Miracle-dz/UniFAR-FTUs huggingface dataset:
Local Evolution of Seed Flotation in Arabidopsis

Arabidopsis seeds rapidly release hydrophilic polysaccharides from the seed coat on imbibition. These form a heavy mucilage layer around the seed that makes it sink in water. Fourteen natural Arabidopsis variants from central Asia and Scandinavia were identified with seeds that have modified mucilage release and float. Four of these have a novel mucilage phenotype with almost none of the released mucilage adhering to the seed and the absence of cellulose microfibrils. Mucilage release was modified in the variants by ten independent causal mutations in four different loci. Seven distinct mutations affected one locus, coding the MUM2 β-D-galactosidase, and represent a striking example of allelic heterogeneity. The modification of mucilage release has thus evolved a number of times independently in two restricted geographical zones. All the natural mutants identified still accumulated mucilage polysaccharides in seed coat epidermal cells. Using nuclear magnetic resonance (NMR) relaxometry their production and retention was shown to reduce water mobility into internal seed tissues during imbibition, which would help to maintain seed buoyancy. Surprisingly, despite released mucilage being an excellent hydrogel it did not increase the rate of water uptake by internal seed tissues and is more likely to play a role in retaining water around the seed.
Burst Super-Resolution with Diffusion Models for Improving Perceptual Quality

While burst LR images are useful for improving the SR image quality compared with a single LR image, prior SR networks accepting the burst LR images are trained in a deterministic manner, which is known to produce a blurry SR image. In addition, it is difficult to perfectly align the burst LR images, making the SR image more blurry. Since such blurry images are perceptually degraded, we aim to reconstruct the sharp high-fidelity boundaries. Such high-fidelity images can be reconstructed by diffusion models. However, prior SR methods using the diffusion model are not properly optimized for the burst SR task. Specifically, the reverse process starting from a random sample is not optimized for image enhancement and restoration methods, including burst SR. In our proposed method, on the other hand, burst LR features are used to reconstruct the initial burst SR image that is fed into an intermediate step in the diffusion model. This reverse process from the intermediate step 1) skips diffusion steps for reconstructing the global structure of the image and 2) focuses on steps for refining detailed textures. Our experimental results demonstrate that our method can improve the scores of the perceptual quality metrics. Code: https://github.com/placerkyo/BSRD
An Accelerated HPE-Type Algorithm for a Class of Composite Convex-Concave Saddle-Point Problems

This paper proposes a new algorithm for solving a class of composite convex-concave saddle-point problems. The new algorithm is a special instance of the hybrid proximal extragradient framework in which a Nesterov accelerated variant is used to approximately solve the prox subproblems. One of the advantages of the new method is that it works for any constant choice of proximal stepsize. Moreover, a suitable choice of the latter stepsize yields a method with the best known (accelerated inner) iteration complexity for the aforementioned class of saddle-point problems. In contrast to the smoothing technique of [Y. Nesterov, Math. Program. , 103 (2005), pp. 127--152], our accelerated method does not assume that a feasible set is bounded due to its proximal point nature. Experiment results on three problem sets show that the new method outperforms Nesterov's smoothing technique of [Y. Nesterov, Math. Program. , 103 (2005), pp. 127--152].
Literature survey on low rank approximation of matrices

Abstract Low rank approximation of matrices has been well studied in literature. Singular value decomposition, QR decomposition with column pivoting, rank revealing QR factorization, Interpolative decomposition, etc. are classical deterministic algorithms for low rank approximation. But these techniques are very expensive ( operations are required for matrices). There are several randomized algorithms available in the literature which are not so expensive as the classical techniques (but the complexity is not linear in n). So, it is very expensive to construct the low rank approximation of a matrix if the dimension of the matrix is very large. There are alternative techniques like Cross/Skeleton approximation which gives the low-rank approximation with linear complexity in n. In this article we review low rank approximation techniques briefly and give extensive references of many techniques.
Chirality-Induced Magnet-Free Spin Generation in a Semiconductor

