Miracle-dz/UniFAR-FTUs huggingface dataset:
Consistency of permutation tests for HSIC and dHSIC

The Hilbert--Schmidt Independence Criterion (HSIC) is a popular measure of the dependency between two random variables. The statistic dHSIC is an extension of HSIC that can be used to test joint independence of $d$ random variables. Such hypothesis testing for (joint) independence is often done using a permutation test, which compares the observed data with randomly permuted datasets. The main contribution of this work is proving that the power of such independence tests converges to 1 as the sample size converges to infinity. This answers a question that was asked in (Pfister, 2018) Additionally this work proves correct type 1 error rate of HSIC and dHSIC permutation tests and provides guidance on how to select the number of permutations one uses in practice. While correct type 1 error rate was already proved in (Pfister, 2018), we provide a modified proof following (Berrett, 2019), which extends to the case of non-continuous data. The number of permutations to use was studied e.g. by (Marozzi, 2004) but not in the context of HSIC and with a slight difference in the estimate of the $p$-value and for permutations rather than vectors of permutations. While the last two points have limited novelty we include these to give a complete overview of permutation testing in the context of HSIC and dHSIC.
Validation and Normalization of DCS corpus using Sanskrit Heritage tools to build a tagged Gold Corpus

The Digital Corpus of Sanskrit records around 650,000 sentences along with their morphological and lexical tagging. But inconsistencies in morphological analysis, and in providing crucial information like the segmented word, urges the need for standardization and validation of this corpus. Automating the validation process requires efficient analyzers which also provide the missing information. The Sanskrit Heritage Engine's Reader produces all possible segmentations with morphological and lexical analyses. Aligning these systems would help us in recording the linguistic differences, which can be used to update these systems to produce standardized results and will also provide a Gold corpus tagged with complete morphological and lexical information along with the segmented words. Krishna et al. (2017) aligned 115,000 sentences, considering some of the linguistic differences. As both these systems have evolved significantly, the alignment is done again considering all the remaining linguistic differences between these systems. This paper describes the modified alignment process in detail and records the additional linguistic differences observed. Reference: Amrith Krishna, Pavankumar Satuluri, and Pawan Goyal. 2017. A dataset for Sanskrit word segmentation. In Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, page 105-114. Association for Computational Linguistics, August.
Helium Enhancement in the Metal-rich Red Giants of ω Centauri

The helium-enriched (He-enriched) metal-rich red giants of ω Centauri, discovered by Hema & Pandey using the low-resolution spectra from the Vainu Bappu Telescope, and confirmed by the analyses of the high-resolution spectra obtained from the HRS-South African Large Telescope for LEID 34225 and LEID 39048, are reanalyzed here to determine their degree of He enhancement/hydrogen deficiency (H deficiency). The observed MgH band combined with model atmospheres with differing He/H ratios are used for the analyses. The He/H ratios of these two giants are determined by enforcing the fact that the derived Mg abundances from the Mg i lines and from the subordinate lines of the MgH band must be same for the adopted model atmosphere. The estimated He/H ratios for LEID 34225 and LEID 39048 are 0.15 ± 0.04 and 0.20 ± 0.04, respectively, whereas the normal He/H ratio is 0.10. Following the same criteria for the analyses of the other two comparison stars (LEID 61067 and LEID 32169), a normal He/H ratio of 0.10 is obtained. The He/H ratio of 0.15–0.20 corresponds to a mass fraction of helium (Z(He) = Y) of about 0.375–0.445. The range of helium enhancement and the derived metallicity of the program stars are in line with those determined for ω Cen’s blue main-sequence stars. Hence, our study provides the missing link for the evolutionary track of the metal-rich helium-enhanced population of ω Centuari. This research work is the very first spectroscopic determination of the amount of He enhancement in the metal-rich red giants of ω Centauri using the Mg i and MgH lines.
Pfaffian representations of cubic threefolds

