Miracle-dz/UniFAR-FTUs huggingface dataset:
Using a Conditional Generative Adversarial Network to Control the Statistical Characteristics of Generated Images for IACT Data Analysis

Generative adversarial networks are a promising tool for image generation in the astronomy domain. Of particular interest are conditional generative adversarial networks (cGANs), which allow you to divide images into several classes according to the value of some property of the image, and then specify the required class when generating new images. In the case of images from Imaging Atmospheric Cherenkov Telescopes (IACTs), an important property is the total brightness of all image pixels (image size), which is in direct correlation with the energy of primary particles. We used a cGAN technique to generate images similar to whose obtained in the TAIGA-IACT experiment. As a training set, we used a set of two-dimensional images generated using the TAIGA Monte Carlo simulation software. We artificiallly divided the training set into 10 classes, sorting images by size and defining the boundaries of the classes so that the same number of images fall into each class. These classes were used while training our network. The paper shows that for each class, the size distribution of the generated images is close to normal with the mean value located approximately in the middle of the corresponding class. We also show that for the generated images, the total image size distribution obtained by summing the distributions over all classes is close to the original distribution of the training set. The results obtained will be useful for more accurate generation of realistic synthetic images similar to the ones taken by IACTs.
Exploiting Language Models for Entity Alignment in Knowledge Graphs

Knowledge Graphs (KGs) are composed of structured information about a particular domain in the form of entities and relations and have been recognized as the foundation for various artificial intelligence applications. These applications often require diverse knowledge which is not present in a single KG. Entity Alignment (EA) is the task of aligning equivalent entities which represent the same real-world object across different KGs. As a result, heterogeneous knowledge from different data sources is fused into a unified and consistent KG. Prior works in the field incorporate embedding-based methods to learn the graph structure and then perform the alignment by using a set of aligned entity pairs for training. Most of these models are supervised and require already aligned entities. Additionally, they discard a valuable source of information, e.g. textual data which is present in the KG. On the other hand, Language Models (LMs) have become crucial for achieving state-ofthe-art performance in Natural Language Processing (NLP) tasks. These pre-trained LMs generate representations of textual information into a low dimensional vector space. We draw a parallel between LMs and KGs by investigating the ability of an unsupervised multi-lingual word embedding alignment model to perform entity alignment. This thesis introduces a novel approach for Entity Alignment named LM-EA which leverages only the side information, namely entity names and descriptions. LMs Word2vec [25], FastText [6], Wikipedia2Vec [39] and mBERT [13], constitute the basis for the embedding learning process. We propose one supervised and one unsupervised approach for aligning two embedding spaces, inspired by the work of [11]. Our model has been successfully evaluated on two datasets, one monolingual and one multilingual.
A new and efficient method for the computation of Legendre coefficients

An efficient procedure for the computation of the coefficients of Legendre expansions is here presented. We prove that the Legendre coefficients associated with a function f(x) can be represented as the Fourier coefficients of an Abel-type transform of f(x). The computation of N Legendre coefficients can then be performed in O(N log N) operations with a single Fast Fourier Transform of the Abel-type transform of f(x).
First-Principle Investigation of Hypothetical NiF 4 Crystal Structures

: An important synthetic route for the ﬂuorinated organic compounds is electrochemical ﬂuorination (ECF). This is a process taking place on a nickel anode immersed in anhydrous HF. Even though the mechanism is not fully resolved, it is believed that it involves higher valent nickel ﬂuorides formed on the anode. One such compound could be NiF 4 . Its synthesis and existence have been reported in the literature. However, its crystal structure has so far remained unknown. In this paper, we present, for the ﬁrst time, the theoretical study of the possible crystal structure of NiF 4 . We investigated six crystal structures of known metal tetraﬂuorides as possible candidates for NiF 4 by periodic DFT, with the PBE+U method. Of the investigated structures, the most stable polymorph of NiF 4 was found to be of the same crystal structure as RuF 4 . The unit cell parameters were calculated to be a = 4.80 Å, b = 5.14 Å, c = 5.18 Å and β = 105.26 ◦ . All but one of the investigated structures feature octahedrally coordinated nickel centers with two non-bridging ﬂuorine atoms. In the structure originating from ZrF 4 , all six ﬂuorine atoms around the nickel centers are bridging and two are located in the vacancies around the nickel skeleton, not directly bound to nickel. The overall magnetic arrangement in all the investigated structures is antiferromagnetic. A comparison with other binary nickel ﬂuorides supports the experimental ﬁndings that NiF 4 is thermodynamically the least stable.
A spiking neural network with fixed synaptic weights based on logistic maps for a classification task