Electrical generation and transduction of polarized electron spins in semiconductors are of central interest in spintronics and quantum information science. While spin generation in semiconductors has been frequently realized via electrical injection from a ferromagnet, there are significant advantages in nonmagnetic pathways of creating spin polarization. One such pathway exploits the interplay of electron spin with chirality in electronic structures or real space. Here, utilizing chirality-induced spin selectivity (CISS), we demonstrate efficient creation of spin accumulation in n-doped GaAs via electric current injection from a normal metal (Au) electrode through a self-assembled monolayer of chiral molecules ({\alpha}-helix L-polyalanine, AHPA-L). The resulting spin polarization is detected as a Hanle effect in the n-GaAs, which is found to obey a distinct universal scaling with temperature and bias current consistent with chirality-induced spin accumulation. The experiment constitutes a definitive observation of CISS in a fully nonmagnetic device structure and demonstration of its ability to generate spin accumulation in a conventional semiconductor. The results thus place key constraints on the physical mechanism of CISS and present a new scheme for magnet-free semiconductor spintronics.
Quantitative analysis of the kinetics of end-dependent disassembly of RecA filaments from ssDNA.

On linear single-stranded DNA, RecA filaments assemble and disassemble in the 5' to 3' direction. Monomers (or other units) associate at one end and dissociate from the other. ATP hydrolysis occurs throughout the filament. Dissociation can result when ATP is hydrolyzed by the monomer at the disassembly end. We have developed a comprehensive model for the end-dependent filament disassembly process. The model accounts not only for disassembly, but also for the limited reassembly that occurs as DNA is vacated by disassembling filaments. The overall process can be monitored quantitatively by following the resulting decline in DNA-dependent ATP hydrolysis. The rate of disassembly is highly pH dependent, being negligible at pH 6 and reaching a maximum at pH values above 7. 5. The rate of disassembly is not significantly affected by the concentration of free RecA protein within the experimental uncertainty. For filaments on single-stranded DNA, the monomer kcat for ATP hydrolysis is 30 min-1, and disassembly proceeds at a maximum rate of 60-70 monomers per minute per filament end. The latter rate is that predicted if the ATP hydrolytic cycles of adjacent monomers are not coupled in any way.
Tracking Control of Multiple-Wheeled Mobile Robots With Limited Information of a Desired Trajectory

This paper considers the tracking control problem for a group of nonholonomic wheeled mobile robots with limited information of a desired trajectory. With the aid of communications between systems, distributed control laws are proposed such that the state of each mobile robot asymptotically tracks the desired trajectory. To show the effectiveness of the proposed approach, simulation results are presented.
Cardiovascular Manifestations of Rheumatologic Diseases

The prevalence and importance of cardiovascular disease in rheumatologic disorders have increased in the setting of therapeutic advances, resulting in longer life expectancy, a growing understanding of the importance of inflammation and the immune system in the initiation and progression of atherosclerosis, and enhanced disease detection with the use of sophisticated noninvasive cardiac and vascular diagnostic technology. This article will briefly review cardiovascular manifestations of rheumatologic diseases with an emphasis on recent clinical research. Rheumatologic diseases will be discussed individually for ease of reference, although pathophysiology and basis for cardiovascular abnormalities may be common among some of them. When applicable, cardiovascular disease will be categorized as vascular (accelerated atherosclerosis, arterial stiffness, small-vessel disease, aortic disease), myocardial (abnormalities of structure and function), valvular, pericardial, and conduction diseases. Endothelial dysfunction, although reported in some rheumatologic diseases, will not be systematically discussed in view of space limitations and the nonspecific and incompletely characterized nature of this abnormality. Discussion of suggested mechanisms underlying cardiovascular manifestations of rheumatologic diseases is beyond the scope of this review. Rheumatologic diseases with vasculitis as the primary manifestation, such as giant cell arteritis and Takayasu’s arteritis, will not be discussed. Rheumatoid arthritis (RA) is a common chronic autoimmune disease associated with systemic inflammation, a female predominance, and a prevalence of ≈1% that increases with age.1 The diagnosis is a clinical one, based on the characteristic joint manifestations (Table 1). 2 The presence of rheumatoid factor is included in the diagnostic criteria, although antibodies to cyclic citrullinated peptides are more specific for RA.3 Although the precise antigenic stimulus driving the autoimmunity in RA has not been identified, a recent study demonstrated an increased risk of developing RA in smokers in whom citrullinated antigens are expressed on bronchoalveolar immune cells.4 RA is associated with specific human leukocyte …
Measuring the baryon fraction using galaxy clustering

