and Lunar Lander. HyperSNN achieves control accuracies that are on par with conventional machine learning methods but with only 1.36% to 9.96% of the energy expenditure. Furthermore, our experiments showed increased robustness when using HyperSNN. We believe that HyperSNN is especially suitable for interactive, mobile, and wearable devices, promoting energy-efficient and robust system design. Furthermore, it paves the way for the practical implementation of complex algorithms like model predictive control (MPC) in real-world industrial scenarios.
Denoising Diffusion Probabilistic Model for Retinal Image Generation and Segmentation

Experts use retinal images and vessel trees to detect and diagnose various eye, blood circulation, and brain-related diseases. However, manual segmentation of retinal images is a time-consuming process that requires high expertise and is difficult due to privacy issues. Many methods have been proposed to segment images, but the need for large retinal image datasets limits the performance of these methods. Several methods synthesize deep learning models based on Generative Adversarial Networks (GAN) to generate limited sample varieties. This paper proposes a novel Denoising Diffusion Probabilistic Model (DDPM) that outperformed GANs in image synthesis. We developed a Retinal Trees (ReTree) dataset consisting of retinal images, corresponding vessel trees, and a segmentation network based on DDPM trained with images from the ReTree dataset. In the first stage, we develop a two-stage DDPM that generates vessel trees from random numbers belonging to a standard normal distribution. Later, the model is guided to generate fundus images from given vessel trees and random distribution. The proposed dataset has been evaluated quantitatively and qualitatively. Quantitative evaluation metrics include Frechet Inception Distance (FID) score, Jaccard similarity coefficient, Cohen’s kappa, Matthew’s Correlation Coefficient (MCC), precision, recall, F1-score, and accuracy. We trained the vessel segmentation model with synthetic data to validate our dataset’s efficiency and tested it on authentic data. Our developed dataset and source code is available at https://github.com/AAleka/retree.
TeCH: Text-guided Reconstruction of Lifelike Clothed Humans

Despite recent research advancements in reconstructing clothed humans from a single image, accurately restoring the"unseen regions"with high-level details remains an unsolved challenge that lacks attention. Existing methods often generate overly smooth back-side surfaces with a blurry texture. But how to effectively capture all visual attributes of an individual from a single image, which are sufficient to reconstruct unseen areas (e.g., the back view)? Motivated by the power of foundation models, TeCH reconstructs the 3D human by leveraging 1) descriptive text prompts (e.g., garments, colors, hairstyles) which are automatically generated via a garment parsing model and Visual Question Answering (VQA), 2) a personalized fine-tuned Text-to-Image diffusion model (T2I) which learns the"indescribable"appearance. To represent high-resolution 3D clothed humans at an affordable cost, we propose a hybrid 3D representation based on DMTet, which consists of an explicit body shape grid and an implicit distance field. Guided by the descriptive prompts + personalized T2I diffusion model, the geometry and texture of the 3D humans are optimized through multi-view Score Distillation Sampling (SDS) and reconstruction losses based on the original observation. TeCH produces high-fidelity 3D clothed humans with consistent&delicate texture, and detailed full-body geometry. Quantitative and qualitative experiments demonstrate that TeCH outperforms the state-of-the-art methods in terms of reconstruction accuracy and rendering quality. The code will be publicly available for research purposes at https://huangyangyi.github. io/TeCH
SCQPTH: an efficient differentiable splitting method for convex quadratic programming

We present SCQPTH: a differentiable first-order splitting method for convex quadratic programs. The SCQPTH framework is based on the alternating direction method of multipliers (ADMM) and the software implementation is motivated by the state-of-the art solver OSQP: an operating splitting solver for convex quadratic programs (QPs). The SCQPTH software is made available as an open-source python package and contains many similar features including efficient reuse of matrix factorizations, infeasibility detection, automatic scaling and parameter selection. The forward pass algorithm performs operator splitting in the dimension of the original problem space and is therefore suitable for large scale QPs with $100-1000$ decision variables and thousands of constraints. Backpropagation is performed by implicit differentiation of the ADMM fixed-point mapping. Experiments demonstrate that for large scale QPs, SCQPTH can provide a $1\times - 10\times$ improvement in computational efficiency in comparison to existing differentiable QP solvers.
Einsatz von spezifischen Antidots bei DOAK-assoziierter schwerer gastrointestinaler Blutung – ein Expertenkonsensus

