Miracle-dz/UniFAR-FTUs huggingface dataset:
A longitudinal seroepidemiology study to evaluate antibody response to SARS-CoV-2 virus infection and vaccination in children in Calgary, Canada from July 2020 to April 2022: Alberta COVID-19 Childhood Cohort (AB3C) Study

Background Measurement of SARS-CoV-2 antibody seropositivity is important to accurately understand exposure to infection and/or vaccination in specific populations. This study aimed to estimate the serologic response to SARS-CoV-2 virus infection and vaccination in children in Calgary, Alberta over a two-year period. Methods Children with or without prior SARS-CoV-2 infections, were enrolled in Calgary, Canada in 2020. Venous blood was sampled 4 times from July 2020 to April 2022 for SARS-CoV-2 nucleocapsid and spike antibodies. Demographic and clinical information was obtained including SARS-CoV-2 testing results and vaccination records. Results 1035 children were enrolled and 88.9% completed all 4 visits; median age 9 years (IQR: 5,13); 519 (50.1%) female; and 815 (78.7%) Caucasian. Before enrolment, 118 (11.4%) had confirmed or probable SARS-CoV-2. By April 2022, 39.5% of previously uninfected participants had a SARS-CoV-2 infection. Nucleocapsid antibody seropositivity declined to 16.4% of all infected children after more than 200 days post diagnosis. Spike antibodies remained elevated in 93.6% of unvaccinated infected children after more than 200 days post diagnosis. By April 2022, 408 (95.6%) children 12 years and older had received 2 or more vaccine doses, and 241 (61.6%) 5 to 11 year-old children had received 2 vaccine doses. At that time, all 685 vaccinated children had spike antibodies, compared with 94/176 (53.4%) of unvaccinated children. Conclusions In our population, after the first peak of Omicron variant infections and introduction of COVID-19 vaccines for children, all vaccinated children, but just over one-half of unvaccinated children, had SARS-CoV-2 spike antibodies indicating infection and/or vaccination, highlighting the benefit of vaccination. It is not yet known whether a high proportion of seropositivity at the present time predicts sustained population-level protection against future SARS-CoV-2 transmission, infection or severe COVID-19 outcomes in children.
Role of HNF4alpha-cMyc interaction in liver regeneration and recovery after acetaminophen-induced acute liver injury

Background and Aims: Overdose of acetaminophen (APAP) is the major cause of acute liver failure in the western world. We report a novel signaling interaction between hepatocyte nuclear factor 4 alpha (HNF4α) cMyc and nuclear factor erythroid 2–related factor 2 (Nrf2) during liver injury and regeneration after APAP overdose. Approach and Results: APAP-induced liver injury and regeneration were studied in male C57BL/6J (WT) mice, hepatocyte-specific HNF4α knockout mice (HNF4α-KO), and HNF4α-cMyc double knockout mice (DKO). C57BL/6J mice treated with 300 mg/kg maintained nuclear HNF4α expression and exhibited liver regeneration, resulting in recovery. However, treatment with 600-mg/kg APAP, where liver regeneration was inhibited and recovery was delayed, showed a rapid decline in HNF4α expression. HNF4α-KO mice developed significantly higher liver injury due to delayed glutathione recovery after APAP overdose. HNF4α-KO mice also exhibited significant induction of cMyc, and the deletion of cMyc in HNF4α-KO mice (DKO mice) reduced the APAP-induced liver injury. The DKO mice had significantly faster glutathione replenishment due to rapid induction in Gclc and Gclm genes. Coimmunoprecipitation and ChIP analyses revealed that HNF4α interacts with Nrf2 and affects its DNA binding. Furthermore, DKO mice showed significantly faster initiation of cell proliferation resulting in rapid liver regeneration and recovery. Conclusions: These data show that HNF4α interacts with Nrf2 and promotes glutathione replenishment aiding in recovery from APAP-induced liver injury, a process inhibited by cMyc. These studies indicate that maintaining the HNF4α function is critical for regeneration and recovery after APAP overdose.
Identification of Ndfip1 as a novel negative regulator for spatial memory formation associated with increased ubiquitination of Beclin 1 and PTEN