Given a cubic hypersurface $X\subset \mathbb{P}^4$, we study the existence of Pfaffian representations of $X$, namely of $6\times 6$ skew-symmetric matrices of linear forms $M$ such that $X$ is defined by the equation $Pf(M)=0$. It was known that such a matrix always exists whenever $X$ is smooth. Here we prove that the same holds whenever $X$ is singular, hence that every cubic threefold is Pfaffian.
Scalar Active Mixtures: The Nonreciprocal Cahn-Hilliard Model

Pair interactions between active particles need not follow Newton's third law. In this work we propose a continuum model of pattern formation due to non-reciprocal interaction between multiple species of scalar active matter. The classical Cahn-Hilliard model is minimally modified by supplementing the equilibrium Ginzburg-Landau dynamics with particle number conserving currents which cannot be derived from a free energy, reflecting the microscopic departure from action-reaction symmetry. The strength of the asymmetry in the interaction determines whether the steady state exhibits a macroscopic phase separation or a traveling density wave displaying global polar order. The latter structure, which is equivalent to an active self-propelled smectic phase, coarsens via annihilation of defects, whereas the former structure undergoes Ostwald ripening. The emergence of traveling density waves, which is a clear signature of broken time-reversal symmetry in this active system, is a generic feature of any multi-component mixture with microscopic non-reciprocal interactions.
Neural Machine Translation for South Africa's Official Languages

Recent advances in neural machine translation (NMT) have led to state-of-the-art results for many European-based translation tasks. However, despite these advances, there is has been little focus in applying these methods to African languages. In this paper, we seek to address this gap by creating an NMT benchmark BLEU score between English and the ten remaining official languages in South Africa.
A class of fractional p()-Kirchhoff type systems

This paper is concerned with an elliptic system of Kirchhoff type, driven by the fractional p(x)- operator. By means of the direct variational method and Ekeland variational principle, we show the existence of a weak solution for the fractional p()-Kirchhoff system. This is our first attempt to study the elliptic system with fractional variable exponents. Our main theorem extends previous results in several directions.
Systematic Analysis of Cluster Similarity Indices: How to Validate Validation Measures

There are many cluster similarity indices used to evaluate clustering algorithms, and choosing the best one for a particular task is usually an open problem. In this paper, we perform a thorough analysis of this problem: we develop a list of desirable properties (requirements) and theoretically verify which indices satisfy them. In particular, we investigate dozens of pair-counting indices and prove that none of them meet all the requirements. Based on our analysis, we propose using the arccosine of the correlation coefficient as a similarity measure and show that it satisfies almost all the requirements (except for one, which is still satisfied asymptotically). We illustrate the practical importance of our analysis via an online experiment within a major news aggregator system.
Distance-2 Coloring in the CONGEST Model

We give efficient randomized and deterministic distributed algorithms for computing a distance-2 vertex coloring of a graph G in the CONGEST model. In particular, if Δ is the maximum degree of G, we show that there is a randomized CONGEST model algorithm to compute a distance-2 coloring of G with Δ2 + 1 colors in O(log Δ · log n) rounds. Further if the number of colors is slightly increased to (1 + ∈)Δ2 for some ∈ > 1/polylog n, we show that it is even possible to compute a distance-2 coloring deterministically in polylog n time in the CONGEST model. Finally, we give a O(Δ2 + log* n)-round deterministic CONGEST algorithm to compute distance-2 coloring with Δ2 + 1 colors.
An Accurate Analytical Approach for the Parameterization of the Single Diode Model of Photovoltaic Cell

