Miracle-dz/UniFAR-FTUs huggingface dataset:
Sequential Quadratic Optimization for Nonlinear Equality Constrained Stochastic Optimization

Sequential quadratic optimization algorithms are proposed for solving smooth nonlinear optimization problems with equality constraints. The main focus is an algorithm proposed for the case when the constraint functions are deterministic, and constraint function and derivative values can be computed explicitly, but the objective function is stochastic. It is assumed in this setting that it is intractable to compute objective function and derivative values explicitly, although one can compute stochastic function and gradient estimates. As a starting point for this stochastic setting, an algorithm is proposed for the deterministic setting that is modeled after a state-of-the-art line-search SQP algorithm, but uses a stepsize selection scheme based on Lipschitz constants (or adaptively estimated Lipschitz constants) in place of the line search. This sets the stage for the proposed algorithm for the stochastic setting, for which it is assumed that line searches would be intractable. Under reasonable assumptions, convergence (resp.,~convergence in expectation) from remote starting points is proved for the proposed deterministic (resp.,~stochastic) algorithm. The results of numerical experiments demonstrate the practical performance of our proposed techniques.
Static and Streaming Data Structures for Fréchet Distance Queries

Given a curve P with points in ℝd in a streaming fashion, and parameters ɛ > 0 and k, we construct a distance oracle that uses \(O(\frac{1}{\varepsilon })^{kd}\log \varepsilon ^{-1}\) space, and given a query curve Q with k points in ℝd returns in \(\tilde{O}(kd)\) time a 1+ɛ approximation of the discrete Fréchet distance between Q and P. In addition, we construct simplifications in the streaming model, oracle for distance queries to a sub-curve (in the static setting), and introduce the zoom-in problem. Our algorithms work in any dimension d, and therefore we generalize some useful tools and algorithms for curves under the discrete Fréchet distance to work efficiently in high dimensions.
Best proximity point results in topological spaces and extension of Banach contraction principle

In this paper, we introduce the notion of topologically Banach contraction mapping defined on an arbitrary topological space X with the help of a continuous function $g:X\times X\rightarrow \mathbb{R}$ and investigate the existence of fixed points of such mapping. Moreover, we introduce two types of mappings defined on a non-empty subset of X and produce sufficient conditions which will ensure the existence of best proximity points for these mappings. Our best proximity point results also extend some existing results from metric spaces or Banach spaces to topological spaces. More precisely, our newly introduced mappings are more general than that of the corresponding notions introduced by Bunlue and Suantai [Arch. Math. (Brno), 54(2018), 165-176]. We present several examples to validate our results and justify its motivation. To study best proximity point results, we introduce the notions of g-closed, g-sequentially compact subsets of X and produce examples to show that there exists a non-empty subset of X which is not closed, sequentially compact under usual topology but is g-closed and g-sequentially compact.
Balance Scene Learning Mechanism for Offshore and Inshore Ship Detection in SAR Images

Huge imbalance of different scenes’ sample numbers seriously reduces synthetic aperture radar (SAR) ship detection accuracy. Thus, to solve this problem, this letter proposes a balance scene learning mechanism (BSLM) for offshore and inshore ship detection in SAR images. BSLM involves three steps: 1) based on unsupervised representation learning, a generative adversarial network (GAN) is used to extract the scene features of SAR images; 2) using these features, a scene binary cluster (offshore/inshore) is conducted by ${K}$ -means; and 3) finally, the small cluster’s samples (inshore) are augmented via replication, rotation transformation or noise addition to balance another big cluster (offshore), so as to eliminate scene learning bias and obtain balanced learning representation ability that can enhance learning benefits and improve detection accuracy. This letter applies BSLM to four widely used and open-sourced deep learning detectors, i.e., faster regions-convolutional neural network (Faster R-CNN), Cascade R-CNN, single shot multibox detector (SSD), and RetinaNet, to verify its effectiveness. Experimental results on the open SAR ship detection data set (SSDD) reveal that BSLM can greatly improve detection accuracy, especially for more complex inshore scenes.
Effect of cholesterol vs. ergosterol on DPPC bilayer properties: insights from atomistic simulations