Long-term memory formation requires de novo RNA and protein synthesis. By using the differential display-polymerase chain reaction strategy, we have presently identified the Nedd4 family interacting protein 1 (Ndfip1) cDNA fragment that is differentially expressed between the slow learners and the fast learners from the water maze learning task in rats. Further, the fast learners show decreased Ndfip1 mRNA and protein expression levels than the slow learners. Spatial training similarly decreases the Ndfip1 mRNA and protein expression levels. Conversely, the Ndfip1 conditional heterozygous (cHet) mice show enhanced spatial memory performance compared to the Ndfip1flox/WT control mice. Result from co-immunoprecipitation experiment indicates that spatial training decreases the association between Ndfip1 and the E3 ubiquitin ligase Nedd4 (Nedd4-1), and we have shown that both Beclin 1 and PTEN are endogenous ubiquitination targets of Nedd4 in the hippocampus. Further, spatial training decreases endogenous Beclin 1 and PTEN ubiquitination, and increases Beclin 1 and PTEN expression in the hippocampus. On the other hand, the Becn1 conditional knockout (cKO) mice and the Pten cKO mice both show impaired spatial learning and memory performance. Moreover, the expression level of Beclin 1 and PTEN is higher in the Ndfip1 cHet mice compared with the Ndfip1flox/WT control mice. Here, we have identified Ndfip1 as a candidate novel negative regulation for spatial memory formation and this is associated with increased ubiquitination of Beclin 1 and PTEN in the hippocampus.
Factors associated with time to first dialysis-associated peritonitis episode: Data from the Peritonitis Prevention Study (PEPS)

Introduction: Peritonitis remains a potentially serious complication of peritoneal dialysis (PD) treatment. It is therefore important to identify risk factors in order to reduce the incidence of peritonitis. The aim of the present analysis was to identify factors associated with time to first peritonitis episode. Methods: Incident PD patients from 57 centres in Europe participated in the prospective randomised controlled Peritonitis Prevention Study (PEPS) from 2010 to 2015. Peritonitis-free, self-care PD patients ≥18 years were randomised to a retraining or a control group and followed for 1–36 months after PD initiation. The association of biochemical, clinical and prescription data with time to first peritonitis episode was studied. Results: A first peritonitis episode was experienced by 33% (223/671) of participants. Univariable Cox proportional hazard regression showed a strong association between the time-updated number of PD bags connected per 24 h (PD bags/24 h) and time to first peritonitis episode (HR 1.35; 95% confidence interval (CI) 1.17–1.57), even after inclusion of PD modalities in the same model. Multivariable Cox regression revealed that the factors independently associated with time to first peritonitis episode included age (HR 1.16 per 10 years; 95% CI 1.05–1.28), PD bags/24 h (HR 1.32; 95% CI 1.13–1.54), serum albumin <35 versus >35 g/L (HR 1.39; 95% CI 1.06–1.82) and body weight per 10 kg (HR 1.10; 95% CI 1.01–1.19). Conclusion: This study of incident PD patients indicates that older age, greater number of PD bags connected/24 h, higher body weight and hypoalbuminaemia are independently associated with a shorter time to first peritonitis episode.
LANDSLIDES: FORCE BALANCE AND SOIL WATER

Unfortunately the research activities in soil mechanics.diverted the attention of many investigators and teachers from the manifold limitations imposed by nature on the application of mathematics to problems in earthwork engineering. As a consequence, more and more emphasis has been placed on refinements in sampling and testing and on those very few problems that can be solved with accuracy. Yet, accurate solutions can be obtained only if the soil strata are practically homogeneous and continuous in horizontal directions. Furthermore, since the investigations leading to accurate solutions involve highly specialized methods of sampling and testing, they are justified only in exceptional cases. On the overwhelming majority of jobs no more than an approximate forecast is needed, and if such a forecast cannot be made by simple means it cannot be made at all. If it is not possible to make an approximate forecast, the behavior of the soil must be observed during construction, and the design may subsequently have to be modified in accordance with the findings. These facts cannot be ignored without defying the purpose of soil mechanics. (Terzaghi , 1996) [note: 1st edition was 1948]
A Mixing-Accelerated Primal-Dual Proximal Algorithm for Distributed Nonconvex Optimization