A single diode model with five parameters is the simplest and robust approach for modeling a photovoltaic (PV) module in a simulated environment. These parameters need to be accurately extracted from the specifications given in the datasheet of the PV module such that the simulation model should exhibit the same characteristics as the actual measurements. A definite set of five independent equations, that should represent the characteristics of the PV module as accurately as possible, is needed to solve for these five parameters. In literature, the first four equations are easily created from the key data points on the characteristic curve given in the datasheet of the PV module. The main challenge however is the formulation of the fifth equation. The approaches found in literature have inherent inaccuracies due to some approximations or iterative techniques leading to discrepancy in the simulated model. This paper presents a unique analytical approach for the formulation of the fifth equation which yields the most accurate single diode model. As evident from the results, the proposed method is superior to not just the single diode model approaches but also to the double diode ones in simulating the characteristics of the PV module with least error.
You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentation

Data augmentation is one of the most effective ways to make end-to-end automatic speech recognition (ASR) perform close to the conventional hybrid approach, especially when dealing with low-resource tasks. Using recent advances in speech synthesis (text-to-speech, or TTS), we build our TTS system on an ASR training database and then extend the data with synthesized speech to train a recognition model. We argue that, when the training data amount is relatively low, this approach can allow an end-to-end model to reach hybrid systems’ quality. For an artificial low-to-medium-resource setup, we compare the proposed augmentation with the semi-supervised learning technique. We also investigate the influence of vocoder usage on final ASR performance by comparing Griffin-Lim algorithm with our modified LPCNet. When applied with an external language model, our approach outperforms a semi-supervised setup for LibriSpeech test-clean and only 33% worse than a comparable supervised setup. Our system establishes a competitive result for end-to-end ASR trained on LibriSpeech train-clean-100 set with WER 4.3% for test-clean and 13.5% for test-other.
Quantum metamaterial for nondestructive microwave photon counting.

Detecting traveling photons is an essential primitive for many quantum information processing tasks. We introduce a single-photon detector design operating in the microwave domain, based on a weakly nonlinear metamaterial where the nonlinearity is provided by a large number of Josephson junctions. The combination of weak nonlinearity and large spatial extent circumvents well-known obstacles limiting approaches based on a localized Kerr medium. Using numerical many-body simulations we show that the single-photon detection fidelity increases with the length of the metamaterial to approach one at experimentally realistic lengths. A remarkable feature of the detector is that the metamaterial approach allows for a large detection bandwidth. In stark contrast to conventional photon detectors operating in the optical domain, the photon is not destroyed by the detection and the photon wavepacket is minimally disturbed. The detector design we introduce offers new possibilities for quantum information processing, quantum optics and metrology in the microwave frequency domain.
Local-scale Patterns of Larval Settlement in a Planktivorous Damselfish—Do They Predict Recruitment?

The degree to which local-scale spatial variation in larval supply predicted subsequent patterns in juvenile densities was examined for a planktivorous damselfish (Dascyllus trimaculatus) at Moorea, French Polynesia. D. trimaculatus young settle from the plankton to sea anemones, where they remain until becoming free-ranging adults. Daily settlement onto anemones without fish was estimated at several sites in a lagoon for one 14-day period and compared with patterns in the density of juveniles that had accrued after 10 successive settlement periods. Spatial variation attributable to larval supply occurred mostly among individual anemones, between the halves of each study site, and among different locations in the lagoon. In contrast, there was relatively little variation at any spatial scale in the density of young after 10 settlement pulses. Several mechanisms were examined that could account for the observed reduction in spatial variance through time, and three involving interactions with young already present were implicated. First, settlement was not independent of residents (the density of young already on an anemone); compared with the absence of fish, settlement was facilitated at low and inhibited at high densities of residents. Second, older juveniles moved among local anemones, and the immigration and emigration rates per capita varied with density on an anemone. Finally, per capita mortality during the first few days after settlement was density-dependent. These findings emphasize the critical need to distinguish among effects that arise from the delivery of larvae, the availability of appropriate habitat, and interactions that affect fish at settlement and immediately thereafter.
On the interpretation of non-resonant phenomena at colliders