Spiking neural networks are increasingly popular for machine learning applications, thanks to ongoing progress in the hardware implementation of spiking networks in low-energy-consuming neuromorphic hardware. Still, obtaining a spiking neural network model that solves a classification task with the same level of accuracy as a artificial neural network remains a challenge. Of especial relevance is the development of spiking neural network models trained on base of local synaptic plasticity rules that can be implemented either in digital neuromorphic chips or in memristive devices. However, existing spiking networks with local learning all have, to our knowledge, one-layer topology, and no multi-layer ones have been proposed so far. As an initial step towards resolving this problem, we study the possibility of using a non-trainable layer of spiking neurons as an encoder layer within a prospective multi-layer spiking neural network, implying that the prospective subsequent layers could be trained on base of local plasticity. We study a spiking neural network model with non-trainable synaptic weights preset on base of logistic maps, similarly to what was proposed recently in the literature for formal neural networks. We show that one layer of spiking neurons with such weights can transform input vectors preserving the information about the classes of the input vectors, so that this information can be extracted from the neuron’s output spiking rates by
Vanishing of special values and central derivatives in Hida families

The theme of this work is the study of the Nekov\'a\v{r}-Selmer group H^1_f(K,T) attached to a twisted Hida family T of Galois representations and a quadratic number field K. The results that we obtain have the following shape: if a twisted L-function of a suitable modular form in the Hida family has order of vanishing r at most 1 at the central critical point then the rank of H^1_f(K,T) as a module over a certain local Hida-Hecke algebra is equal to r. Under the above assumption, we also show that infinitely many twisted L-functions of modular forms in the Hida family have the same order of vanishing at the central critical point. Our theorems extend to more general arithmetic situations results obtained by Howard when K is an imaginary quadratic field and all the primes dividing the tame level of the Hida family split in K.
Shadow Rate Models and Monetary Policy

We examine the channels and efficacy of monetary policy at the zero lower bound (ZLB) through the lens of shadow rate models. We compare estimates across models with various factor structures and different assumptions about interest rate forecasts. We confirm that calendar-based forward guidance discretely shifted the implied duration of the ZLB and that large scale asset purchases (LSAPs) primarily lowered term premia. However, we find that the real effects of monetary policy are more muted relative to prior estimates: a 1 standard deviation fall in the shadow rate causes a peak decline in the unemployment rate of 0.003-0.01%. ∗Connolly: Colgate University Department of Economics and Boston College Department of Economics. Struby: Carleton College Department of Economics. The authors would like to thank Leland Farmer for sharing his discretization filter code and Florian Huber for sharing his MSFAVAR code. We also thank Dino Palazzo, Dongho Song, Dominique Brabant, Ryan Chahrour, and audiences at the Federal Reserve Bank of Kansas City, Macalester College, Colgate University, the Federal Reserve Bank of St. Louis Time Series Workshop and numerous conferences for helpful feedback. Alex Gallin provided excellent research assistance. The authors acknowledge the Minnesota Supercomputing Institute (MSI) at the University of Minnesota for providing resources that contributed to the research results reported within this paper. Any mistakes are the authors’ alone. We use data from the Blue Chip Financial Survey, which is a registered trademark of CCH Incorporated. This paper previously circulated under the title “Subjective Shadow Rate Beliefs at the Zero Lower Bound. ” †First version: April 2019
The Shannon–McMillan–Breiman theorem beyond amenable groups

We introduce a new isomorphism-invariant notion of entropy for measure preserving actions of arbitrary countable groups on probability spaces, which we call orbital Rokhlin entropy. It employs Danilenko's orbital approach to entropy of a partition, and is motivated by Seward's recent generalization of Rokhlin entropy from amenable to general groups. A key ingredient in our approach is the use of an auxiliary probability-measure-preserving hyperfinite equivalence relation. Under the assumption of ergodicity of the auxiliary equivalence relation, our main result is a Shannon-McMillan-Breiman pointwise almost sure convergence theorem for the orbital entropy of partitions in measure-preserving group actions, the first such convergence result going beyond the realm of amenable groups. As a special case, we obtain a Shannon-McMillan-Breiman theorem for all strongly mixing actions of any countable group. Furthermore, we compare orbital Rokhlin entropy to Rokhlin entropy, and using an important recent result of Seward we show that they coincide for free, ergodic actions of any countable group. Finally, we consider actions of non-Abelian free groups and demonstrate the geometric significance of the entropy equipartition property implied by the Shannon-McMillan-Breiman theorem. We show that the orbital entropy of a partition is the limit of the information functions of the sequence of partitions arising from refining any given finite partition along almost every horoball in the group.
Are State-of-the-art Visual Place Recognition Techniques any Good for Aerial Robotics?

