Miracle-dz/UniFAR-FTUs huggingface dataset:
PSUMNet: Unified Modality Part Streams are All You Need for Efficient Pose-based Action Recognition

Pose-based action recognition is predominantly tackled by approaches which treat the input skeleton in a monolithic fashion, i.e. joints in the pose tree are processed as a whole. However, such approaches ignore the fact that action categories are often characterized by localized action dynamics involving only small subsets of part joint groups involving hands (e.g. `Thumbs up') or legs (e.g. `Kicking'). Although part-grouping based approaches exist, each part group is not considered within the global pose frame, causing such methods to fall short. Further, conventional approaches employ independent modality streams (e.g. joint, bone, joint velocity, bone velocity) and train their network multiple times on these streams, which massively increases the number of training parameters. To address these issues, we introduce PSUMNet, a novel approach for scalable and efficient pose-based action recognition. At the representation level, we propose a global frame based part stream approach as opposed to conventional modality based streams. Within each part stream, the associated data from multiple modalities is unified and consumed by the processing pipeline. Experimentally, PSUMNet achieves state of the art performance on the widely used NTURGB+D 60/120 dataset and dense joint skeleton dataset NTU 60-X/120-X. PSUMNet is highly efficient and outperforms competing methods which use 100%-400% more parameters. PSUMNet also generalizes to the SHREC hand gesture dataset with competitive performance. Overall, PSUMNet's scalability, performance and efficiency makes it an attractive choice for action recognition and for deployment on compute-restricted embedded and edge devices. Code and pretrained models can be accessed at https://github.com/skelemoa/psumnet
The RFOT Theory of Glasses: Recent Progress and Open Issues

The Random First Order Transition (RFOT) theory started with the pioneering work of Kirkpatrick, Thirumalai and Wolynes. It leverages the methods and advances of the theory of disordered systems. It fares remarkably well at reproducing the salient experimental facts of super-cooled liquids. Yet, direct and indisputable experimental validations are missing. In this short survey, we will review recent investigations that broadly support all static aspects of RFOT, but also those for which the standard dynamical extension of the theory appears to be struggling, in particular in relation with facilitation effects. We discuss possible solutions and open issues.
Joint reconstruction-segmentation on graphs

Practical image segmentation tasks concern images which must be reconstructed from noisy, distorted, and/or incomplete observations. A recent approach for solving such tasks is to perform this reconstruction jointly with the segmentation, using each to guide the other. However, this work has so far employed relatively simple segmentation methods, such as the Chan--Vese algorithm. In this paper, we present a method for joint reconstruction-segmentation using graph-based segmentation methods, which have been seeing increasing recent interest. Complications arise due to the large size of the matrices involved, and we show how these complications can be managed. We then analyse the convergence properties of our scheme. Finally, we apply this scheme to distorted versions of ``two cows'' images familiar from previous graph-based segmentation literature, first to a highly noised version and second to a blurred version, achieving highly accurate segmentations in both cases. We compare these results to those obtained by sequential reconstruction-segmentation approaches, finding that our method competes with, or even outperforms, those approaches in terms of reconstruction and segmentation accuracy.
Patching open-vocabulary models by interpolating weights

Open-vocabulary models like CLIP achieve high accuracy across many image classification tasks. However, there are still settings where their zero-shot performance is far from optimal. We study model patching, where the goal is to improve accuracy on specific tasks without degrading accuracy on tasks where performance is already adequate. Towards this goal, we introduce PAINT, a patching method that uses interpolations between the weights of a model before fine-tuning and the weights after fine-tuning on a task to be patched. On nine tasks where zero-shot CLIP performs poorly, PAINT increases accuracy by 15 to 60 percentage points while preserving accuracy on ImageNet within one percentage point of the zero-shot model. PAINT also allows a single model to be patched on multiple tasks and improves with model scale. Furthermore, we identify cases of broad transfer, where patching on one task increases accuracy on other tasks even when the tasks have disjoint classes. Finally, we investigate applications beyond common benchmarks such as counting or reducing the impact of typographic attacks on CLIP. Our findings demonstrate that it is possible to expand the set of tasks on which open-vocabulary models achieve high accuracy without re-training them from scratch.
Semantic Self-adaptation: Enhancing Generalization with a Single Sample