The amplitude of the baryon signature in galaxy clustering depends on the cosmological baryon fraction. We consider two ways to isolate this signal in galaxy redshift surveys. First, we extend standard template-based Baryon Acoustic Oscillation (BAO) models to include the amplitude of the baryonic signature by splitting the transfer function into baryon and cold dark matter components with freely varying proportions. Second, we include the amplitude of the split as an extra parameter in Effective Field Theory (EFT) models of the full galaxy clustering signal. We find similar results from both approaches. For the Baryon Oscillation Spectroscopic Survey (BOSS) data we find $f_b\equiv\Omega_b/\Omega_m=0.173\pm0.027$ for template fits post-reconstruction, $f_b=0.153\pm0.029$ for template fits pre-reconstruction, and $f_b=0.154\pm0.022$ for EFT fits, with an estimated systematic error of 0.013 for all three methods. Using reconstruction only produces a marginal improvement for these measurements. Although significantly weaker than constraints on $f_b$ from the Cosmic Microwave Background, these measurements rely on very simple physics and, in particular, are independent of the sound horizon. In a companion paper we show how they can be used, together with Big Bang Nucleosynthesis measurements of the physical baryon density and geometrical measurements of the matter density from the Alcock-Paczynski effect, to constrain the Hubble parameter. While the constraints on $H_0$ based on density measurements from BOSS are relatively weak, measurements from DESI and Euclid will lead to errors on $H_0$ that are competitive with those from local distance ladder measurements.
Robustness of type-II Dirac cones in biphenylene-based structures

The electronic properties of one- and two-dimensional biphenylene-based systems, such as nanoribbons and bilayers, are studied within a unified approach. Besides the bilayer with direct (AA) stacking, we present two additional symmetric stackings for bilayer biphenylene that we denote by AB, by analogy with bilayer graphene, and AX, which can be derived by a small translation (slip) from the AA bilayer, with distinct band structures. We combine first-principles calculations with a tight-binding model to provide a realistic effective description of these structures. Our approach provides a global framework to characterize and analyze the robustness of the type-II Dirac cone within these structures, captures the variations caused by different stackings, and highlights important symmetries inherent in the biphenylene nanoribbon Dirac cones and edge states.
Renal Sympathetic Denervation for Blood Pressure Control: A Review of the Current Evidence and Ongoing Studies

Hypertensive heart disease is the leading cause of mortality and morbidity in the United States. Despite widespread availability of medical therapy, it remains a challenge to treat. Autonomic nervous system imbalance resulting in overactivity of the sympathetic nervous system is integral to the development of hypertension and ultimately the development of hypertensive heart disease. Although the results with renal sympathetic denervation so far have been encouraging, optimism has recently been tempered with the broadcast alert from Medtronic, the sponsor of Symplicity HTN‐3, that the trial did not meet its primary efficacy endpoint. The principal focus of this article is to review the developments in renal sympathetic denervation for the treatment of resistant hypertension.
Deep decomposition method for the limited aperture inverse obstacle scattering problem