Visual Place Recognition (VPR) has seen significant advances at the frontiers of matching performance and computational superiority over the past few years. However, these evaluations are performed for ground-based mobile platforms and cannot be generalized to aerial platforms. The degree of viewpoint variation experienced by aerial robots is complex, with their processing power and on-board memory limited by payload size and battery ratings. Therefore, in this paper, we collect $8$ state-of-the-art VPR techniques that have been previously evaluated for ground-based platforms and compare them on $2$ recently proposed aerial place recognition datasets with three prime focuses: a) Matching performance b) Processing power consumption c) Projected memory requirements. This gives a birds-eye view of the applicability of contemporary VPR research to aerial robotics and lays down the the nature of challenges for aerial-VPR.
Experimentally superposing two pure states with partial prior knowledge

Superposition, arguably the most fundamental property of quantum mechanics, lies at the heart of quantum information science. However, how to create the superposition of any two unknown pure states remains as a daunting challenge. Recently, it is proved that such a quantum protocol does not exist if the two input states are completely unknown, whereas a probabilistic protocol is still available with some prior knowledge about the input states [M. Oszmaniec \emph{et al.}, Phys. Rev. Lett. 116, 110403 (2016)]. The knowledge is that both of the two input states have nonzero overlaps with some given referential state. In this work, we experimentally realize the probabilistic protocol of superposing two pure states in a three-qubit nuclear magnetic resonance system. We demonstrate the feasibility of the protocol by preparing a families of input states, and the average fidelity between the prepared state and expected superposition state is over 99%. Moreover, we experimentally illustrate the limitation of the protocol that it is likely to fail or yields very low fidelity, if the nonzero overlaps are approaching zero. Our experimental implementation can be extended to more complex situations and other quantum systems.
A dichotomy for measures of maximal entropy near time-one maps of transitive Anosov flows

We show that time-one maps of transitive Anosov flows of compact manifolds are accumulated by diffeomorphisms robustly satisfying the following dichotomy: either all of the measures of maximal entropy are non-hyperbolic, or there are exactly two ergodic measures of maximal entropy, one with a positive central exponent and the other with a negative central exponent. We establish this dichotomy for certain partially hyperbolic diffeomorphisms isotopic to the identity whenever both of their strong foliations are minimal. Our proof builds on the approach developed by Margulis for Anosov flows where he constructs suitable families of measures on the dynamical foliations.
Isolation And Identification Of Fungi Associated With Walnuts, Almonds, Hazelnuts, Cashews, Pistachios And Peanuts Using Molecular Diagnostics Technique(PCR) In The Local Markets Of Kirkuk Governorate / Iraq

The aim of the current study is to Isolation and identification of fungi contaminated with walnuts, almonds, hazelnuts, cashews,peanuts and pistachios using molecular diagnostics (PCR). Nuts samples were collected with an amount of 2000 grams of walnuts,almonds, hazelnuts, cashews, pistachios and peanuts from different locations of the local markets in the city of Kirkuk and intendedfor human consumption for the period. From 15-November-2022 until 20-December-2022.The results of the molecular diagnosisPolymerase Chain Reaction (PCR)of the fungi with a sequence analysis and drawing the genetic tree to compare the isolated anddiagnosed fungi with the global isolates associated with the fungal genera Aspergillus, Penicillium, Rhizopus, Paecilomyces of nuts,Aspergillus flavus species With a percentage of 26.3%, the isolated fungi sequence and prevalence rates were as follows: A.flavus,A.niger, P. aurantiogrise, P. oxalicum, A. fumigatus, Rhizopus stolonifer and Paecillomyces variotti at 26.3%, 19.7%, 15.8%, 13.8%,11.2%, 9.2% and 3.9%, respectively.
A Dynamic Bayesian Model for Interpretable Decompositions of Market Behaviour

We propose a heterogeneous simultaneous graphical dynamic linear model (H-SGDLM), which extends the standard SGDLM framework to incorporate a heterogeneous autoregressive realised volatility (HAR-RV) model. This novel approach creates a GPU-scalable multivariate volatility estimator, which decomposes multiple time series into economically-meaningful variables to explain the endogenous and exogenous factors driving the underlying variability. This unique decomposition goes beyond the classic one step ahead prediction; indeed, we investigate inferences up to one month into the future using stocks, FX futures and ETF futures, demonstrating its superior performance according to accuracy of large moves, longer-term prediction and consistency over time.
Generalized Mirror Prox: Solving Variational Inequalities with Monotone Operator, Inexact Oracle, and Unknown H\"older Parameters

