Miracle-dz/UniFAR-FTUs huggingface dataset:
Elastic-Degenerate String Matching via Fast Matrix Multiplication

An elastic-degenerate (ED) string is a sequence of $n$ sets of strings of total length $N$, which was recently proposed to model a set of similar sequences. The ED string matching (EDSM) problem is to find all occurrences of a pattern of length $m$ in an ED text. An $O(nm^{1.5}\sqrt{\log m}+N)$-time algorithm for EDSM is known [Aoyama et al., CPM 2018]. The standard assumption in the prior work on this question is that $N$ is substantially larger than both $n$ and $m$, and thus we would like to have a linear dependency on the former. Under this assumption, the natural open problem is whether we can decrease the 1.5 exponent in the time complexity, similarly as in the related (but, to the best of our knowledge, not equivalent) word break problem [Backurs and Indyk, FOCS 2016]. Our starting point is a conditional lower bound for EDSM. We use the popular combinatorial Boolean Matrix Multiplication (BMM) conjecture stating that there is no truly subcubic combinatorial algorithm for BMM [Abboud and Williams, FOCS 2014]. By designing an appropriate reduction we show that a combinatorial algorithm solving the EDSM problem in $O(nm^{1.5-e}+N)$ time, for any $e>0$, refutes this conjecture. Our reduction should be understood as an indication that decreasing the exponent requires fast matrix multiplication. String periodicity and fast Fourier transform are two standard tools in string algorithms. Our main technical contribution is that we successfully combine these tools with fast matrix multiplication to design a non-combinatorial $\tilde{O}(nm^{\omega-1}+N)$-time algorithm for EDSM, where $\omega$ denotes the matrix multiplication exponent. To the best of our knowledge, we are the first to combine these tools. In particular, using the fact that $\omega<2.373$ [Le Gall, ISSAC 2014; Williams, STOC 2012], we obtain an $O(nm^{1.373}+N)$-time algorithm for EDSM.
CrossTrainer: Practical Domain Adaptation with Loss Reweighting

Domain adaptation provides a powerful set of model training techniques given domain-specific training data and supplemental data with unknown relevance. The techniques are useful when users need to develop models with data from varying sources, of varying quality, or from different time ranges. We build CrossTrainer, a system for practical domain adaptation. CrossTrainer utilizes loss reweighting from [6], which provides consistently high model accuracy across a variety of datasets in our empirical analysis. However, loss reweighting is sensitive to the choice of a weight hyperparameter that is expensive to tune. We develop optimizations leveraging unique properties of loss reweighting that allow CrossTrainer to output accurate models while improving training time compared to naïve hyperparameter search.
Bayesian Optimization for Multi-objective Optimization and Multi-point Search

Bayesian optimization is an effective method to efficiently optimize unknown objective functions with high evaluation costs. Traditional Bayesian optimization algorithms select one point per iteration for single objective function, whereas in recent years, Bayesian optimization for multi-objective optimization or multi-point search per iteration have been proposed. However, Bayesian optimization that can deal with them at the same time in non-heuristic way is not known at present. We propose a Bayesian optimization algorithm that can deal with multi-objective optimization and multi-point search at the same time. First, we define an acquisition function that considers both multi-objective and multi-point search problems. It is difficult to analytically maximize the acquisition function as the computational cost is prohibitive even when approximate calculations such as sampling approximation are performed; therefore, we propose an accurate and computationally efficient method for estimating gradient of the acquisition function, and develop an algorithm for Bayesian optimization with multi-objective and multi-point search. It is shown via numerical experiments that the performance of the proposed method is comparable or superior to those of heuristic methods.
FCC-GAN: A Fully Connected and Convolutional Net Architecture for GANs

Generative Adversarial Networks (GANs) are a powerful class of generative models. Despite their successes, the most appropriate choice of a GAN network architecture is still not well understood. GAN models for image synthesis have adopted a deep convolutional network architecture, which eliminates or minimizes the use of fully connected and pooling layers in favor of convolution layers in the generator and discriminator of GANs. In this paper, we demonstrate that a convolution network architecture utilizing deep fully connected layers and pooling layers can be more effective than the traditional convolution-only architecture, and we propose FCC-GAN, a fully connected and convolutional GAN architecture. Models based on our FCC-GAN architecture learn both faster than the conventional architecture and also generate higher quality of samples. We demonstrate the effectiveness and stability of our approach across four popular image datasets.
EDVR: Video Restoration With Enhanced Deformable Convolutional Networks