Zusammenfassung Gastrointestinale (GI) Blutungen gehören zu den häufigsten Komplikationen beim Einsatz direkt wirkender oraler Antikoagulanzien (DOAK). Für die Maßnahmen bei (vermuteter) GI-Blutung existieren klare (Notfall-)Algorithmen, bei denen im präendoskopischen Management die anamnestische Erfragung einer medikamentösen Antikoagulation und einfache Gerinnungstests erfolgen sollen. Zur Gerinnungsoptimierung werden beispielsweise Thrombozyten-Konzentrate, Frischplasma (FFP) oder Prothrombinkomplex-Präparate (PPSB) eingesetzt. Für schwere Blutungen unter dem Thrombin-Inhibitor Dabigatran steht Idarucizumab, für Blutungen unter den Faktor Xa-Inhibitoren Rivaroxaban oder Apixaban steht Andexanet alfa als spezifisches Antidot zur DOAK-Antagonisierung zur Verfügung. Diese Antidote stellen Notfallpräparate dar, die typischerweise erst nach Durchführung leitlinienkonformer multimodaler Maßnahmen einschließlich Notfallendoskopie eingesetzt werden. Eine Antagonisierung oraler Antikoagulanzien sollte bei schwerer gastrointestinaler Blutung in folgenden Situationen erfolgen: (1) refraktärer hämorrhagischer Schock, (2) endoskopisch unstillbare Blutungen, oder (3) nicht vermeidbare Verzögerungen bis zur Notfallendoskopie bei lebensbedrohlicher Blutung. Nach erfolgreicher (endoskopischer) Blutstillung sollte die Antikoagulation (z.B. DOAK, Vitamin-K-Antagonist, Heparin) unter Berücksichtigung des individuellen Blutungsrisikos und des Thromboembolierisikos wieder zeitnah (d.h. in der Regel innerhalb einer Woche) fortgeführt werden.
NKG2D-CAR T cells eliminate senescent cells in aged mice and nonhuman primates

Cellular senescence, characterized by stable cell cycle arrest, plays an important role in aging and age-associated pathologies. Eliminating senescent cells rejuvenates aged tissues and ameliorates age-associated diseases. Here, we identified that natural killer group 2 member D ligands (NKG2DLs) are up-regulated in senescent cells in vitro, regardless of stimuli that induced cellular senescence, and in various tissues of aged mice and nonhuman primates in vivo. Accordingly, we developed and demonstrated that chimeric antigen receptor (CAR) T cells targeting human NKG2DLs selectively and effectively diminish human cells undergoing senescence induced by oncogenic stress, replicative stress, DNA damage, or P16INK4a overexpression in vitro. Targeting senescent cells with mouse NKG2D-CAR T cells alleviated multiple aging-associated pathologies and improved physical performance in both irradiated and aged mice. Autologous T cells armed with the human NKG2D CAR effectively delete naturally occurring senescent cells in aged nonhuman primates without any observed adverse effects. Our findings establish that NKG2D-CAR T cells could serve as potent and selective senolytic agents for aging and age-associated diseases driven by senescence. Description NKG2D-CAR T cells selectively eliminate senescent cells in aged animal models. Editor’s summary Cellular senescence contributes to aging and to aging-associated diseases, and depletion of senescent cells has shown potential for treating such diseases. Here, Yang et al. developed chimeric antigen receptor (CAR) T cells targeting Natural Killer Group 2 Member D (NKG2D) ligands, which are highly expressed on senescent cells. The CAR, which is comprised of the extracellular domain of NKG2D and the intracellular signaling domains of 4-1BB and CD3ζ, was able to efficiently drive killing of senescent cells in vitro and in vivo in both aged mice and aged nonhuman primates. Further, aged mice treated with NKG2D-CAR T cells exhibited improved physical performance after treatment. These data support further development of NKG2D-CAR T cells for aging-associated pathologies. —Courtney Malo
Towards Benchmarking Power-Performance Characteristics of Federated Learning Clients

Federated Learning (FL) is a decentralized machine learning approach where local models are trained on distributed clients, allowing privacy-preserving collaboration by sharing model updates instead of raw data. However, the added communication overhead and increased training time caused by heterogenous data distributions results in higher energy consumption and carbon emissions for achieving similar model performance than traditional machine learning. At the same time, efficient usage of available energy is an important requirement for battery constrained devices. Because of this, many different approaches on energy-efficient and carbon-efficient FL scheduling and client selection have been published in recent years. However, most of this research oversimplifies power performance characteristics of clients by assuming that they always require the same amount of energy per processed sample throughout training. This overlooks real-world effects arising from operating devices under different power modes or the side effects of running other workloads in parallel. In this work, we take a first look on the impact of such factors and discuss how better power-performance estimates can improve energy-efficient and carbon-efficient FL scheduling.
Ajna: Generalized deep uncertainty for minimal perception on parsimonious robots