With null results in resonance searches at the LHC, the physics potential focus is now shifting towards the interpretation of non-resonant phenomena. An example of such shift is the increased popularity of the EFT programme. We can embark on such programme owing to good integrated luminosity and an excellent understanding of the detectors, which will allow these searches to become more intense as the LHC continues. In this paper we provide a framework to perform this interpretation in terms of a diverse set of scenarios, including {\it 1. ) } generic heavy new physics described at low energies in terms of a derivative expansion, such as in the EFT approach, {\it 2. )} very light particles with derivative couplings, such as axions or other light pseudo-Goldstone bosons, and {\it 3. )} the effect of a quasi-continuum of resonances, which can come from a number of strongly-coupled theories, extra-dimensional models, clockwork set-ups and their deconstructed cousins. These scenarios are not equivalent despite all non-resonant, although the matching among some of them is possible and we provide it in this paper.
A Deeper Look at the Unsupervised Learning of Disentangled Representations in β-VAE from the Perspective of Core Object Recognition

The ability to recognize objects despite there being differences in appearance, known as Core Object Recognition, forms a critical part of human perception. While it is understood that the brain accomplishes Core Object Recognition through feedforward, hierarchical computations through the visual stream, the underlying algorithms that allow for invariant representations to form downstream is still not well understood. (DiCarlo et al., 2012) Various computational perceptual models have been built to attempt and tackle the object identification task in an artificial perceptual setting. Artificial Neural Networks, computational graphs consisting of weighted edges and mathematical operations at vertices, are loosely inspired by neural networks in the brain and have proven effective at various visual perceptual tasks, including object characterization and identification. (Pinto et al., 2008) (DiCarlo et al., 2012) For many data analysis tasks, learning representations where each dimension is statistically independent and thus disentangled from the others is useful. If the underlying generative factors of the data are also statistically independent, Bayesian inference of latent variables can form disentangled representations. This thesis constitutes a research project exploring a generalization of the Variational Autoencoder (VAE), $\beta$-VAE, that aims to learn disentangled representations using variational inference. $\beta$-VAE incorporates the hyperparameter $\beta$, and enforces conditional independence of its bottleneck neurons, which is in general not compatible with the statistical independence of latent variables. This text examines this architecture, and provides analytical and numerical arguments, with the goal of demonstrating that this incompatibility leads to a non-monotonic inference performance in $\beta$-VAE with a finite optimal $\beta$.
Galaxy power spectrum in general relativity

We present the galaxy power spectrum in general relativity. Using a novel approach, we derive the galaxy power spectrum taking into account all the relativistic effects in observations. In particular, we show independently of survey geometry that relativistic effects yield no divergent terms (proportional to k−4Pm(k) or k−2Pm(k) on all scales) that would mimic the signal of primordial non-Gaussianity. This cancellation of such divergent terms is indeed expected from the equivalence principle, meaning that any perturbation acting as a uniform gravity on the scale of the experiment cannot be measured. We find that the unphysical infrared divergence obtained in previous calculations occurred only due to not considering all general relativistic contributions consistently. Despite the absence of divergent terms, general relativistic effects represented by non-divergent terms alter the galaxy power spectrum at large scales (smaller than the horizon scale). In our numerical computation of the full galaxy power spectrum, we show the deviations from the standard redshift-space power spectrum due to these non-divergent corrections. We conclude that, as relativistic effects significantly alter the galaxy power spectrum at k≲ keq, they need to be taken into account in the analysis of large-scale data.
Yellow Death: A Disease of Date Palm in Iran Caused by Fusarium solani

In December 2003, a severe general yellowing and death of the fronds of date palm (Phoenix dactylifera) occurred in a grove in the vicinity of Kazeron district, west of Fars province, Iran. Fusarium solani was isolated from the crown, and xylem rays sampled from the trunk 1.5 m above soil level. In pathogenicity tests using artificially infested soil and I-year-old date palm seedlings, an isolate from the trunk (FST) induced general chlorosis and death of seedlings 25-28 days after inoculation. Similar results were obtained when seedlings were planted in naturally infested soil. In both procedures, distal portions of the roots and crown were affected. The fungus was re-isolated from the crown and leaf bases of the inoculated seedlings. This is the first report of a serious disease of date palm, which we call yellow death, incited by F. solani in Iran.
deepSELF: An Open Source Deep Self End-to-End Learning Framework

