Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of POS tagger for English-Bengali Code-Mixed data

Code-mixed texts are widespread nowadays due to the advent of social media. Since these texts combine two languages to formulate a sentence, it gives rise to various research problems related to Natural Language Processing. In this paper, we try to excavate one such problem, namely, Parts of Speech tagging of code-mixed texts. We have built a system that can POS tag English-Bengali code-mixed data where the Bengali words were written in Roman script. Our approach initially involves the collection and cleaning of English-Bengali code-mixed tweets. These tweets were used as a development dataset for building our system. The proposed system is a modular approach that starts by tagging individual tokens with their respective languages and then passes them to different POS taggers, designed for different languages (English and Bengali, in our case). Tags given by the two systems are later joined together and the final result is then mapped to a universal POS tag set. Our system was checked using 100 manually POS tagged code-mixed sentences and it returned an accuracy of 75.29%.
Cellular $\mathbb A^1$-homology and the motivic version of Matsumoto's theorem

We define a new version of $\mathbb A^1$-homology, called cellular $\mathbb A^1$-homology, for smooth schemes over a field that admit an increasing filtration by open subschemes with cohomologically trivial closed strata. We provide several explicit computations of cellular $\mathbb A^1$-homology and use them to determine the $\mathbb A^1$-fundamental group of a split reductive group over an arbitrary field, thereby obtaining the motivic version of Matsumoto's theorem on universal central extensions of split, semisimple, simply connected algebraic groups. As applications, we uniformly explain and generalize results due to Brylinski-Deligne and Esnault-Kahn-Levine-Viehweg, determine the isomorphism classes of central extensions of such an algebraic group by an arbitrary strictly $\mathbb A^1$-invariant sheaf and also reprove classical results of E. Cartan on homotopy groups of complex Lie groups.
Force myography benchmark data for hand gesture recognition and transfer learning

Force myography has recently gained increasing attention for hand gesture recognition tasks. However, there is a lack of publicly available benchmark data, with most existing studies collecting their own data often with custom hardware and for varying sets of gestures. This limits the ability to compare various algorithms, as well as the possibility for research to be done without first needing to collect data oneself. We contribute to the advancement of this field by making accessible a benchmark dataset collected using a commercially available sensor setup from 20 persons covering 18 unique gestures, in the hope of allowing further comparison of results as well as easier entry into this field of research. We illustrate one use-case for such data, showing how we can improve gesture recognition accuracy by utilising transfer learning to incorporate data from multiple other persons. This also illustrates that the dataset can serve as a benchmark dataset to facilitate research on transfer learning algorithms.
Ultraviolet radiation prevents bleaching in the mediterranean coral Oculina patagonica

The causative agent of bleaching in the coral Oculina patagonica in the Mediterranean Sea is Vibrio shiloi. The bacterium becomes more virulent with increasing seawater temperature, resulting in extensive coral bleaching (80 to 90%) every summer in 1 to 6 m depth. However, colonies in shallow water (0 to 80 cm depth) showed negligible bleaching, despite being exposed to higher temperatures (ca 2°C) than the deeper water corals. Fragments transplanted from 4 m depth to a shal- low reef flat ( 2 × 10 4 cm -2 ) in bleached fragments at 4 m, whereas in non-bleached fragments from the reef flat, V. shiloi could not be detected. When O. patagonica was infected with V. shiloi in laboratory aquaria and subsequently exposed to sunlight, the intracellular bacteria were rapidly killed, aborting the infection and preventing bleaching. How- ever, when the infected corals were protected from ultraviolet (UV) light, the intracellular V. shiloi multiplied and the coral bleached. We present here for the first time evidence for prevention of coral bacterial bleaching by UV radiation (UVR).
Spinning up a time machine

We present a global metric describing closed timelike curves embedded in Minkowski spacetime. Physically, the metric represents an Alcubierre warp drive on a rotating platform. The physical realizability of such a metric is uncertain due to the exotic matter required to produce it. Never-the-less we suggest that this metric will have applications in more rigorously studying the behavior of quantum fields interacting with closed timelike curves.
On 2-closures of rank 3 groups

A permutation group $G$ on $\Omega$ is called a rank 3 group if it has precisely three orbits in its induced action on $\Omega \times \Omega$. The largest permutation group on $\Omega$ having the same orbits as $G$ on $\Omega \times \Omega$ is called the 2-closure of $G$. A description of 2-closures of rank 3 groups is given. As a special case, it is proved that 2-closure of a primitive one-dimensional affine rank 3 permutation group of sufficiently large degree is also affine and one-dimensional.
How and Why is An Answer (Still) Correct? Maintaining Provenance in Dynamic Knowledge Graphs