In this paper, we develop a distributed mixing-accelerated primal-dual proximal algorithm, referred to as MAP-Pro, which enables nodes in multi-agent networks to cooperatively minimize the sum of their nonconvex, smooth local cost functions in a decentralized fashion. The proposed algorithm is constructed upon minimizing a computationally inexpensive augmented-Lagrangian-like function and incorporating a time-varying mixing polynomial to expedite information fusion across the network. The convergence results derived for MAP-Pro include a sublinear rate of convergence to a stationary solution and, under the Polyak-{\L}ojasiewics (P-{\L}) condition, a linear rate of convergence to the global optimal solution. Additionally, we may embed the well-noted Chebyshev acceleration scheme in MAP-Pro, which generates a specific sequence of mixing polynomials with given degrees and enhances the convergence performance based on MAP-Pro. Finally, we illustrate the competitive convergence speed and communication efficiency of MAP-Pro via a numerical example.
UNICORN: A Unified Backdoor Trigger Inversion Framework

The backdoor attack, where the adversary uses inputs stamped with triggers (e.g., a patch) to activate pre-planted malicious behaviors, is a severe threat to Deep Neural Network (DNN) models. Trigger inversion is an effective way of identifying backdoor models and understanding embedded adversarial behaviors. A challenge of trigger inversion is that there are many ways of constructing the trigger. Existing methods cannot generalize to various types of triggers by making certain assumptions or attack-specific constraints. The fundamental reason is that existing work does not consider the trigger's design space in their formulation of the inversion problem. This work formally defines and analyzes the triggers injected in different spaces and the inversion problem. Then, it proposes a unified framework to invert backdoor triggers based on the formalization of triggers and the identified inner behaviors of backdoor models from our analysis. Our prototype UNICORN is general and effective in inverting backdoor triggers in DNNs. The code can be found at https://github.com/RU-System-Software-and-Security/UNICORN.
The Lattice of C*-covers of an operator algebra

In this paper it is shown that the lattice of C$^*$-covers of an operator algebra does not contain enough information to distinguish operator algebras up to completely isometric isomorphism. In addition, four natural equivalences of the lattice of C$^*$-covers are developed and proven to be distinct. The lattice of C$^*$-covers of direct sums and tensor products are studied. Along the way key examples are found of an operator algebra that generates exactly $n$ C$^*$-algebras up to $*$-isomorphism and a simple operator algebra that is not similar to a C$^*$-algebra.
Unraveling Instance Associations: A Closer Look for Audio-Visual Segmentation

Audio-visual segmentation (AVS) is a challenging task that involves accurately segmenting sounding objects based on audio-visual cues. The effectiveness of audio-visual learning critically depends on achieving accurate cross-modal alignment between sound and visual objects. Successful audio-visual learning requires two essential components: 1) a challenging dataset with high-quality pixel-level multi-class annotated images associated with audio files, and 2) a model that can establish strong links between audio information and its corresponding visual object. However, these requirements are only partially addressed by current methods, with training sets containing biased audio-visual data, and models that generalise poorly beyond this biased training set. In this work, we propose a new cost-effective strategy to build challenging and relatively unbiased high-quality audio-visual segmentation benchmarks. We also propose a new informative sample mining method for audio-visual supervised contrastive learning to leverage discriminative contrastive samples to enforce cross-modal understanding. We show empirical results that demonstrate the effectiveness of our benchmark. Furthermore, experiments conducted on existing AVS datasets and on our new benchmark show that our method achieves state-of-the-art (SOTA) segmentation accuracy.
PopulAtion Parameter Averaging (PAPA)