Robots are active agents that operate in dynamic scenarios with noisy sensors. Predictions based on these noisy sensor measurements often lead to errors and can be unreliable. To this end, roboticists have used fusion methods using multiple observations. Lately, neural networks have dominated the accuracy charts for perception-driven predictions for robotic decision-making and often lack uncertainty metrics associated with the predictions. Here, we present a mathematical formulation to obtain the heteroscedastic aleatoric uncertainty of any arbitrary distribution without prior knowledge about the data. The approach has no prior assumptions about the prediction labels and is agnostic to network architecture. Furthermore, our class of networks, Ajna, adds minimal computation and requires only a small change to the loss function while training neural networks to obtain uncertainty of predictions, enabling real-time operation even on resource-constrained robots. In addition, we study the informational cues present in the uncertainties of predicted values and their utility in the unification of common robotics problems. In particular, we present an approach to dodge dynamic obstacles, navigate through a cluttered scene, fly through unknown gaps, and segment an object pile, without computing depth but rather using the uncertainties of optical flow obtained from a monocular camera with onboard sensing and computation. We successfully evaluate and demonstrate the proposed Ajna network on four aforementioned common robotics and computer vision tasks and show comparable results to methods directly using depth. Our work demonstrates a generalized deep uncertainty method and demonstrates its utilization in robotics applications. Description Ajna is a generalized uncertainty method for robotic and computer vision tasks.
Theoretical and experimental studies of large scale modes in Hall thrusters and methods of their control

Partially-magnetized plasmas with magnetized electrons and weakly-magnetized ions in crossed electric and magnetic fields are common in the nature and industrial devices, such as magnetrons, MHD generators, electric propulsion (ion/Hall thrusters, helicon thrusters), and Penning discharges. Such plasmas are usually in a strongly non-equilibrium state, exhibiting a variety of turbulent fluctuations. These fluctuations are responsible for anomalous transport phenomena and large-scale plasma structures. This study investigates one of the most known and pervasive examples: rotating spokes and breathing modes in Hall thrusters. Breathing mode is an m = 0 mode (here m is the azimuthal mode number), propagating in the axial direction and leading to strong oscillations of the discharge current, up to 100 % in amplitude. Spoke is an m = 1, 2, . mode rotating in an azimuthal direction and is thought to be responsible for a large part of the anomalous current in a system. These large-scale perturbations can critically affect the performance of Hall thrusters, but the exact nature of these modes is unknown. Both types of oscillations are believed to be induced and strongly affected by the equilibrium electron current due to E×B drift, plasma density gradients, and ionization. This thesis is devoted to theoretical and experimental studies of spoke and breathing mode mechanisms, their coupling, and methods of their control. We have performed the theoretical study of the gradient-drift (collisionless Simon-Hoh) instability. This is a robust instability considered to be important for Hall plasma discharges supported by the electron current due to the E×B drift. Most of the previous studies of this mode were based on the local approximation. Here, we consider the nonlocal model which takes into account the electron inertia as well as the effects of the plasma profiles, such as the electric, magnetic fields and plasma density. Contrary to local models, nonlocal analysis predicts multiple unstable modes, existing in the regions where local instability criteria is not satisfied. This is especially pronounced for the long wavelength modes which are believed to lead to spokes.
Cross-talk quantification in molecular networks with application to pathway-pathway and cell-cell interactions

Motivations Disease phenotypes are interpreted as the consequence of interactions among molecular processes affected by a series of molecular alterations. Current omics approaches underline the importance of such interactions, focusing on molecular mechanisms, by using manually curated pathways or gene sets. As there are no clear boundaries between gene sets, cross-talks between them are relevant to investigate disease phenotypes. Here we present Ulisse, a method to (1) quantify cross-talks between gene sets, with application to pathways and intercellular cross-talks; (2) investigate the role of the genes involved in cross-talks, via functional relevance analysis, in terms of regulated processes/cell types. As a proof-of-concept, we studied pathway and cell-cell cross-talks in normal and tumoral breast samples, and compared the obtained results with other available tools: PathNet for pathway cross-talk, ICELLNET and SingleCellSignalR for intercellular cross-talk. Results Cross-talk analysis allowed to identify dysregulated interactions between tumor progression key mechanisms, while intercellular cross-talks describe well-known interactions of the tumor with the microenvironment. Through functional relevance analysis, Ulisse was able to pinpoint well known key players of the tumor, as well as potential therapeutic targets. The comparison of Ulisse to other packages highlighted the good performance of our package. In conclusion, Ulisse proved to be a valuable tool to study cross-talks, and provide a newly implemented approach to analyse involved genes. Availability and implementation Ulisse is an R package and available at https://github.com/emosca-cnr/Ulisse Contact ettore.mosca@itb.cnr. it Supplementary information Link to supplementary tables + reference to on-line supplementary material