The lack of out-of-domain generalization is a critical weakness of deep networks for semantic segmentation. Previous studies relied on the assumption of a static model, i. e. , once the training process is complete, model parameters remain fixed at test time. In this work, we challenge this premise with a self-adaptive approach for semantic segmentation that adjusts the inference process to each input sample. Self-adaptation operates on two levels. First, it fine-tunes the parameters of convolutional layers to the input image using consistency regularization. Second, in Batch Normalization layers, self-adaptation interpolates between the training and the reference distribution derived from a single test sample. Despite both techniques being well known in the literature, their combination sets new state-of-the-art accuracy on synthetic-to-real generalization benchmarks. Our empirical study suggests that self-adaptation may complement the established practice of model regularization at training time for improving deep network generalization to out-of-domain data. Our code and pre-trained models are available at https://github.com/visinf/self-adaptive.
Implicit Large-eddy Simulations of Global Solar Convection: Effects of Numerical Resolution in Nonrotating and Rotating Cases

Simulating deep solar convection and its coupled mean-field motions is a formidable challenge where few observational results constrain models that suffer from the nonphysical influence of the grid resolution. We present hydrodynamic global implicit large-eddy simulations of deep solar convection performed with the EULAG-MHD code, and we explore the effects of grid resolution on the properties of rotating and nonrotating convection. The results, based on low-order moments and turbulent spectra, reveal that convergence in nonrotating simulations may be achieved at resolutions not much higher than these considered here. The flow is highly anisotropic, with the energy contained in horizontal divergent motions exceeding their radial counterpart by more than three orders of magnitude. By contrast, in rotating simulations, the largest energy is in the toroidal part of the horizontal motions. As the grid resolution increases, the turbulent correlations change in such a way that a solar-like differential rotation, obtained in the simulation with the coarser grid, transitions to an antisolar differential rotation. The reason for this change is the contribution of the effective viscosity to the balance of the forces driving large-scale flows. As the effective viscosity decreases, the angular momentum balance improves, yet the force balance in the meridional direction lessens, favoring a strong meridional flow that advects angular momentum toward the poles. The results suggest that obtaining the correct distribution of angular momentum may not be a mere issue of numerical resolution. Accounting for additional physics, such as magnetism or the near-surface shear layer, may be necessary in simulating the solar interior.
Federated Learning for Digital Twin-Based Vehicular Networks: Architecture and Challenges

Emerging intelligent transportation applications, such as accident reporting, lane change assistance, collision avoidance, and infotainment, will be based on diverse requirements (e.g., latency, reliability, quality of physical experience). To fulfill such requirements, there is a significant need to deploy a digital twin-based intelligent transportation system. Although the twin-based implementation of vehicular networks can offer performance optimization. Modeling twins is a significantly challenging task. Machine learning (ML) can be a preferable solution to model such a virtual model, and specifically federated learning (FL) is a distributed learning scheme that can better preserve privacy compared to centralized ML. Although FL can offer performance enhancement, it requires careful design. Therefore, in this article, we present an overview of FL for the twin-based vehicular network. A general architecture showing FL for the twin-based vehicular network is proposed. Our proposed architecture consists of two spaces, such as twin space and a physical space. The physical space consists of all the physical entities (e.g., cars and edge servers) required for vehicular networks, whereas the twin space refers to the logical space that is used for the deployment of twins. A twin space can be implemented either using edge servers and cloud servers. We also outline a few use cases of FL for the twin-based vehicular network. Finally, the paper is concluded and an outlook on open challenges is presented.
Design and evaluation of IPFS: a storage layer for the decentralized web