Cholesterol and ergosterol are two dominant sterols in the membranes of eukaryotic and yeast cells, respectively. Although their chemical structure is very similar, their impact on the structure and dynamics of membranes differs. In this work, we have explored the effect of these two sterols on binary mixtures with 1,2-dipalnitoyl-sn-glycerol-3-phosphocholine (DPPC) lipid bilayer at various sterol concentration and temperatures, employing molecular dynamics simulations. The simulations revealed that cholesterol has a stronger impact on the ordering of the lipid chains and leads to more condensed membranes with respect to ergosterol. This difference likely arises from a more planar structure of the ring part as well as the better alignment of cholesterol among the DPPC chains with respect to ergosterol. The degree of the planarity of the ring system affects the orientation of the methyl groups on the rough side and distribute the lipid chains on the two sides of the sterols differently. Similar to the structural observations, cholesterol also has a stronger influence on the dynamics, and consistently, establishes stronger DPPC-sterol interactions when compared to ergosterol. Although our findings are consistent with some previous simulations as well as recent experiments, they are at odds with some other studies. Therefore, the presented results may shed new lights on the impact of sterols on the saturated lipids bilayers with implications for binary mixtures of lipids as well as lipid rafts. Significance Cholesterol and ergosterol are crucial lipid molecules of eukaryotic and prokaryotic cells, respectively, with an important role for the characteristics of the membranes. Surprisingly, many experimental studies have reported opposing results concerning their relative impact. Our work aims to understand the molecular mechanism behind the influence of these sterols on the properties of saturated DPPC chains via a systematic computational approach at atomic resolution. The results show that cholesterol has a higher impact on the ordering, condensing and dynamics of the lipid chains and closely interact with them due to its more planar structure as compared to ergosterol. These effects can have implications in lipid rafts and the interaction of therapeutic drugs with membranes.
An open innovation and its role in global fish and seafood value chains: beyond the conventional wisdom

Fish and seafood is one of the fastest growing segments in the food market. Value chain management aims at linking, assisting and guiding different actors along the value chain, such as fishermen (producers), processors and retailers up to the final consumer, to capture the best value at all stages of production, processing, marketing and consumption while at the same time observing a sustainable use of fishery resources. Open innovation depends on the external environment and bring ideas and solutions into the company. The current research aims at highlighting the relevance of open innovation for global fishery value chains, especially possibilities for value chain actors to participate in innovation processes and explore possible co-innovation avenues. The present study identifies potential open innovation avenues. DOI: http://dx.doi. org/10.4038/jas.v8i3. 6093 The Journal of Agricultural Sciences, 2013, vol.8, no3 p. 161-173
McKay graphs for alternating and classical groups

Let $G$ be a finite group, and $\alpha$ a nontrivial character of $G$. The McKay graph $\mathcal{M}(G,\alpha)$ has the irreducible characters of $G$ as vertices, with an edge from $\chi_1$ to $\chi_2$ if $\chi_2$ is a constituent of $\alpha\chi_1$. We study the diameters of McKay graphs for finite simple groups $G$. For alternating groups, we prove a conjecture made in [LST]: there is an absolute constant $C$ such that $\hbox{diam}\,{\mathcal M}(G,\alpha) \le C\frac{\log |\mathsf{A}_n|}{\log \alpha(1)}$ for all nontrivial irreducible characters $\alpha$ of $\mathsf{A}_n$. Also for classsical groups of symplectic or orthogonal type of rank $r$, we establish a linear upper bound $Cr$ on the diameters of all nontrivial McKay graphs.
Column-Randomized Linear Programs: Performance Guarantees and Applications