Knowledge graphs (KGs), that have become the backbone of many critical knowledge-centric applications, are mostly automatically constructed based on an ensemble of extraction techniques applied over diverse data sources. It is, therefore, important to establish the provenance of results for a query to determine how these were computed. Provenance is shown to be useful for assigning confidence scores to the results, for debugging the KG generation itself, and for providing answer explanations. In many such applications, certain queries are registered as standing queries since their answers are needed often. However, KGs keep continuously changing due to reasons such as changes in the source data, improvements to the extraction techniques, refinement/enrichment of information, and so on. This raises the issue of efficiently maintaining the provenance polynomials of complex graph pattern queries for dynamic and large KGs instead of having to recompute them from scratch each time the KG is updated. Addressing this issue, we present a framework HUKA that uses provenance polynomials for tracking the derivation of query results over knowledge graphs by encoding the edges involved in generating the answer. More importantly, HUKA also maintains these provenance polynomials in the face of updates---insertions as well as deletions of facts---in the underlying KG. Experimental results over large real-world KGs such as YAGO and DBpedia with various benchmark SPARQL query workloads reveals that HUKA can be almost 50 times faster than existing systems for provenance computation on dynamic KGs.
Habitat and Temporal Variation in Diet of Great Cormorant Nestlings in Greek Colonies

Abstract Diet of Great Cormorant (Phalacrocorax carbo sinensis) nestlings was studied during four consecutive breeding seasons (1999-2002) at three Greek colonies (Axios Delta, and Lakes Kerkini and Mikri Prespa) in order to assess variation and commercial value of prey. A variety of fish taxa were found in nestlings’ regurgitates in each area and season, but only one or two dominated by numbers or biomass. Black Goby (Gobius jozo), Round Sardinella (Sardinella aurita) and Twaite Shad (Alosa fallax) were the most important prey in the Axios Delta; Bleak (Alburnus alburnus), Giebel (Carassius auratus gibelio) and Roach (Rutilus rutilus) at Lake Kerkini; Chalcalburnus belvica and Giebel at Lake Mikri Prespa. Nestling diet varied both seasonally (but only at Lake Kerkini significantly so) and annually (significantly in the Axios Delta and at Lake Mikri Prespa). Temporal changes can be attributed to changes in prey availability and abundance and confirm this bird’s opportunistic behavior. Between-colonies, differences in diet were significant, probably due to differences in habitat and prey species diversity and composition. The low consumption of valuable fish prey by Great Cormorant nestlings (<10%, numbers and biomass) suggests minimal competition with human interests.
Almost exact recovery in noisy semi-supervised learning

This paper investigates noisy graph-based semi-supervised learning or community detection. We consider the Stochastic Block Model (SBM), where, in addition to the graph observation, an oracle gives a non-perfect information about some nodes' cluster assignment. We derive the Maximum A Priori (MAP) estimator, and show that a continuous relaxation of the MAP performs almost exact recovery under non-restrictive conditions on the average degree and amount of oracle noise. In particular, this method avoids some pitfalls of several graph-based semi-supervised learning methods such as the flatness of the classification functions, appearing in the problems with a very large amount of unlabeled data.
A Progressive Approach to Scalar Field Topology

This article introduces progressive algorithms for the topological analysis of scalar data. Our approach is based on a hierarchical representation of the input data and the fast identification of topologically invariant vertices, which are vertices that have no impact on the topological description of the data and for which we show that no computation is required as they are introduced in the hierarchy. This enables the definition of efficient coarse-to-fine topological algorithms, which leverage fast update mechanisms for ordinary vertices and avoid computation for the topologically invariant ones. We demonstrate our approach with two examples of topological algorithms (critical point extraction and persistence diagram computation), which generate interpretable outputs upon interruption requests and which progressively refine them otherwise. Experiments on real-life datasets illustrate that our progressive strategy, in addition to the continuous visual feedback it provides, even improves run time performance with regard to non-progressive algorithms and we describe further accelerations with shared-memory parallelism. We illustrate the utility of our approach in batch-mode and interactive setups, where it respectively enables the control of the execution time of complete topological pipelines as well as previews of the topological features found in a dataset, with progressive updates delivered within interactive times.
Meta-LR-Schedule-Net: Learned LR Schedules that Scale and Generalize

The learning rate (LR) is one of the most important hyper-parameters in stochastic gradient descent (SGD) for deep neural networks (DNNs) training and generalization. However, current hand-designed LR schedules need to manually pre-specify schedule as well as its extra hyper-parameters, which limits its ability to adapt non-convex optimization problems due to the significant variation of training dynamic. To address this issue, we propose a model capable of adaptively learning LR schedule from data. We specifically design a meta-learner with explicit mapping formulation to parameterize LR schedules, which can adjust LR adaptively to comply with current training dynamic by leveraging the information from past training histories. Image and text classification benchmark experiments substantiate the capability of our method for achieving proper LR schedules compared with baseline methods. Moreover, we transfer the learned LR schedule to other various tasks, like different training batch sizes, epochs, datasets, network architectures, especially large scale ImageNet dataset, showing its stronger generalization capability than related methods. Finally, guided by a small set of clean validation set, we show our method can achieve better generalization error when training data is biased with corrupted noise than baseline methods.
Formal Power Series on Algebraic Cryptanalysis