Ensemble methods combine the predictions of multiple models to improve performance, but they require significantly higher computation costs at inference time. To avoid these costs, multiple neural networks can be combined into one by averaging their weights. However, this usually performs significantly worse than ensembling. Weight averaging is only beneficial when different enough to benefit from combining them, but similar enough to average well. Based on this idea, we propose PopulAtion Parameter Averaging (PAPA): a method that combines the generality of ensembling with the efficiency of weight averaging. PAPA leverages a population of diverse models (trained on different data orders, augmentations, and regularizations) while slowly pushing the weights of the networks toward the population average of the weights. We also propose PAPA variants (PAPA-all, and PAPA-2) that average weights rarely rather than continuously; all methods increase generalization, but PAPA tends to perform best. PAPA reduces the performance gap between averaging and ensembling, increasing the average accuracy of a population of models by up to 0.8% on CIFAR-10, 1.9% on CIFAR-100, and 1.6% on ImageNet when compared to training independent (non-averaged) models.
Photometry of Outer Solar System Objects from the Dark Energy Survey. I. Photometric Methods, Light-curve Distributions, and Trans-Neptunian Binaries

We report the methods of and initial scientific inferences from the extraction of precision photometric information for the >800 trans-Neptunian objects (TNOs) discovered in the images of the Dark Energy Survey (DES). Scene-modeling photometry is used to obtain shot-noise-limited flux measures for each exposure of each TNO, with background sources subtracted. Comparison of double-source fits to the pixel data with single-source fits are used to identify and characterize two binary TNO systems. A Markov Chain Monte Carlo method samples the joint likelihood of the intrinsic colors of each source as well as the amplitude of its flux variation, given the time series of multiband flux measurements and their uncertainties. A catalog of these colors and light-curve amplitudes A is included with this publication. We show how to assign a likelihood to the distribution q(A) of light-curve amplitudes in any subpopulation. Using this method, we find decisive evidence (i.e., evidence ratio <0.01) that cold classical (CC) TNOs with absolute magnitude 6 < H r < 8.2 are more variable than the hot classical (HC) population of the same H r , reinforcing theories that the former form in situ and the latter arise from a different physical population. Resonant and scattering TNOs in this H r range have variability consistent with either the HCs or CCs. DES TNOs with H r < 6 are seen to be decisively less variable than higher-H r members of any dynamical group, as expected. More surprising is that detached TNOs are decisively less variable than scattering TNOs, which requires them to have distinct source regions or some subsequent differential processing.
Multipartite Entanglement: A Journey through Geometry

Genuine multipartite entanglement is crucial for quantum information and related technologies, but quantifying it has been a long-standing challenge. Most proposed measures do not meet the “genuine” requirement, making them unsuitable for many applications. In this work, we propose a journey toward addressing this issue by introducing an unexpected relation between multipartite entanglement and hypervolume of geometric simplices, leading to a tetrahedron measure of quadripartite entanglement. By comparing the entanglement ranking of two highly entangled four-qubit states, we show that the tetrahedron measure relies on the degree of permutation invariance among parties within the quantum system. We demonstrate potential future applications of our measure in the context of quantum information scrambling within many-body systems.
Spectral Gap Regularization of Neural Networks

We introduce Fiedler regularization, a novel approach for regularizing neural networks that utilizes spectral/graphical information. Existing regularization methods often focus on penalizing weights in a global/uniform manner that ignores the connectivity structure of the neural network. We propose to use the Fiedler value of the neural network's underlying graph as a tool for regularization. We provide theoretical motivation for this approach via spectral graph theory. We demonstrate several useful properties of the Fiedler value that make it useful as a regularization tool. We provide an approximate, variational approach for faster computation during training. We provide an alternative formulation of this framework in the form of a structurally weighted $\text{L}_1$ penalty, thus linking our approach to sparsity induction. We provide uniform generalization error bounds for Fiedler regularization via a Rademacher complexity analysis. We performed experiments on datasets that compare Fiedler regularization with classical regularization methods such as dropout and weight decay. Results demonstrate the efficacy of Fiedler regularization. This is a journal extension of the conference paper by Tam and Dunson (2020).
Voxel or Pillar: Exploring Efficient Point Cloud Representation for 3D Object Detection