Solving Large-Scale Linear Programs by Randomly Sampling Columns Large-scale linear programs (LPs) play a pivotal role in various applications, including the classic cutting-stock problem and the vehicle routing problem. The standard solution approach for these LPs, namely, column generation (CG), is often computationally intractable because of the NP-hard nature of the corresponding subproblem in many applications. In “Column-Randomized Linear Programs: Performance Guarantees and Applications,” Akchen and Mišić introduce a randomized method that involves first sampling a set of columns from the original LP and subsequently solving an LP composed of the sampled columns, termed the column-randomized LP. The authors analyze the optimality gap of the column-randomized LPs and establish conditions under which the gap is small. Empirical findings demonstrate the effectiveness of the column-randomized LP approach, showcasing its advantages over the CG approach in two practical applications: the cutting-stock problem and nonparametric choice model estimation.
Balanced Meta-Softmax for Long-Tailed Visual Recognition

Deep classifiers have achieved great success in visual recognition. However, real-world data is long-tailed by nature, leading to the mismatch between training and testing distributions. In this paper, we show that Softmax function, though used in most classification tasks, gives a biased gradient estimation under the long-tailed setup. This paper presents Balanced Softmax, an elegant unbiased extension of Softmax, to accommodate the label distribution shift between training and testing. Theoretically, we derive the generalization bound for multiclass Softmax regression and show our loss minimizes the bound. In addition, we introduce Balanced Meta-Softmax, applying a complementary Meta Sampler to estimate the optimal class sample rate and further improve long-tailed learning. In our experiments, we demonstrate that Balanced Meta-Softmax outperforms state-of-the-art long-tailed classification solutions on both visual recognition and instance segmentation tasks.
The failure of testing for cosmic opacity via the distance-duality relation

The distance-duality relation (DDR) between the luminosity distance $D_L$ and the angular diameter distance $D_A$ is viewed as a powerful tool for testing for the opacity of the Universe, being independent of any cosmological model. It was applied by many authors, who mostly confirm its validity and report a negligible opacity of the Universe. Nevertheless, a thorough analysis reveals that applying the DDR in cosmic opacity tests is tricky. Its applicability is strongly limited because of a non-unique interpretation of the $D_L$ data in terms of cosmic opacity and a rather low accuracy and deficient extent of currently available $D_A$ data. Moreover, authors usually assume that cosmic opacity is frequency independent and parametrize it in their tests by a prescribed phenomenological function. In this way, they only prove that cosmic opacity does not follow their assumptions. As a consequence, no convincing evidence of transparency of the universe using the DDR has so far been presented.
Evaluation of the Neonates Born Through Meconium Stained Amniotic Fluid: A Single-Center Experience

Amaç: Mekonyumla boyanmış amniotik sıvı (MSAF) obstetrik bir risk ile ilişkili olduğu ve term, preterm ve postterm yenidoğanlarda advers neonatal sonuçların riskini önemli ölçüde arttırdığı bildirilmiştir. Bu çalışmada mekonyumlu lezyonlu amniyon sıvısı ile doğan yenidoğanları değerlendirmeyi amaçladık. Gereç ve Yöntemler: Tüm canlı doğanlarda (n=2866) doğumhane verileri ve mekonyumlu lezyonlu amniotik sıvı yenidoğanların klinik verileri. Bulgular: Canlı doğan yenidoğanların %10’u mekonyum lekeli amniyon sıvısı (n=288) ile komplike idi. Mekonyum boyalı yenidoğanlarda, gebelik yaşı, doğum ağırlığı, boy, baş çevresi, kadın cinsiyet, aktif resüsitasyon, yenidoğan yoğun bakım ünitesine kabul ve istatistiksel olarak daha yüksekti ve Apgar skorları mekonyumdan doğan yenidoğanlara göre daha düşüktü. Mekonyum lekeli amniyon sıvısı ile doğan yenidoğanlar tekil gebeliklerden alındı (p=0,007). Regresyon analizi, kadın cinsiyet ve doğum öyküsünün mekonyum varlığını etkileyebileceğini göstermiştir. Sonuç: Çalışmanın sonuçları, trakeal emilimin, MSAF ile doğan yenidoğanlar için mekonyum aspirasyon sendromu önlemek için tam olmadığını gösterdi. Vokal kordonun altındaki mekonyum, tüm MSAF yenidoğanlarında mevcut değildi. MSAF yoluyla doğan yenidoğanlarda kadın cinsiyeti ve doğum yüksekliği olabilir. Anahtar kelimeler: Mekonyumlu lekeli amniyon sıvısı, mekonyum aspirasyon sendromu, yenidoğan ÖZ ABSTRACT Objective: Meconium stained amniotic fluid (MSAF) has been reported to be associated with an obstetric hazard and significantly increase risks of adverse neonatal outcomes at term, preterm and postterm neonates. We aimed to evaluate the neonates born through meconium stained amniotic fluid in this study. Materials and Methods: Delivery room data of the all liveborn neonates (n=2866) and clinical data of the meconium stained amniotic fluid neonates. Results: Ten percent of liveborn neonates were complicated by meconium stained amniotic fluid (n=288). Gestational age, birth weight, height, head circumference, female gender, active resuscitation, admission to the neonatal intensive care unit were statistically higher, and Apgar scores at 1 minute were lower in meconium stained neonates than the neonates born without meconium. Neonates born through meconium stained amniotic fluid were delivered from singleton pregnancies (p=0.007). Regression analysis showed that female sex and birth length were might affect the presence of meconium. Conclusion: This study results were shown that tracheal suctioning is not full prevent to meconium aspiration syndrome for who neonates born through MSAF. Meconium below to vocal cord was not present all MSAF neonates. Female gender and birth height may be related for neonates with born through MSAF.
Solitary magnetostrophic Rossby waves in spherical shells