Video restoration tasks, including super-resolution, deblurring, etc, are drawing increasing attention in the computer vision community. A challenging benchmark named REDS is released in the NTIRE19 Challenge. This new benchmark challenges existing methods from two aspects: (1) how to align multiple frames given large motions, and (2) how to effectively fuse different frames with diverse motion and blur. In this work, we propose a novel Video Restoration framework with Enhanced Deformable convolutions, termed EDVR, to address these challenges. First, to handle large motions, we devise a Pyramid, Cascading and Deformable (PCD) alignment module, in which frame alignment is done at the feature level using deformable convolutions in a coarse-to-fine manner. Second, we propose a Temporal and Spatial Attention (TSA) fusion module, in which attention is applied both temporally and spatially, so as to emphasize important features for subsequent restoration. Thanks to these modules, our EDVR wins the champions and outperforms the second place by a large margin in all four tracks in the NTIRE19 video restoration and enhancement challenges. EDVR also demonstrates superior performance to state-of-the-art published methods on video super-resolution and deblurring. The code is available at https://github.com/xinntao/EDVR.
Early local differentiation of the cell wall matrix defines the contact sites in lobed mesophyll cells of Zea mays.

BACKGROUND AND AIMS The morphogenesis of lobed mesophyll cells (MCs) is highly controlled and coupled with intercellular space formation. Cortical microtubule rings define the number and the position of MC isthmi. This work investigated early events of MC morphogenesis, especially the mechanism defining the position of contacts between MCs. The distributions of plasmodesmata, the hemicelluloses callose and (1 → 3,1 → 4)-β-d-glucans (MLGs) and the pectin epitopes recognized by the 2F4, JIM5, JIM7 and LM6 antibodies were studied in the cell walls of Zea mays MCs. METHODS Matrix cell wall polysaccharides were immunolocalized in hand-made sections and in sections of material embedded in LR White resin. Callose was also localized using aniline blue in hand-made sections. Plasmodesmata distribution was examined by transmission electron microscopy. RESULTS Before reorganization of the dispersed cortical microtubules into microtubule rings, particular bands of the longitudinal MC walls, where the MC contacts will form, locally differentiate by selective (1) deposition of callose and the pectin epitopes recognized by the 2F4, LM6, JIM5 and JIM7 antibodies, (2) degradation of MLGs and (3) formation of secondary plasmodesmata clusterings. This cell wall matrix differentiation persists in cell contacts of mature MCs. Simultaneously, the wall bands between those of future cell contacts differentiate with (1) deposition of local cell wall thickenings including cellulose microfibrils, (2) preferential presence of MLGs, (3) absence of callose and (4) transient presence of the pectins identified by the JIM5 and JIM7 antibodies. The wall areas between cell contacts expand determinately to form the cell isthmi and the cell lobes. CONCLUSIONS The morphogenesis of lobed MCs is characterized by the early patterned differentiation of two distinct cell wall subdomains, defining the sites of the future MC contacts and of the future MC isthmi respectively. This patterned cell wall differentiation precedes cortical microtubule reorganization and may define microtubule ring disposition.
High Frequency Residual Learning for Multi-Scale Image Classification

We present a novel high frequency residual learning framework, which leads to a highly efficient multi-scale network (MSNet) architecture for mobile and embedded vision problems. The architecture utilizes two networks: a low resolution network to efficiently approximate low frequency components and a high resolution network to learn high frequency residuals by reusing the upsampled low resolution features. With a classifier calibration module, MSNet can dynamically allocate computation resources during inference to achieve a better speed and accuracy trade-off. We evaluate our methods on the challenging ImageNet-1k dataset and observe consistent improvements over different base networks. On ResNet-18 and MobileNet with alpha=1.0, MSNet gains 1.5% accuracy over both architectures without increasing computations. On the more efficient MobileNet with alpha=0.25, our method gains 3.8% accuracy with the same amount of computations.
On corner scattering for operators of divergence form and applications to inverse scattering

Abstract We consider the scattering problem governed by the Helmholtz equation with inhomogeneity in both “conductivity” in the divergence form and “potential” in the lower order term. The support of the inhomogeneity is assumed to contain a convex corner. We prove that, due to the presence of such corner under appropriate assumptions on the potential and conductivity in the vicinity of the corner, any incident field scatters. Based on corner scattering analysis we present a uniqueness result on determination of the polygonal convex hull of the support of admissible inhomogeneities, from scattering data corresponding to one single incident wave. These results require only certain regularity around the corner for the coefficients modeling the inhomogeneity, whereas away from the corner they can be quite general. Our main results on scattering and inverse scattering are established for while some analytic tools are developed in any dimension
Exponential Separations Between Turnstile Streaming and Linear Sketching

Almost every known turnstile streaming algorithm is implementable as a linear sketch. Is this necessarily true, or can there exist turnstile streaming algorithms that use much less space than any linear sketch? It was shown in~\cite{LNW14} that, if a turnstile algorithm works for arbitrarily long streams with arbitrarily large coordinates at intermediate stages of the stream, then the turnstile algorithm can be implemented as a linear sketch. Our results have the opposite form: if either the stream length or the maximum value of the stream are substantially restricted, there exist problems where linear sketching is exponentially harder than turnstile streaming.
Metallic and insulating stripes and their relation with superconductivity in the doped Hubbard model

