Miracle-dz/UniFAR-FTUs huggingface dataset:
Activation to Saliency: Forming High-Quality Labels for Unsupervised Salient Object Detection

This paper focuses on the Unsupervised Salient Object Detection (USOD) issue. We come up with a two-stage Activation-to-Saliency (A2S) framework that effectively excavates saliency cues to train a robust saliency detector. It is worth noting that our method does not require any manual annotation in the whole process. In the first stage, we transform an unsupervisedly pre-trained network to aggregate multi-level features into a single activation map, where an Adaptive Decision Boundary (ADB) is proposed to assist the training of the transformed network. Moreover, a new loss function is proposed to facilitate the generation of high-quality pseudo labels. In the second stage, a self-rectification learning strategy is developed to train a saliency detector and refine the pseudo labels online. In addition, we construct a lightweight saliency detector using two Residual Attention Modules (RAMs) to learn robust saliency information. Extensive experiments on several SOD benchmarks prove that our framework reports significant performance compared with existing USOD methods. Moreover, training our framework on 3,000 images consumes about 1 hour, which is over 10 times faster than previous state-of-the-art methods. Code has been published at https://github.com/moothes/A2S-USOD.
Flexible Networks for Learning Physical Dynamics of Deformable Objects

Learning the physical dynamics of deformable objects with particle-based representation has been the objective of many computational models in machine learning. While several state-of-the-art models have achieved this objective in simulated environments, most existing models impose a precondition, such that the input is a sequence of ordered point sets. That is, the order of the points in each point set must be the same across the entire input sequence. This precondition restrains the model from generalizing to real-world data, which is considered to be a sequence of unordered point sets. In this paper, we propose a model named time-wise PointNet (TP-Net) that solves this problem by directly consuming a sequence of unordered point sets to infer the future state of a deformable object with particle-based representation. Our model consists of a shared feature extractor that extracts global features from each input point set in parallel and a prediction network that aggregates and reasons on these features for future prediction. The key concept of our approach is that we use global features rather than local features to achieve invariance to input permutations and ensure the stability and scalability of our model. Experiments demonstrate that our model achieves state-of-the-art performance with real-time prediction speed in both synthetic dataset and real-world dataset. In addition, we provide quantitative and qualitative analysis on why our approach is more effective and efficient than existing approaches.
AdS 3 holography for non-BPS geometries

By using the approach introduced in Ganchev et al. (Q-Balls Meet Fuzzballs: Non-BPS Microstate Geometries. arXiv:2107.09677 [hep-th]) we construct non-BPS solutions of 6D ( 1 , 0 ) supergravity coupled to two tensor multiplets as a perturbation of AdS 3 × S 3 . These solutions are both regular and asymptotically AdS 3 × S 3 , so according to the standard holographic framework they must have a dual CFT interpretation as non-supersymmetric heavy operators of the D1–D5 CFT. We provide quantitative evidence that such heavy CFT operators are bound states of a large number of light BPS operators that are mutually non-BPS.
Dynamics of Two Dark Solitons in a Polariton Condensate

We theoretically investigate dynamics of two dark solitons in a polariton condensate under nonresonant pumping, based on driven dissipative Gross–Pitaevskii equations coupled to the rate equation. The equation of motion of the relative center position of two-dark soliton is obtained analytically by using the Lagrangian approach. In particular, the analytical expression of the effective potential between two dark solitons is given. The resulting equation of motion captures how the open-dissipative character of a polariton Bose–Einstein condensate affects properties of dynamics of two-dark soliton, i.e., two-dark soliton relax by blending with the background at a finite time. We further simulate the relative motion of two dark solitons numerically with the emphasis on how two-soliton motion is manipulated by the initial velocity, in excellent agreement with the analytical results. The prediction of this work is sufficient for the experimental observations within current facilities.
A Unified Framework for Multi-distribution Density Ratio Estimation

Binary density ratio estimation (DRE), the problem of estimating the ratio $p_1/p_2$ given their empirical samples, provides the foundation for many state-of-the-art machine learning algorithms such as contrastive representation learning and covariate shift adaptation. In this work, we consider a generalized setting where given samples from multiple distributions $p_1, \ldots, p_k$ (for $k>2$), we aim to efficiently estimate the density ratios between all pairs of distributions. Such a generalization leads to important new applications such as estimating statistical discrepancy among multiple random variables like multi-distribution $f$-divergence, and bias correction via multiple importance sampling. We then develop a general framework from the perspective of Bregman divergence minimization, where each strictly convex multivariate function induces a proper loss for multi-distribution DRE. Moreover, we rederive the theoretical connection between multi-distribution density ratio estimation and class probability estimation, justifying the use of any strictly proper scoring rule composite with a link function for multi-distribution DRE. We show that our framework leads to methods that strictly generalize their counterparts in binary DRE, as well as new methods that show comparable or superior performance on various downstream tasks.
Computation of Feedback Control Laws Based on Switched Tracking of Demonstrations