Abstract Finite-amplitude hydromagnetic Rossby waves in the magnetostrophic regime are studied. We consider the slow mode, which travels in the opposite direction to the hydrodynamic or fast mode, in the presence of a toroidal magnetic field and zonal flow by means of quasi-geostrophic models for thick spherical shells. The weakly nonlinear long waves are derived asymptotically using a reductive perturbation method. The problem at the first order is found to obey a second-order ordinary differential equation, leading to a hypergeometric equation for a Malkus field and a confluent Heun equation for an electrical wire field, and is non-singular when the wave speed approaches the mean flow. Investigating its neutral non-singular eigensolutions for different basic states, we find the evolution is described by the Korteweg–de Vries equation. This implies that the nonlinear slow wave forms solitons and solitary waves. These may take the form of a coherent eddy, such as a single anticyclone. We speculate on the relation of the anticyclone to the asymmetric gyre seen in the Earth's fluid core, and in state-of-the-art dynamo direct numerical simulations.
Randomized Automatic Differentiation

The successes of deep learning, variational inference, and many other fields have been aided by specialized implementations of reverse-mode automatic differentiation (AD) to compute gradients of mega-dimensional objectives. The AD techniques underlying these tools were designed to compute exact gradients to numerical precision, but modern machine learning models are almost always trained with stochastic gradient descent. Why spend computation and memory on exact (minibatch) gradients only to use them for stochastic optimization? We develop a general framework and approach for randomized automatic differentiation (RAD), which allows unbiased gradient estimates to be computed with reduced memory in return for variance. We examine limitations of the general approach, and argue that we must leverage problem specific structure to realize benefits. We develop RAD techniques for a variety of simple neural network architectures, and show that for a fixed memory budget, RAD converges in fewer iterations than using a small batch size for feedforward networks, and in a similar number for recurrent networks. We also show that RAD can be applied to scientific computing, and use it to develop a low-memory stochastic gradient method for optimizing the control parameters of a linear reaction-diffusion PDE representing a fission reactor.
Surface alterations of erythrocytes in Plasmodium falciparum malaria. Antigenic variation, antigenic diversity, and the role of the spleen

The surface of erythrocytes infected with late developmental stages of Plasmodium falciparum is profoundly altered and new antigenic determinants can be detected by surface immunofluorescence using immune squirrel monkey serum. The expression of these parasite-specific antigenic determinants on the surface of the host erythrocyte can be modulated by the presence or absence of the spleen and by immune pressure. An antigenic switch occurred when a cloned population of the Ugandan Palo Alto strain of P. falciparum was transferred from a splenectomized into an intact monkey and this switch was reversible. In another strain (Indochina-1), we showed that the parasites isolated during secondary and recrudescent peaks expressed erythrocyte- associated surface antigens different from the parasites isolated during the primary infection; six variant antigenic types distinct from the original population were isolated in this way. The passive transfer of immune serum can induce antigenic variation and this can occur in a cloned parasite. The various mechanisms of antigenic variation in P. falciparum are discussed in the context of strain-specific diversity and the role of antigenic diversity in acquired immunity.
Neural Mesh Flow: 3D Manifold Mesh Generationvia Diffeomorphic Flows