The dualism between superconductivity and charge/spin modulations (the so-called stripes) dominates the phase diagram of many strongly-correlated systems. A prominent example is given by the Hubbard model, where these phases compete and possibly coexist in a wide regime of electron dopings for both weak and strong couplings. Here, we investigate this antagonism within a variational approach that is based upon Jastrow-Slater wave functions, including backflow correlations, which can be treated within a quantum Monte Carlo procedure. We focus on clusters having a ladder geometry with MM legs (with MM ranging from 22 to 1010) and a relatively large number of rungs, thus allowing us a detailed analysis in terms of the stripe length. We find that stripe order with periodicity \lambda=8λ=8 in the charge and 2\lambda=162λ=16 in the spin can be stabilized at doping \delta=1/8δ=1/8. Here, there are no sizable superconducting correlations and the ground state has an insulating character. A similar situation, with \lambda=6λ=6, appears at \delta=1/6δ=1/6. Instead, for smaller values of dopings, stripes can be still stabilized, but they are weakly metallic at \delta=1/12δ=1/12 and metallic with strong superconducting correlations at \delta=1/10δ=1/10, as well as for intermediate (incommensurate) dopings. Remarkably, we observe that spin modulation plays a major role in stripe formation, since it is crucial to obtain a stable striped state upon optimization. The relevance of our calculations for previous density-matrix renormalization group results and for the two-dimensional case is also discussed.
English-Bhojpuri SMT System: Insights from the Karaka Model

This thesis has been divided into six chapters namely: Introduction, Karaka Model and it impacts on Dependency Parsing, LT Resources for Bhojpuri, English-Bhojpuri SMT System: Experiment, Evaluation of EB-SMT System, and Conclusion. Chapter one introduces this PhD research by detailing the motivation of the study, the methodology used for the study and the literature review of the existing MT related work in Indian Languages. Chapter two talks of the theoretical background of Karaka and Karaka model. Along with this, it talks about previous related work. It also discusses the impacts of the Karaka model in NLP and dependency parsing. It compares Karaka dependency and Universal Dependency. It also presents a brief idea of the implementation of these models in the SMT system for English-Bhojpuri language pair.
Trinity of Pixel Enhancement: a Joint Solution for Demosaicking, Denoising and Super-Resolution

Demosaicing, denoising and super-resolution (SR) are of practical importance in digital image processing and have been studied independently in the passed decades. Despite the recent improvement of learning-based image processing methods in image quality, there lacks enough analysis into their interactions and characteristics under a realistic setting of the mixture problem of demosaicing, denoising and SR. In existing solutions, these tasks are simply combined to obtain a high-resolution image from a low-resolution raw mosaic image, resulting in a performance drop of the final image quality. In this paper, we first rethink the mixture problem from a holistic perspective and then propose the Trinity Enhancement Network (TENet), a specially designed learning-based method for the mixture problem, which adopts a novel image processing pipeline order and a joint learning strategy. In order to obtain the correct color sampling for training, we also contribute a new dataset namely PixelShift200, which consists of high-quality full color sampled real-world images using the advanced pixel shift technique. Experiments demonstrate that our TENet is superior to existing solutions in both quantitative and qualitative perspective. Our experiments also show the necessity of the proposed PixelShift200 dataset.
Distilling the Essence of an Evolutionary Process and Implications for a Formal Description of Culture

It has been proposed that, since the origin of life and the ensuing evolution of biological species, a second evolutionary process has appeared on our planet. It is the evolution of culture-e.g., ideas, beliefs, and artifacts. Does culture evolve in the same genuine sense as biological life? And if so, does it evolve through natural selection, or by some other means? Why does no other species remotely approach the degree of cultural complexity of humans? These questions lie at the foundation of who we are and what makes our lives meaningful. Although much research has been done on how selective pressures operating at the biological level affect cognition and culture, little research has focused on culture as an evolutionary process in its own right. Like biological forms, cultural forms-ideas, attitudes, artifacts, mannerisms, etc.-incrementally adapt to the constraints and affordances of their environment through descent with modification. In some respects culture appears to be Darwinian, i.e., a process of differential replication and selection amongst randomly generated variants. This suggests that knowledge of biological evolution can be put to use to gain insight into culture. However, attempts to apply Darwinian theory to culture have not yielded the kind of unifying framework for the social sciences that it provided for the biological sciences, largely because of the nonrandom manner in which the mind-the hub of cultural change-generates and assimilates novelty. This paper investigates how and when humans became capable of supporting culture, and what previously held it back, focusing on how we attained the creative powers we now possess. To invent in the strategic, intuitive manner characteristic of humans requires a cognitive architecture that supports the capacity to spontaneously adapt concepts to new circumstances and merge them together to conceptualize new situations.
Joint Sparsity in SAR Tomography for Urban Mapping