Recent years have witnessed growing consolidation of web operations. For example, the majority of web traffic now originates from a few organizations, and even micro-websites often choose to host on large pre-existing cloud infrastructures. In response to this, the "Decentralized Web" attempts to distribute ownership and operation of web services more evenly. This paper describes the design and implementation of the largest and most widely used Decentralized Web platform --- the InterPlanetary File System (IPFS) --- an open-source, content-addressable peer-to-peer network that provides distributed data storage and delivery. IPFS has millions of daily content retrievals and already underpins dozens of third-party applications. This paper evaluates the performance of IPFS by introducing a set of measurement methodologies that allow us to uncover the characteristics of peers in the IPFS network. We reveal presence in more than 2700 Autonomous Systems and 152 countries, the majority of which operate outside large central cloud providers like Amazon or Azure. We further evaluate IPFS performance, showing that both publication and retrieval delays are acceptable for a wide range of use cases. Finally, we share our datasets, experiences and lessons learned.
Superconductivity of non-Fermi liquids described by Sachdev-Ye-Kitaev models

We investigate models of electrons in the Sachdev-Ye-Kitaev class with random and all-to-all electron hopping, electron spin exchange, and Cooper-pair hopping. An attractive on-site interaction between electrons leads to superconductivity at low temperatures. Depending on the relative strengths of the hopping and spin exchange, the normal state at the critical temperature is either a Fermi-liquid or a non-Fermi liquid. We present a large-$M$ (where spin symmetry is enlarged to SU$(M)$) study of the normal state to superconductor phase transition. We describe the transition temperature, the superconducting order parameter, and the electron spectral functions. We contrast between Fermi liquid and non-Fermi liquid normal states: we find that for weaker attractive on-site interaction there is a relative enhancement of $T_c$ when the normal state is a non-Fermi liquid, and correspondingly a strong deviation from BCS limit. Also, the phase transition in this case becomes a first-order transition for strong non-Fermi liquids. On the other hand, for stronger on-site interaction, there is no appreciable difference in $T_c$ between whether the superconductivity emerges from a Fermi liquid or a non-Fermi liquid. Notable features of superconductivity emerging from a non-Fermi liquid are that the superconducting electron spectral function is different from the Fermi-liquid case, with additional peaks at higher energies, and there is no Hebel-Slichter peak in the NMR relaxation rate in the non-Fermi liquid case.
On the evolution of hypersurfaces along their inverse spacetime mean curvature

We construct weak solutions for the evolution of hypersurfaces along their inverse space-time mean curvature in asymptotically flat maximal initial data set. As the speed of the new flow is given by a space-time invariant, it can detect both futureand past-trapped apparent horizons. The weak solution extends concepts developed by Huisken-Ilmanen for inverse mean curvature flow and by Moore for inverse null mean curvature flow.
A Deformation-based Edit Distance for Merge Trees

In scientific visualization, scalar fields are often compared through edit distances between their merge trees. Typical tasks include ensemble analysis, feature tracking and symmetry or periodicity detection. Tree edit distances represent how one tree can be transformed into another through a sequence of simple edit operations: relabeling, insertion and deletion of nodes. In this paper, we present a new set of edit operations working directly on the merge tree as an geometrical or topological object: the represented operations are deformation retractions and inverse transformations on merge trees, which stands in contrast to other methods working on branch decomposition trees. We present a quartic time algorithm for the new edit distance, which is branch decomposition-independent and a metric on the set of all merge trees.
On the spin content of the classical massless Rarita-Schwinger system

We analyze the Rarita-Schwinger massless theory in the Lagrangian and Hamiltonian approaches. At the Lagrangian level, the standard gamma-trace gauge fixing constraint leaves a 1/2 and a 3/2 propagating Poincaré group helicities. At the Hamiltonian level, the result depends on whether the Dirac conjecture is assumed or not. In the affirmative case, a secondary first class constraint is added to the total Hamiltonian and a corresponding gauge fixing condition must be imposed, completely removing the 1/2 sector. In the opposite case, the 1/2 field propagates and the Hamilton field equations match the Euler-Lagrange equations.
Ionic strength-sensitive and pH-insensitive interactions between C-reactive protein (CRP) and an anti-CRP antibody