A common approach in robotics is to learn tasks by generalizing from special cases given by a so-called demonstrator. In this paper, we apply this paradigm and present an algorithm that uses a demonstrator (typically given by a trajectory optimizer) to automatically synthesize feedback controllers for steering a system described by ordinary differential equations into a goal set. The resulting feedback control law switches between the demonstrations that it uses as reference trajectories. In comparison to the direct use of trajectory optimization as a control law, for example, in the form of model predictive control, this allows for a much simpler and more efficient implementation of the controller. The synthesis algorithm comes with rigorous convergence and optimality results, and computational experiments confirm its efficiency.
A phenomenological theory of superconductor diodes

Nonreciprocal responses in noncentrosymmetric systems contain a broad range of phenomena. Especially, non-dissipative and coherent nonreciprocal transport in solids is an important fundamental issue. The recent discovery of superconductor (SC) diodes under external magnetic fields, where the magnitude of the critical current changes as the direction is reversed, significantly boosted this research area. However, a theoretical understanding of such phenomena is lacking. Here, we provide theoretical descriptions of SC diodes with a generalized Ginzburg–Landau method. The theory is applied to Rashba spin–orbit coupled systems, where analytical relations between the nonreciprocal critical currents and the system parameters are achieved. Numerical calculations with mean-field theory are also obtained to study broader parameter regions. These results offer a rather general description and design principles of SC diodes.
Development of simplified thin-walled beam models for crashworthiness analyses

Abstract In this paper, simplified finite element models for thin-walled beams with hexagon and circular cross sections are created and validated through crashworthiness analyses. To create the simplified models, the published collapse theories of thin-walled hexagon and circular beams under axial compression are reviewed and applied to simulate their axial buckling behavior. Meanwhile, their moment – bending relationship is predicted by derived analytical equations, which are then applied to the simplified models to simulate the bending behavior. After the simplified models are generated, the same crashworthiness analyses are performed on both simplified and detailed models to verify the developed simplified models as well as the applied existing and derived collapse theories. From the results, good agreements are achieved between the detailed and simplified models, and the derived collapse theories for thin-walled hexagon and circular beams are therefore validated.
Discovery of highly reactive self-splicing group II introns within the mitochondrial genomes of human pathogenic fungi

Abstract Fungal pathogens represent an expanding global health threat for which treatment options are limited. Self-splicing group II introns have emerged as promising drug targets, but their development has been limited by a lack of information on their distribution and architecture in pathogenic fungi. To meet this challenge, we developed a bioinformatic workflow for scanning sequence data to identify unique RNA structural signatures within group II introns. Using this approach, we discovered a set of ubiquitous introns within thermally dimorphic fungi (genera of Blastomyces, Coccidioides and Histoplasma). These introns are the most biochemically reactive group II introns ever reported, and they self-splice rapidly under near-physiological conditions without protein cofactors. Moreover, we demonstrated the small molecule targetability of these introns by showing that they can be inhibited by the FDA-approved drug mitoxantrone in vitro. Taken together, our results highlight the utility of structure-based informatic searches for identifying riboregulatory elements in pathogens, revealing a striking diversity of reactive self-splicing introns with great promise as antifungal drug targets.
Energy planning using strategic environmental assessment - exploring new tools in a Swedish municipality

Development of local energy systems, i.e. supply, distribution and use of energy in a municipality, is crucial for overall environmental performance of national energy systems. It follows that availability and effectiveness of instruments to control this development are of great interest. For local authorities, energy planning may be such an instrument. Swedish legislation mandates each local authority to issue a plan for all supply and use of energy. In accordance with the EU directive 2001/42/EC, legislation now also requires that such plans include environmental assessment. This is often referred to as strategic environmental assessment (SEA). An ongoing research project is reported where tools for SEA are implemented and evaluated in municipal energy planning. The hypothesis is that such tools will improve the planning process, compared with current practice, and subsequently the energy plan in terms of its ability to direct the development of local energy systems towards less environmental impact. This development includes both increased energy efficiency and use of renewable energy sources. A planning process based on earlier studies of energy planning, on the EU-directive, and on earlier SEA processes is proposed and implemented in a Swedish municipality. Several tools are provided, such as, scenario analysis, environmental life cycle analysis, and public participation in the form of citizen panels. The main research question that will eventually be analysed by evaluating the process and its outcome is: “Does the use of SEA tools lead to improved energy planning from an environmental point of view? ”
γδ T cell IFNγ production is directly subverted by Yersinia pseudotuberculosis outer protein YopJ in mice and humans