In cryptography, attacks that utilize a Grobner basis have broken several cryptosystems. The complexity of computing a Grobner basis dominates the overall computing and its estimation is important for such cryptanalysis. The complexity is given by using the solving degree, but it is hard to decide this value of a large scale system arisen from cryptography. Thus the degree of regularity and the first fall degree are used as proxies for the solving degree based on a wealth of experiments. If a given system is semi-regular, the complexity is estimated by using the degree of regularity derived from a certain power series, otherwise, by using the first fall degree derived from a construction of a syzygy. The degree of regularity is also defined on a non-semi-regular system and is experimentally larger than the first fall degree, but those relation is not clear theoretically. Moreover, in contrast to the degree of regularity, the first fall degree has been investigated specifically for each cryptosystem and its discussion on generic systems is not given. In this paper, we show an upper bound for the first fall degree of a polynomial system over a sufficiently large field. In detail, we prove that this upper bound for a non-semi-regular system is the degree of regularity. Moreover, we prove that the upper bound for a multi-graded polynomial system is a certain value only decided by its multi-degree. Furthermore, we show that the condition for the order of a field in our results is satisfied in attacks against actual multivariate cryptosystems. Consequently, under a reasonable condition for the order of a field, we clear a relation between the first fall degree and the degree of regularity and provide a theoretical method using a multivariate power series for cryptanalysis.
Surrogate Locally-Interpretable Models with Supervised Machine Learning Algorithms

Supervised Machine Learning (SML) algorithms, such as Gradient Boosting, Random Forest, and Neural Networks, have become popular in recent years due to their superior predictive performance over traditional statistical methods. However, their complexity makes the results hard to interpret without additional tools. There has been a lot of recent work in developing global and local diagnostics for interpreting SML models. In this paper, we propose a locally-interpretable model that takes the fitted ML response surface, partitions the predictor space using model-based regression trees, and fits interpretable main-effects models at each of the nodes. We adapt the algorithm to be efficient in dealing with high-dimensional predictors. While the main focus is on interpretability, the resulting surrogate model also has reasonably good predictive performance.
Transient Jupiter Co-orbitals from Solar System Sources

We demonstrate dynamical pathways from main-belt asteroid and Centaur orbits to those in co-orbital motion with Jupiter, including the retrograde (inclination i > 90°) state. We estimate that, at any given time, there should be ∼1 km-scale or larger escaped asteroid in a transient direct (prograde) orbit with semimajor axis near that of Jupiter’s (a ≃ aJ), with proportionally more smaller objects as determined by their size distribution. Most of these objects would be in the horseshoe dynamical state, and are hard to detect due to their moderate eccentricities (spending most of their time beyond 5 au) and longitudes relative to Jupiter being spread nearly all over the sky. We also show that ≈1% of the transient asteroid co-orbital population is on retrograde orbits with Jupiter. This population, like the recently identified asteroid (514107) 2015 BZ509, can spend millions of years with a ≃ aJ including tens or hundreds of thousands of years formally in the retrograde 1:-1 co-orbital resonance. Escaping near-Earth asteroids (NEAs) are thus likely the precursors of the handful of known high-inclination objects with a ≃ aJ. We compare the production of Jovian co-orbitals from escaping NEAs with those from incoming Centaurs. We find that temporary direct co-orbitals are likely dominated by Centaur capture, but we only find production of (temporary) retrograde Jovian co-orbitals (including very long-lived ones) from the NEA source. We postulate that the primordial elimination of the inner solar system’s planetesimal population could provide a supply route for a metastable outer solar system reservoir for the high-inclination Centaurs.
Soft X-ray emission lines in the X-ray binary Swift J1858.6–0814 observed with XMM–Newton Reflection Grating Spectrometer: disc atmosphere or wind?