We introduce an inexact oracle model for variational inequalities (VI) with monotone operator, propose a numerical method which solves such VI's and analyze its convergence rate. As a particular case, we consider VI's with H\"older continuous operator and show that our algorithm is universal. This means that without knowing the H\"older parameter $\nu$ and H\"older constant $L_{\nu}$ it has the best possible complexity for this class of VI's, namely our algorithm has complexity $O\left( \inf_{\nu\in[0,1]}\left(\frac{L_{\nu}}{\varepsilon} \right)^{\frac{2}{1+\nu}}R^2 \right)$, where $R$ is the size of the feasible set and $\varepsilon$ is the desired accuracy of the solution. We also consider the case of VI's with strongly monotone operator and generalize our method for VI's with inexact oracle and our universal method for this class of problems. Finally, we show, how our method can be applied to convex-concave saddle-point problems with H\"older continuous partial subgradients.
Risk Bounds for Learning Multiple Components with Permutation-Invariant Losses

This paper proposes a simple approach to derive efficient error bounds for learning multiple components with sparsity-inducing regularization. Indeed, we show that for such regularization schemes, known decompositions of the Rademacher complexity over the components can be used in a more efficient manner to result in tighter bounds without too much effort. We give examples of application to switching regression and center-based clustering/vector quantization. Then, the complete workflow is illustrated on the problem of subspace clustering, for which decomposition results were not previously available. For all these problems, the proposed approach yields risk bounds with mild dependencies on the number of components and completely removes this dependency for nonconvex regularization schemes that could not be handled by previous methods.
Does internet use promote mental health among middle-aged and older adults in China?

In recent years, China’s Internet penetration rate has increased, and the scale of middle-aged and older adults’ netizen has continued to expand. However, the impact of internet use on mental health remains controversial. This paper analyzes 14,497 middle-aged and older adults’ valid respondents in the 2018 Chinese Family Panel Study (CFPS2018) to evaluate the impact of Internet use on the mental health of middle-aged and older adults and its intermediary mechanisms. The findings show that moderate use of the Internet can significantly reduce depression levels and boost cognitive function in middle-aged and older adults. But excessive internet use will also lead to increased levels of depression and decreased cognitive function. Different Internet use purposes will also lead to different psychological states. Online socializing, entertainment and business activities can significantly reduce depression levels and promote cognitive functions. Online studying and working only have positive effects on cognitive functions, which have no significant relationship to depression levels. In addition, analysis of the mediation effect found that life satisfaction is a path mechanism for Internet use and affect different dimensions of mental health.
Ericoid mycorrhizal fungi as biostimulants for improving propagation and production of ericaceous plants

The mutualistic relationship between mycorrhizal fungi and plant roots is a widespread terrestrial symbiosis. The symbiosis enables plants to better adapt to adverse soil conditions, enhances plant tolerance to abiotic and biotic stresses, and improves plant establishment and growth. Thus, mycorrhizal fungi are considered biostimulants. Among the four most common types of mycorrhizae, arbuscular mycorrhiza (AM) and ectomycorrhiza (EcM) have been more intensively studied than ericoid mycorrhiza (ErM) and orchidaceous mycorrhiza (OrM). ErM fungi can form symbiotic relationships with plants in the family Ericaceae. Economically important plants in this family include blueberry, bilberry, cranberry, and rhododendron. ErM fungi are versatile as they are both saprotrophic and biotrophic. Increasing reports have shown that they can degrade soil organic matter, resulting in the bioavailability of nutrients for plants and microbes. ErM fungi can synthesize hormones to improve fungal establishment and plant root initiation and growth. ErM colonization enables plants to effective acquisition of mineral nutrients. Colonized plants are able to tolerate different abiotic stresses, including drought, heavy metals, and soil salinity as well as biotic stresses, such as pathogen infections. This article is intended to briefly introduce ErM fungi and document their beneficial effects on ericaceous plants. It is anticipated that the exploration of this special group of fungi will further improve our understanding of their value of symbiosis to ericaceous plants and ultimately result in the application of valuable species or strains for improving the establishment and growth of ericaceous plants.
On the invariance of the relative rest in doubly special relativity

In the framework of the most-studied doubly special relativity models the use of the naive formula v = dE/dp has been argued to lead to inconsistencies connected to different rules of transformation, under boosts, of particles with the same energy but with different masses. In this paper, we show that, at least in 1 + 1 dimensions, doubly special relativity can be formulated in such a way that the formula v = dE/dp is fully consistent with the invariance of the relative rest, easily fitting to the relativity principle. It is also argued that, always in 1 + 1 dimensions, is not necessary to renounce to the usual (commutative) Minkowski spacetime endowed with energy-independent boost transformations. The compatibility of the approach with superluminal propagation, with linear addition rule for energy, and possible extensions to 3 + 1 dimensions are also discussed.