With meter-resolution images delivered by modern synthetic aperture radar (SAR) satellites satellites like TerraSAR-X and TanDEM-X, it is now possible to map urban areas from space in very high level of detail using advanced interferometric techniques such as persistent scatterer interferometry and tomographic SAR inversion (TomoSAR), whereas these multi-pass techniques are based on a great number of images. We aim at precise TomoSAR reconstruction while significantly reducing the required number of images by incorporating building a priori knowledge to the estimation. In the paper, we propose a novel workflow that marries the freely available geographic information systems (GIS) data (i.e., 2-D building footprints) and the joint sparsity concept for TomoSAR inversion. Experiments on bistatic TanDEM-X data stacks demonstrate the great potential of the proposed approach, e.g., highly accurate tomographic reconstruction is achieved using six interferograms only.
The Small GTPase Arf6: An Overview of Its Mechanisms of Action and of Its Role in Host–Pathogen Interactions and Innate Immunity

The small GTase Arf6 has several important functions in intracellular vesicular trafficking and regulates the recycling of different types of cargo internalized via clathrin-dependent or -independent endocytosis. It activates the lipid modifying enzymes PIP 5-kinase and phospholipase D, promotes actin polymerization, and affects several functionally distinct processes in the cell. Arf6 is used for the phagocytosis of pathogens and can be directly or indirectly targeted by various pathogens to block phagocytosis or induce the uptake of intracellular pathogens. Arf6 is also used in the signaling of Toll-like receptors and in the activation of NADPH oxidases. In this review, we first give an overview of the different roles and mechanisms of action of Arf6 and then focus on its role in innate immunity and host–pathogen interactions.
Facing the Challenge of Acute Autoimmune Liver Disease: Report of a Case and Review of the Literature

Autoimmune liver diseases (ALD) comprise two broad categories: those with a hepatic predominance, autoimmune hepatitis (AIH), and those with a predominance of cholestatic features including primary biliary cirrhosis (PBC) and primary sclerosing cholangitis (PSC). An overlap syndrome (OS) is thought to exist where a patient appears with clinical features of more than one immune-mediated liver disease. Based on one personal observation we report a case of histologically proven OS. The relevant literature is briefly reviewed. A 68-year-old male was admitted in our department with general malaise, anorexia and jaundice. Trans-abdominal ultrasonography as well as computerized tomography showed a slightly enlarged liver with mild fatty deposition and the patient was submitted to transcutaneous liver biopsy. Histological features included inflammatory infiltration of intra-hepatic bile ducts with the presence of endoepithelial lymphocytes and plasma cells, degenerative distortions and even focal damage of relevant epithelial cellular structures, periductal fibrosis, portal inflammation, interface hepatitis, ductular proliferation and canalicular cholestasis. It is apparent that exact definitions pertaining the OS are limited and general consensus on the precise determinations of overlap/variant syndromes is warranted in order to enable more accurate future research and establish common guidelines in the management of the condition.
3D object search through semantic component

In this paper, we present a novel concept named semantic component for 3D object search which describes a key component that semantically defines a 3D object. In most cases, the semantic component is intra-category stable and therefore can be used to construct an efficient 3D object retrieval scheme. By segmenting an object into segments and learning the similar segments shared by all the objects in the same category, we can summarise what human uses for object recognition, from the analysis of which we develop a method to find the semantic component of an object. In our experiments, the proposed method is justified and the effectiveness of our algorithm is also demonstrated.
Studies on the distribution of heavy metal concentr ations in River Adyar, Chennai, Tamil Nadu

To assess the levels of heavy metals and the extent of pollution in the surface water and sediments fr om the River Adyar, Chennai, the present study has been undertak en. Water and sediment samples were collected from four different stations along the course of the river in Chennai. The concentrations of heavy metals such a s Zinc (Zn), Cadmium (Cd), Copper (Cu), Iron (Fe) and Lead (Pb), were determined using Atomic Absorption Spectrophotometer. The ranges of heavy metals in wa ter (µg L -1 ) and sediment (µg g -1 ) Zn, Cd, Cu, Fe and Pb were: 0.24-0.40; 0.23-0.36; 0.61-0.83; 4.83-5.00; 0.02-0. 05; and 0.79-0.83; 0.72-0.73; 0.64-1.24; 11.91-12.8 ; and 0.300. 42, in Ekatuthangal (Station-I), Guindy (Station- II), Saidapet (Station-III) and Kotturpuram (Statio n-IV) respectively. The heavy metal concentrations in the downstream indicated an increase in the pollution load due to movement of suspended and dissolved industrial effl uents and anthropogenic wastes. Except Cadmium and Iron all the heavy metals studied were under permissible lim its.