We find soft X-ray emission lines from the X-ray binary Swift J1858.6-0814 in data from XMM-Newton-RGS: N VII, O VII and O VIII, as well as notable residuals short of a detection at Ne IX and other higher ionisation transitions. These could be associated with the disc atmosphere, as in accretion disc corona sources, or with a wind, as has been detected in Swift J1858.6-0814 in emission lines at optical wavelengths. Indeed, the N VII line is redshifted, consistent with being the emitting component of a P-Cygni profile. We find that the emitting plasma has an ionisation parameter $\log(\xi)=1.35\pm0.2$ and a density $n>1.5\times10^{11}$ cm$^{-3}$. From this, we infer that the emitting plasma must be within $10^{13}$ cm of the ionising source, $\sim5\times10^{7}r_{\rm g}$ for a $1. 4M_{\odot}$ neutron star, and from the line width that it is at least $10^4r_{\rm g}$ away ($2\times10^{9}(M/1.4M_{\odot})$ cm). We compare this with known classes of emission line regions in other X-ray binaries and active galactic nuclei.
Space- and Computationally-Efficient Set Reconciliation via Parity Bitmap Sketch (PBS)

Set reconciliation is a fundamental algorithmic problem that arises in many networking, system, and database applications. In this problem, two large setsAandBof objects (bitcoins, files, records, etc.) are stored respectively at two different network-connected hosts, which we name Alice and Bob respectively. Alice and Bob communicate with each other to learnAΔB, the difference betweenAandB, and as a result the reconciled setA∪B.Current set reconciliation schemes are based on either invertible Bloom filters (IBF) or error-correction codes (ECC). The former has a low computational complexity ofO(d), wheredis the cardinality ofAΔB, but has a high communication overhead that is several times larger than the theoretical minimum. The latter has a low communication overhead close to the theoretical minimum, but has a much higher computational complexity ofO(d2). In this work, we propose Parity Bitmap Sketch (PBS), an ECC-based set reconciliation scheme that gets the better of both worlds: PBS has both a low computational complexity ofO(d)just like IBF-based solutions and a low communication overhead of roughly twice the theoretical minimum. A separate contribution of this work is a novel rigorous analytical framework that can be used for the precise calculation of various performance metrics and for the near-optimal parameter tuning of PBS.
Translate the Facial Regions You Like Using Region-Wise Normalization

Though GAN (Generative Adversarial Networks) based technique has greatly advanced the performance of image synthesis and face translation, only few works available in literature provide region based style encoding and translation. We propose in this paper a region-wise normalization framework, for region level face translation. While per-region style is encoded using available approach, we build a so called RIN (region-wise normalization) block to individually inject the styles into per-region feature maps and then fuse them for following convolution and upsampling. Both shape and texture of different regions can thus be translated to various target styles. A region matching loss has also been proposed to significantly reduce the inference between regions during the translation process. Extensive experiments on three publicly available datasets, i.e. Morph, RaFD and CelebAMask-HQ, suggest that our approach demonstrate a large improvement over state-of-the-art methods like StarGAN, SEAN and FUNIT. Our approach has further advantages in precise control of the regions to be translated. As a result, region level expression changes and step by step make up can be achieved. The video demo is available at this https URL.
Video compression with low complexity CNN-based spatial resolution adaptation

It has recently been demonstrated that spatial resolution adaptation can be integrated within video compression to improve overall coding performance by spatially down-sampling before encoding and super-resolving at the decoder. Significant improvements have been reported when convolutional neural networks (CNNs) were used to perform the resolution up-sampling. However, this approach suffers from high complexity at the decoder due to the employment of CNN-based super-resolution. In this paper, a novel framework is proposed which supports the flexible allocation of complexity between the encoder and decoder. This approach employs a CNN model for video down-sampling at the encoder and uses a Lanczos3 filter to reconstruct full resolution at the decoder. The proposed method was integrated into the HEVC HM 16.20 software and evaluated on JVET UHD test sequences using the All Intra configuration. The experimental results demonstrate the potential of the proposed approach, with significant bitrate savings (more than 10%) over the original HEVC HM, coupled with reduced computational complexity at both encoder (29%) and decoder (10%).
Improved Handling of Repeats and Jumps in Audio-Sheet Image Synchronization

This paper studies the problem of automatically generating piano score following videos given an audio recording and raw sheet music images. Whereas previous works focus on synthetic sheet music where the data has been cleaned and preprocessed, we instead focus on developing a system that can cope with the messiness of raw, unprocessed sheet music PDFs from IMSLP. We investigate how well existing systems cope with real scanned sheet music, filler pages and unrelated pieces or movements, and discontinuities due to jumps and repeats. We find that a significant bottleneck in system performance is handling jumps and repeats correctly. In particular, we find that a previously proposed Jump DTW algorithm does not perform robustly when jump locations are unknown a priori. We propose a novel alignment algorithm called Hierarchical DTW that can handle jumps and repeats even when jump locations are not known. It first performs alignment at the feature level on each sheet music line, and then performs a second alignment at the segment level. By operating at the segment level, it is able to encode domain knowledge about how likely a particular jump is. Through carefully controlled experiments on unprocessed sheet music PDFs from IMSLP, we show that Hierarachical DTW significantly outperforms Jump DTW in handling various types of jumps.