Yersinia pseudotuberculosis is a foodborne pathogen that subverts immune function by translocation of Yersinia outer protein (Yop) effectors into host cells. As adaptive γδ T cells protect the intestinal mucosa from pathogen invasion, we assessed whether Y. pseudotuberculosis subverts these cells in mice and humans. Tracking Yop translocation revealed that the preferential delivery of Yop effectors directly into murine Vγ4 and human Vδ2+ T cells inhibited anti-microbial IFNγ production. Subversion was mediated by the adhesin YadA, injectisome component YopB, and translocated YopJ effector. A broad anti-pathogen gene signature and STAT4 phosphorylation levels were inhibited by translocated YopJ. Thus, Y. pseudotuberculosis attachment and translocation of YopJ directly into adaptive γδ T cells is a major mechanism of immune subversion in mice and humans. This study uncovered a conserved Y. pseudotuberculosis pathway that subverts adaptive γδ T cell function to promote pathogenicity.
Highly accurate whole-genome imputation of SARS-CoV-2 from partial or low-quality sequences

Abstract Background The current SARS-CoV-2 pandemic has emphasized the utility of viral whole-genome sequencing in the surveillance and control of the pathogen. An unprecedented ongoing global initiative is producing hundreds of thousands of sequences worldwide. However, the complex circumstances in which viruses are sequenced, along with the demand of urgent results, causes a high rate of incomplete and, therefore, useless sequences. Viral sequences evolve in the context of a complex phylogeny and different positions along the genome are in linkage disequilibrium. Therefore, an imputation method would be able to predict missing positions from the available sequencing data. Results We have developed the impuSARS application, which takes advantage of the enormous number of SARS-CoV-2 genomes available, using a reference panel containing 239,301 sequences, to produce missing data imputation in viral genomes. ImpuSARS was tested in a wide range of conditions (continuous fragments, amplicons or sparse individual positions missing), showing great fidelity when reconstructing the original sequences, recovering the lineage with a 100% precision for almost all the lineages, even in very poorly covered genomes (<20%). Conclusions Imputation can improve the pace of SARS-CoV-2 sequencing production by recovering many incomplete or low-quality sequences that would be otherwise discarded. ImpuSARS can be incorporated in any primary data processing pipeline for SARS-CoV-2 whole-genome sequencing.
Tumor necrosis factor (TNF) induces astrogliosis, microgliosis and promotes survival of cortical neurons

Objectives Neuro-inflammation occurs as a sequence of brain injury and is associated with production of cytokines. Cytokines can modulate the function and survival of neurons, microglia and astrocytes. The objective of this study is to examine the effect of TNF on the neurons, microglia and astrocytes in normal brain and stab wound brain injury. Methods Normal BALB/c male mice (N) without any injury were subdivided into NA and NB groups. Another set mouse was subjected to stab wound brain injury (I) and were subdivided into IA and IB. NA and IA groups received intraperitoneal injections of TNF (1 µg/kg body weight/day) for nine days, whereas NB and IB groups received intraperitoneal injections of PBS. Animals were killed on 1st, 2nd, 3rd, 7th, and 9th day. Frozen brain sections through the injury site in IA and IB or corresponding region in NA and NB groups were stained for neurodegeneration, immunostained for astrocytes, microglia and neurons. Western blotting for GFAP and ELISA for BDNF were done from the tissues collected from all groups. Results The number of degenerating neurons significantly decreased in TNF treated groups. There was a significant increase in the number of astrocytes and microglia in TNF treated groups compared to PBS treated groups. In addition, it was found that TNF stimulated the expression of GFAP and BDNF in NA and IA groups. Conclusions TNF induces astrogliosis and microgliosis in normal and injured brain and promotes the survival of cortical neurons in stab wound brain injury, may be by upregulating the BDNF level.
Serum from Older Adults Increases Apoptosis and Molecular Aging Markers in Human Hippocampal Progenitor Cells

Age-related alteration in neural stem cell function is linked to neurodegenerative conditions and cognitive decline. In rodents, this can be reversed by exposure to a young systemic milieu and conversely, the old milieu can inhibit stem cell function in young rodents. In this study, we investigated the in vitro effect of the human systemic milieu on human hippocampal progenitor cells (HPCs) using human serum from early adulthood, mid-life and older age. We showed that neuroblast number following serum treatment is predictive of larger dentate gyrus, CA3, CA4 and whole hippocampus volumes and that allogeneic human serum from asymptomatic older individuals induced a two-fold increase in apoptotic cell death of HPCs compared with serum from young adults. General linear models revealed that variability in markers of proliferation and differentiation was partly attributable to use of antihypertensive medication and very mild cognitive decline among older subjects. Finally, using an endophenotype approach and whole-genome expression arrays, we showed upregulation of established and novel ageing molecular hallmarks in response to old serum. Serum from older subjects induced a wide range of cellular and molecular phenotypes, likely reflecting a lifetime of environmental exposures. Our findings support a role for the systemic enviroment in neural stem cell maintenance and are in line with others highlighting a distinction between neurobiological and chronological ageing. Finally, the herein described serum assay can be used by future studies to further analyse the effect of environmental exposures as well as to determine the role of the systemic environment in health and disease.
Relaxed 3D genome conformation facilitates the pluripotent to totipotent-like state transition in embryonic stem cells