C-reactive protein (CRP) is an important biomarker of infection and inflammation, as CRP is one of the most prominent acute-phase proteins. CRP is usually detected using anti-CRP antibodies (Abs), where the intermolecular interactions between CRP and the anti-CRP Ab are largely affected by the pH and ionic strength of environmental solutions. Therefore, it is important to understand the environmental effects of CRP–anti-CRP Ab interactions when designing highly sensitive biosensors. Here, we investigated the efficiency of fluorescently labeled CRP–anti-CRP monoclonal antibody (mAb) interactions at different pHs and ionic strengths. Our results indicate that the affinity was insensitive to pH changes in the range of 5.9 to 8.1, while it was significantly sensitive to ionic strength changes. The binding affinity decreased by 55% at an ionic strength of 1.6 mM, when compared to that under a physiological condition (~150 mM). Based on the isoelectric focusing results, both the labeled CRP and anti-CRP mAb were negatively charged in the studied pH range, which rendered the system insensitive to pH changes, but sensitive to ionic strength changes. The decreased ionic strength led to a significant enhancement of the repulsive force between CRP and the anti-CRP mAb. Although the versality of the findings is not fully studied yet, the results provide insights into designing highly sensitive CRP sensors, especially field-effect transistor-based sensors.
Evaluation of a novel SARS‐CoV‐2 rapid antigenic test diagnostic value in respiratory samples; is the reported test accuracy similar to values in the real‐world? A cross‐sectional study

Although reverse transcription‐polymerase chain reaction (RT‐PCR) assay was introduced as the gold standard to detect SARS‐CoV‐2, the method was known to be time‐consuming besides the requirement for an equipped laboratory. This survey aims to investigate a novel SARS‐CoV‐2 antigen test as a diagnostic tool in COVID‐19 patients to overcome these limitations in addition to evaluating COVID‐19 population characteristics.
Nighttime salivary cortisol as a biomarker of stress and an indicator of worsening quality of life in patients with head and neck cancer: A cross‐sectional study

Head and neck cancer (HNC) is a generic term defined on an anatomical–topographic basis to describe malignant tumors located in the oral cavity, pharynx, larynx, and thyroid. A situation commonly presented by individuals with cancer is stress, with evidence indicating a close relationship between stress, behavior, and the immune system with the development and progression of cancer.
One year of continuous dynamic monitoring of a football stadium suspension roof: analysis of data provided by an autonomous monitoring application

In recent years, significant effort has been made to improve the assessment of the health condition of civil engineering structures by means of vibration measurements acquired in permanent monitoring. One of the main obstacles to achieve this goal is the development and implementation of an autonomous application which is capable of extracting relevant information out of the large amount of data collected continuously, regardless of the influence of the environmental effects (e. g. temperatures, wind, etc.). In this context, the present paper describes the most relevant results obtained along one year of permanent dynamic monitoring of a suspension roof. The monitored structure corresponds to the roof of a football stadium which is basically composed by two concrete slabs suspended by 34 pairs of full locked coil cables. The stadium is located in the city of Braga, Portugal, and its suspension roof has been permanently monitored by the Laboratory of Vibrations and Structural Monitoring (ViBest) of the Faculty of Engineering of University of Porto (FEUP) since March 2009. After the installation of the monitoring system on the west slab, the dynamic properties of the structure have been automatically tracked with very few failures. Therefore, apart from describing the results obtained along one year of monitoring, this paper also outlines the major aspects concerning the tools which were especially created to automatically acquire and process the data, remove the environmental effects, and extract useful information to assess the health condition of the monitored structure.
Comparison of traditional and upper thoracic epidural analgesia after off‐pump coronary artery bypass graft surgery: A Quasi‐experimental study