In this paper, we consider a deep learning approach to the limited aperture inverse obstacle scattering problem. It is well known that traditional deep learning relies solely on data, which may limit its performance for the inverse problem when only indirect observation data and a physical model are available. A fundamental question arises in light of these limitations: is it possible to enable deep learning to work on inverse problems without labeled data and to be aware of what it is learning? This work proposes a deep decomposition method (DDM) for such purposes, which does not require ground truth labels. It accomplishes this by providing physical operators associated with the scattering model to the neural network architecture. Additionally, a deep learning based data completion scheme is implemented in DDM to prevent distorting the solution of the inverse problem for limited aperture data. Furthermore, apart from addressing the ill-posedness imposed by the inverse problem itself, DDM is a physics-aware machine learning technique that can have interpretability property. The convergence result of DDM is theoretically proven. Numerical experiments are presented to demonstrate the validity of the proposed DDM even when the incident and observation apertures are extremely limited.
DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs

This paper revives Densely Connected Convolutional Networks (DenseNets) and reveals the underrated effectiveness over predominant ResNet-style architectures. We believe DenseNets' potential was overlooked due to untouched training methods and traditional design elements not fully revealing their capabilities. Our pilot study shows dense connections through concatenation are strong, demonstrating that DenseNets can be revitalized to compete with modern architectures. We methodically refine suboptimal components - architectural adjustments, block redesign, and improved training recipes towards widening DenseNets and boosting memory efficiency while keeping concatenation shortcuts. Our models, employing simple architectural elements, ultimately surpass Swin Transformer, ConvNeXt, and DeiT-III - key architectures in the residual learning lineage. Furthermore, our models exhibit near state-of-the-art performance on ImageNet-1K, competing with the very recent models and downstream tasks, ADE20k semantic segmentation, and COCO object detection/instance segmentation. Finally, we provide empirical analyses that uncover the merits of the concatenation over additive shortcuts, steering a renewed preference towards DenseNet-style designs. Our code is available at https://github.com/naver-ai/rdnet.
Self-Supervised Interpretable Sensorimotor Learning via Latent Functional Modularity

We introduce MoNet, a novel method that combines end-to-end learning with modular network architectures for self-supervised and interpretable sensorimotor learning. MoNet is composed of three functionally distinct neural modules: Perception, Planning, and Control. Leveraging its inherent modularity through a cognition-guided contrastive loss function, MoNet efficiently learns task-specific decision-making processes in latent space, without requiring task-level supervision. Moreover, our method incorporates an online post-hoc explainability approach, which enhances the interpretability of the end-to-end inferences without a trade-off in sensorimotor performance. In real-world indoor environments, MoNet demonstrates effective visual autonomous navigation, surpassing baseline models by 11% to 47% in task specificity analysis. We further delve into the interpretability of our network through the post-hoc analysis of perceptual saliency maps and latent decision vectors. This offers insights into the incorporation of explainable artificial intelligence within the realm of robotic learning, encompassing both perceptual and behavioral perspectives.
The Boundary Proposal

One of the leading ideas for the beginning of the Universe is the Hartle-Hawking `No-Boundary Proposal. ' Since the Cobordism Conjecture claims that any spacetime allows for a dynamical boundary, we suggest that one may equally well consider a `Boundary Proposal'. Specifically, the corresponding euclidean instanton is a sphere with two holes around north and south pole cut out. Analogously to the Hartle-Hawking proposal, the sphere is then cut in two at the equator and half of it is dropped. The equator is glued to an expanding Lorentzian de Sitter space, implementing a beginning of the Universe with a spacelike spherical boundary at its earliest moment. This process is in principle on equal footing with the one based on the no-boundary instanton. In fact, if the Linde-Vilenkin sign choice is used, this `Boundary' creation process may even dominate. An intriguing implication arises if tensionless end-of-the-world branes, as familiar from type-IIA or M-theory, are available: Analogously to the Boundary Proposal, one may then be able to create a compact, flat torus universe from nothing, without any exponential suppression or enhancement factors.
Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model