Abstract The 3D genome organization is crucial for gene regulation. Although recent studies have revealed a uniquely relaxed genome conformation in totipotent early blastomeres of both fertilized and cloned embryos, how weakened higher-order chromatin structure is functionally linked to totipotency acquisition remains elusive. Using low-input Hi-C, ATAC-seq and ChIP-seq, we systematically examined the dynamics of 3D genome and epigenome during pluripotent to totipotent-like state transition in mouse embryonic stem cells (ESCs). The spontaneously converted 2-cell-embryo-like cells (2CLCs) exhibited more relaxed chromatin architecture compared to ESCs, including global weakening of both enhancer-promoter interactions and TAD insulation. While the former correlated with inactivation of ESC enhancers and down-regulation of pluripotent genes, the latter might facilitate contacts between the putative new enhancers arising in 2CLCs and neighboring 2C genes. Importantly, disruption of chromatin organization by depleting CTCF or the cohesin complex promoted the ESC to 2CLC transition. Our results thus establish a critical role of 3D genome organization in totipotency acquisition.
Electrothermal effects at the microscale and their consequences on system design

Electrothermal devices exhibit increasing secondary effects upon downscaling. These effects alter their temperature profiles and thermal energy distributions. Based on scaling laws, we show that the Thomson effect is increased over Joule heating as the structures are sized down. Thermal models are upgraded to take into account this secondary effect, and finite element simulations together with experimental runs validate them.
Motion Model of Planned Target in Marine Simulation System

1 Abstract In marine simulator, trainee controls the position of own ship. The precision of own shipi s hydrodynamic mathematical model is relatively high. Usually, there isni t a hydrodynamic model corresponding to the target ships planned by instructor. These target ships navigation along the planned sea route with speed and course set by instructor. The course of ship will change suddenly to another when ship passes a waypoint, so shipi s image in visual system will change suddenly. This phenomenon will greatly decrease the fidelity of visual system. This paper introduces a simplified mathematical model of these target ships and how to calculate the beginning position, the instantaneous position, the course and the end position of ships in details. The model has been applied in marine simulation system successfully.
All-Passive-Type Rotary Magnetic Bearing System Based on Stability Principle of Sleeping Tops

It is well known that magnetic levitation using permanent magnets without control is impossible. However, just as in the case of a sleeping top, if the body to be levitated has rotational velocity, the situation will be changed. In this paper, a new type of system which utilizes both magnetic force of permanent magnets for vertical levitation and a null-flux coil system for axial or radial restoring force is proposed. A method of decreasing the required restoring stiffness for the null-flux system is given. Asymptotic stability of the top is discussed using eigenvalue analysis. Damping methods for the asymptotic stability of two conical vibration modes using eddy current dampers and the null-flux system are proposed. From preliminary experimental results, the possibility of constructing the all-passive-type rotary magnetic bearing is clarified.
In silico modeling revealed new insights into the mechanism of action of enzyme 2'-5'-oligoadenylate synthetase in cattle

Abstract The innate immune system has an important role in developing the initial resistance to virus infection, and the ability of oligoadenylate synthetase to overcome viral evasion and enhance innate immunity is already established in humans. In the present study, we have tried to explore the molecular and structural variations present in Sahiwal (indigenous) and crossbred (Frieswal) cattle to identify the molecular mechanism of action of OAS1 gene in activation of innate immune response. The significant changes in structural alignment in terms of orientation of loops, shortening of β-sheets and formation of 3–10 α-helix was noticed in Sahiwal and Frieswal cattle. Further, it has been observed that OAS1 from Sahiwal had better binding with APC and DTP ligand than Frieswal OAS1. A remarkable change was seen in orientation at the nucleoside base region of both the ligands, which are bound with OAS1 protein from Frieswal and Sahiwal cattle. The Molecular Dynamic study of apo and ligand complex structures was provided more insight towards the stability of OAS1 from both cattle. This analysis displayed that the Sahiwal cattle protein has more steady nature throughout the simulation and has better binding towards Frieswal in terms of APC and DTP binding. Thus, OAS1 protein is the potential target for explaining the innate immune response in Sahiwal than Frieswal. Communicated by Ramaswamy H. Sarma