Meshes are important representations of physical 3D entities in the virtual world. Applications like rendering, simulations and 3D printing require meshes to be manifold so that they can interact with the world like the real objects they represent. Prior methods generate meshes with great geometric accuracy but poor manifoldness. In this work, we propose Neural Mesh Flow (NMF) to generate two-manifold meshes for genus-0 shapes. Specifically, NMF is a shape auto-encoder consisting of several Neural Ordinary Differential Equation (NODE)[1]blocks that learn accurate mesh geometry by progressively deforming a spherical mesh. Training NMF is simpler compared to state-of-the-art methods since it does not require any explicit mesh-based regularization. Our experiments demonstrate that NMF facilitates several applications such as single-view mesh reconstruction, global shape parameterization, texture mapping, shape deformation and correspondence. Importantly, we demonstrate that manifold meshes generated using NMF are better-suited for physically-based rendering and simulation. Code and data will be released.
Symmetry Classes of Open Fermionic Quantum Matter

We present a full symmetry classification of dissipatively evolving fermion matter. Our approach is bottom up, starting from the ten different classes of linear and anti-linear state transformations in fermionic Fock spaces. Both in unitary quantum mechanics and in the `micro-reversible' settings of thermal equilibrium, the anti-linear transformations in conjunction with an explicit inversion of physical time represent time reversal. However, out of equilibrium time reversal becomes meaningless, while the anti-linear transformations in Fock space remain significant and now must be considered in autonomy. Their different implementation and its consequences for the understanding of fermion symmetries out of equilibrium is the central motif of our study. Following the logic of the bottom up approach, we address the question under which conditions the irreversible evolution equations describing the quantum dynamics of fermion matter remain invariant under the application of Fock space transformations. This defines ten symmetry classes, separately for the generators of reversible dynamics, dissipation and fluctuations featuring in these equations. We discuss their physical manifestation both for interacting systems in the second quantized Fock space formalism, and for the evolution of free fermion Gaussian states of matter, where the language of non-Hermitian matrices is used. Emphasis will be put on the stationary phases forming as end points of dissipative evolution, and in particular on the ensuing symmetry protected topological phases of dissipative matter. The starting point of ten Fock space symmetries implies a transparent classification of these phases and defines non-equilibrium blueprints for their realization from first principles, as we will demonstrate on the exemplary case study of an interacting lattice model.
Turbulence Sets the Length Scale for Planetesimal Formation: Local 2D Simulations of Streaming Instability and Planetesimal Formation

The trans-Neptunian object 2014 MU69, named Arrokoth, is the most recent evidence that planetesimals did not form by successive collisions of smaller objects, but by the direct gravitational collapse of a pebble cloud. But what process sets the physical scales on which this collapse may occur? Star formation has the Jeans mass, that is, when gravity is stronger than thermal pressure, helping us to understand the mass of our Sun. But what controls mass and size in the case of planetesimal formation? Both asteroids and Kuiper Belt objects show a kink in their size distribution at 100 km. Here we derive a gravitational collapse criterion for a pebble cloud to fragment to planetesimals, showing that a critical mass is needed for the clump to overcome turbulent diffusion. We successfully tested the validity of this criterion in direct numerical simulations of planetesimal formation triggered by the streaming instability. Our result can therefore explain the sizes for planetesimals found forming in streaming instability simulations in the literature, while not addressing the detailed size distribution. We find that the observed characteristic diameter of ∼100 km corresponds to the critical mass of a pebble cloud set by the strength of turbulent diffusion stemming from streaming instability for a wide region of a solar nebula model from 2 to 60 au, with a tendency to allow for smaller objects at distances beyond and at late times, when the nebula gas gets depleted.