We introduce an open-source toolkit, i.e., the deep Self End-to-end Learning Framework (deepSELF), as a toolkit of deep self end-to-end learning framework for multi-modal signals. To the best of our knowledge, it is the first public toolkit assembling a series of state-of-the-art deep learning technologies. Highlights of the proposed deepSELF toolkit include: First, it can be used to analyse a variety of multi-modal signals, including images, audio, and single or multi-channel sensor data. Second, we provide multiple options for pre-processing, e.g., filtering, or spectrum image generation by Fourier or wavelet transformation. Third, plenty of topologies in terms of NN, 1D/2D/3D CNN, and RNN/LSTM/GRU can be customised and a series of pretrained 2D CNN models, e.g., AlexNet, VGGNet, ResNet can be used easily. Last but not least, above these features, deepSELF can be flexibly used not only as a single model but also as a fusion of such.
Influence of climate on annual survival of Barn Swallows (Hirundo rustica) breeding in North America

ABSTRACT Population dynamics of migratory birds are influenced by both local weather and larger-scale patterns in climate that can operate at various stages of their annual cycle. We investigated correlations between (1) annual climatic indices and weather during the breeding season and (2) the annual survival of Barn Swallows (Hirundo rustica) breeding at 2 sites in North America. Mark–recapture data collected during a 10-yr period for each of the 2 colonies in eastern and western North America were analyzed to model annual survival probabilities. Annual survival rates of Barn Swallows breeding in Seattle, Washington, USA, were higher in years preceded by El Niño Southern Oscillation (ENSO) winters and higher in years with more positive North Atlantic Oscillation (NAO) values. ENSO was expected to primarily influence wintering conditions through rainfall amount, and NAO was expected to influence climate on the breeding grounds; thus, climatic conditions on both breeding and wintering grounds likely affected the survival of these Seattle-breeding birds. By contrast, annual survival of swallows breeding in southern Ontario, Canada, remained constant over time and were not affected by any of the climatic parameters studied, which suggests that NAO did not have a strong effect on climatic conditions there and/or that these birds winter in regions where ENSO is not strongly correlated with local weather conditions. Alternatively, there may be less geographic variation in wintering-ground locations for Barn Swallows breeding in Seattle, resulting in stronger ENSO effects on survival for the Seattle population. Our results demonstrate how correlations between climate patterns on wintering grounds and annual survival can provide information on migratory connectivity at continental scales and underline the importance of local weather conditions throughout the annual cycle on survivorship and population dynamics of aerial insectivorous birds.
Role of phosphate-solubilizing bacteria in improving the soil fertility and crop productivity in organic farming

In the present study, two phosphate-solubilizing bacteria Pantoea cypripedii and Pseudomonas plecoglossicida isolated from an organic field were tested for their efficacy to solubilize rock phosphate (RP) and other plant growth-promoting activities such as nitrogen fixation and the production of indole acetic acid (IAA) and siderophores. Both of the bacteria effectively solubilized RP and released significant amount of phosphorous (up to 271 µg P ml–1) in the culture medium. These isolates produced IAA and siderophores and failed to fix nitrogen as determined by acetylene reduction. Two-year field study was conducted to test the efficacy of these bacteria on the growth and yield of maize and wheat crops grown in organic farm with and without RP. Field trials for both crops were conducted in a completely randomized block design consisting of six treatments and each treatment with triplicates. Significant increase in growth parameters, grain yield, total P uptake, and soil fertility in context of organic carbon, available P, total P, and enzyme activities were observed in inoculation treatments along with RP fertilization compared with control. This study suggested that P. cypripedii and P. plecoglossicida along with RP play an important role in improving crop productivity in organic farming.