Text-to-image (T2I) generative models have recently emerged as a powerful tool, enabling the creation of photo-realistic images and giving rise to a multitude of applications. However, the effective integration of T2I models into fundamental image classification tasks remains an open question. A prevalent strategy to bolster image classification performance is through augmenting the training set with synthetic images generated by T2I models. In this study, we scrutinize the shortcomings of both current generative and conventional data augmentation techniques. Our analysis reveals that these methods struggle to produce images that are both faithful (in terms of foreground objects) and diverse (in terms of background contexts) for domain-specific concepts. To tackle this challenge, we introduce an innovative inter-class data augmentation method known as Diff-Mix (https://github.com/Zhicaiwww/Diff-Mix), which enriches the dataset by performing image translations between classes. Our empirical results demonstrate that Diff-Mix achieves a better balance between faithfulness and diversity, leading to a marked improvement in performance across diverse image classification scenarios, including few-shot, conventional, and long-tail classifications for domain-specific datasets.
Analysis of the Sonic Hedgehog Signaling Pathway in Normal and Abnormal Bladder Development

In this study, we examined the expression of Sonic Hedgehog, Patched, Gli1, Gli2, Gli3 and Myocardin in the developing bladders of male and female normal and megabladder (mgb−/−) mutant mice at embryonic days 12 through 16 by in situ hybridization. This analysis indicated that each member of the Sonic Hedgehog signaling pathway as well as Myocardin displayed distinct temporal and spatial patterns of expression during normal bladder development. In contrast, mgb−/− bladders showed both temporal and spatial changes in the expression of Patched, Gli1 and Gli3 as well as a complete lack of Myocardin expression. These changes occurred primarily in the outer mesenchyme of developing mgb−/− bladders consistent with the development of an amuscular bladder phenotype in these animals. These results provide the first comprehensive analysis of the Sonic Hedgehog signaling pathway during normal bladder development and provide strong evidence that this key signaling cascade is critical in establishing radial patterning in the developing bladder. In addition, the lack of detrusor smooth muscle development observed in mgb−/− mice is associated with bladder-specific temporospatial changes in Sonic Hedgehog signaling coupled with a lack of Myocardin expression that appears to result in altered patterning of the outer mesenchyme and poor initiation and differentiation of smooth muscle cells within this region of the developing bladder.
Tfap2a-dependent changes in mouse facial morphology result in clefting that can be ameliorated by a reduction in Fgf8 gene dosage

Failure of facial prominence fusion causes cleft lip and palate (CL/P), a common human birth defect. Several potential mechanisms can be envisioned that would result in CL/P, including failure of prominence growth and/or alignment as well as a failure of fusion of the juxtaposed epithelial seams. Here, using geometric morphometrics, we analyzed facial outgrowth and shape change over time in a novel mouse model exhibiting fully penetrant bilateral CL/P. This robust model is based upon mutations in Tfap2a, the gene encoding transcription factor AP-2α, which has been implicated in both syndromic and non-syndromic human CL/P. Our findings indicate that aberrant morphology and subsequent misalignment of the facial prominences underlies the inability of the mutant prominences to fuse. Exencephaly also occured in some of the Tfap2a mutants and we observed additional morphometric differences that indicate an influence of neural tube closure defects on facial shape. Molecular analysis of the CL/P model indicates that Fgf signaling is misregulated in the face, and that reducing Fgf8 gene dosage can attenuate the clefting pathology by generating compensatory changes. Furthermore, mutations in either Tfap2a or Fgf8 increase variance in facial shape, but the combination of these mutations restores variance to normal levels. The alterations in variance provide a potential mechanistic link between clefting and the evolution and diversity of facial morphology. Overall, our findings suggest that CL/P can result from small gene-expression changes that alter the shape of the facial prominences and uncouple their coordinated morphogenesis, which is necessary for normal fusion.