Efficient representation of point clouds is fundamental for LiDAR-based 3D object detection. While recent grid-based detectors often encode point clouds into either voxels or pillars, the distinctions between these approaches remain underexplored. In this paper, we quantify the differences between the current encoding paradigms and highlight the limited vertical learning within. To tackle these limitations, we propose a hybrid detection framework named Voxel-Pillar Fusion (VPF), which synergistically combines the unique strengths of both voxels and pillars. To be concrete, we first develop a sparse voxel-pillar encoder that encodes point clouds into voxel and pillar features through 3D and 2D sparse convolutions respectively, and then introduce the Sparse Fusion Layer (SFL), facilitating bidirectional interaction between sparse voxel and pillar features. Our computationally efficient, fully sparse method can be seamlessly integrated into both dense and sparse detectors. Leveraging this powerful yet straightforward representation, VPF delivers competitive performance, achieving real-time inference speeds on the nuScenes and Waymo Open Dataset.
When approximate design for fast homomorphic computation provides differential privacy guarantees

While machine learning has become pervasive in as diversified fields as industry, healthcare, social networks, privacy concerns regarding the training data have gained a critical importance. In settings where several parties wish to collaboratively train a common model without jeopardizing their sensitive data, the need for a private training protocol is particularly stringent and implies to protect the data against both the model's end-users and the actors of the training phase. Differential privacy (DP) and cryptographic primitives are complementary popular countermeasures against privacy attacks. Among these cryptographic primitives, fully homomorphic encryption (FHE) offers ciphertext malleability at the cost of time-consuming operations in the homomorphic domain. In this paper, we design SHIELD, a probabilistic approximation algorithm for the argmax operator which is both fast when homomorphically executed and whose inaccuracy is used as a feature to ensure DP guarantees. Even if SHIELD could have other applications, we here focus on one setting and seamlessly integrate it in the SPEED collaborative training framework from"SPEED: Secure, PrivatE, and Efficient Deep learning"(Grivet S\'ebert et al., 2021) to improve its computational efficiency. After thoroughly describing the FHE implementation of our algorithm and its DP analysis, we present experimental results. To the best of our knowledge, it is the first work in which relaxing the accuracy of an homomorphic calculation is constructively usable as a degree of freedom to achieve better FHE performances.
Fast QTMT Partition for VVC Intra Coding Using U-Net Framework

Versatile Video Coding (VVC) has significantly increased encoding efficiency at the expense of numerous complex coding tools, particularly the flexible Quad-Tree plus Multi-type Tree (QTMT) block partition. This paper proposes a deep learning-based algorithm applied in fast QTMT partition for VVC intra coding. Our solution greatly reduces encoding time by early termination of less-likely intra prediction and partitions with negligible BD-BR increase. Firstly, a redesigned U-Net is recommended as the network’s fundamental framework. Next, we design a Quality Parameter (QP) fusion network to regulate the effect of QPs on the partition results. Finally, we adopt a refined post-processing strategy to better balance encoding performance and complexity. Experimental results demonstrate that our solution outperforms the state-of-the-art works with a complexity reduction of 44.74% to 68.76% and a BD-BR increase of 0.60% to 2.33%.
RFAConv: Innovating Spatial Attention and Standard Convolutional Operation

Spatial attention has been widely used to improve the performance of convolutional neural networks. However, it has certain limitations. In this paper, we propose a new perspective on the effectiveness of spatial attention, which is that the spatial attention mechanism essentially solves the problem of convolutional kernel parameter sharing. However, the information contained in the attention map generated by spatial attention is not sufficient for large-size convolutional kernels. Therefore, we propose a novel attention mechanism called Receptive-Field Attention (RFA). Existing spatial attention, such as Convolutional Block Attention Module (CBAM) and Coordinated Attention (CA) focus only on spatial features, which does not fully address the problem of convolutional kernel parameter sharing. In contrast, RFA not only focuses on the receptive-field spatial feature but also provides effective attention weights for large-size convolutional kernels. The Receptive-Field Attention convolutional operation (RFAConv), developed by RFA, represents a new approach to replace the standard convolution operation. It offers nearly negligible increment of computational cost and parameters, while significantly improving network performance. We conducted a series of experiments on ImageNet-1k, COCO, and VOC datasets to demonstrate the superiority of our approach. Of particular importance, we believe that it is time to shift focus from spatial features to receptive-field spatial features for current spatial attention mechanisms. In this way, we can further improve network performance and achieve even better results. The code and pre-trained models for the relevant tasks can be found at https://github.com/Liuchen1997/RFAConv.