Surgical trauma initiates changes in central and peripheral nervous systems that need to be treated therapeutically to facilitate postoperative pain. The quality of postoperative analgesia is expected to affect clinical outcomes positively. Albeit optimal pain relief following cardiac surgery is often complex, researchers have tried to explore several techniques other than conventional ones during the last decade to find a unique analgesic method for postcardiac surgical patients. This study aims to find a unique analgesic approach that maximizes patient satisfaction after off‐pump coronary artery bypass graft (OPCABG) surgery.
Low assets predict persistent depression through living difficulties amid large-scale disasters: A cohort study

Background: In face of large-scale disasters, persons with fewer assets are at greater risk of persistent poorer mental health than persons with more assets. Everyday daily routine disruptions and financial hardship could mediate this association. Methods: This prospective population-representative study in Hong Kong aimed to investigate the relation between assets during the acute phase of COVID-19 (February – August 2020, T1) and persistent probable depression from T1 to March – August 2021 (T2), as well as the mediating effects of daily routine disruptions and financial hardship on the assets-depression association. Results: Low assets at T1 prospectively related to persistent probable depression from T1 to T2. Primary routine disruptions (i.e., healthy eating and sleep) at T1 and financial hardship at T2 were found to fully mediate the association between T1 assets and persistent probable depression. Limitations: Persistent probable depression reported on the PHQ-9 should be further verified with clinical di- agnoses/interviews. Conclusions: The COVID-19 pandemic was accompanied by a global economic downturn. Persons who have fewer assets could be at greater risk of depression during this period. Our findings suggest a need to provide behavioral and financial assistance to persons with fewer assets in the short run and a need to ensure that everyone has adequate assets to mitigate the mental health consequences of the COVID-19 pandemic in the long run.
Maternal–foetal transfer of severe acute respiratory syndrome coronavirus 2 antibodies among women with and those without HIV infection

In pregnant women, antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein cross the placenta and can be detected in cord-blood at the time of delivery. We measured SARS-CoV-2 full-length antispike IgG in blood samples collected from women living with HIV (WLWHIV) and without HIV when presenting for labour, and from paired cord-blood samples. Antispike IgG was measured in maternal blood at delivery on the Luminex platform. Cord-blood samples from newborns of women in with detectable antispike IgG were analysed. The IgG geometric mean concentrations (GMCs) and the percentage of cord-blood samples with detectable antispike IgG were compared between WLWHIV and without HIV. A total of 184 maternal and cord-blood pairs were analysed, including 47 WLWHIV and 137 without HIV. There was no difference in antispike GMCs between WLWHIV and without HIV [157 binding antibody units (BAU)/ml vs. 187 BAU/ml; P = 0.17)]. Cord-blood samples from newborns of WLWHIV had lower GMCs compared with those without HIV (143 vs. 205 BAU/ml; P = 0.033). Cord-to-maternal blood antibody ratio was 1.0 and similar between the two HIV groups. In WLWHIV, those who were 30 years old or less had lower cord-to-maternal blood antibody ratio (0.75 vs. 1.10; P = 0.037) and their newborns had lower cord-blood GMCs (94 vs. 194 BAU/ml; P = 0.04) compared with the older women. Independently of maternal HIV infection status, there was efficient transplacental transfer of antispike antibodies. The GMCs in cord-blood from newborns of WLWHIV were lower than those in HIV-unexposed newborns.
Mapping class group of a handlebody

Let B be a 3-dimensional handlebody of genus g. Let M be the group of the isotopy classes of orientation preserving homeomorphisms of B. We construct a 2-dimensional simplicial complex X, connected and simply-connected, on which M acts by simplicial transformations and has only a finite number of orbits. From this action we derive an explicit finite presentation ofM. We consider a 3-dimensional handlebody B = Bg of genus g > 0. We may think of B as a solid 3-ball with g solid handles attached to it (see Figure 1). Our goal is to determine an explicit presentation of the mapping class group of B, the group Mg of the isotopy classes of orientation preserving homeomorphisms of B. Every homeomorphism h of B induces a homeomorphism of the boundary S = ∂B of B and we get an embedding of Mg into the mapping class group MCG(S) of the surface S.