Search

The separation of isotopes of natural Krypton at the gas-liquid and liquid-solid phase interfaces was studied under nonequilibrium conditions using a cryogenic cell and mass spectrometry. Condensate formation during Kr cooling begins at an equilibrium temperature, which corresponds to the partial pressure of the dominant isotope 84Kr, and is accompanied by depletion of the gas phase 84Kr with a separation coefficient of ~0.92 due to the excess of the heat of condensation over the heat of dissolution by 70-100 kJ/mol. The formation of a solid phase near the freezing point is accompanied by depletion of the gas phase by heavy isotopes. The separation coefficients 86Kr, 84Kr, 83Kr, 82Kr and 80Kr are about 1.06, 1.11, 0.86, 0.86 and 0.80, respectively, after the transition of ~30% of the atoms to the solid phase. Pressure-selective condensation can be used to separate components with close boiling points when distillation and temperature-selective condensation methods are ineffective, and freezing out of heavy isotopes can be used to enrich elements with practically important isotopes., Comment: 11 pages; 3 Figures; 3 Tables; 30 References

We consider a new type of ${\cal N}=4$, $d=1$ semi-dynamical multiplet based on the non-linear version of the mirror multiplet ${\bf (3, 4, 1)}$, with the triplet of bosonic physical fields parametrizing a three-dimensional sphere $S^3$ of the radius $R$. The limit $R\to\infty$ amounts to the contraction $S^3\to\mathbb{R}^3$ and leads to the linear mirror multiplet ${\bf (3, 4, 1)}$. Spin degrees of freedom described by a Wess-Zumino action specify a two-dimensional surface embedded in the sphere $S^3$. A pair of the examples considered correspond to the round and squashed ``fuzzy'' 2-spheres. We couple the squashed 2-sphere model to the dynamical mirror multiplet ${\bf (2, 4, 2)}$. A notable feature of this coupling is the dependence of the squashing parameter on the bosonic fields $z, \bar z$ of the chiral multiplet., Comment: 1+17 pages

In natural language processing (NLP), lexical function is a concept to unambiguously represent semantic and syntactic features of words and phrases in text first crafted in the Meaning-Text Theory. Hierarchical classification of lexical functions involves organizing these features into a tree-like hierarchy of categories or labels. This is a challenging task as it requires a good understanding of the context and the relationships among words and phrases in text. It also needs large amounts of labeled data to train language models effectively. In this paper, we present a dataset of most frequent Spanish verb-noun collocations and sentences where they occur, each collocation is assigned to one of 37 lexical functions defined as classes for a hierarchical classification task. Each class represents a relation between the noun and the verb in a collocation involving their semantic and syntactic features. We combine the classes in a tree-based structure, and introduce classification objectives for each level of the structure. The dataset was created by dependency tree parsing and matching of the phrases in Spanish news. We provide baselines and data splits for each objective.

Let $\theta$ be a real number, $n\in\mathbb N$, and $D_n(\theta)=\left\{\sum_{k=1}^n a_k\theta^{k}\mid a_k\in\{0,\dots,\lfloor \theta\rfloor\}\right\}. $ Let $\theta$ be a Perron number, that is, an algebraic integer $>1$ whose other Galois conjugates are less than $\theta$ in absolute value. I shall prove two results: (1) $\theta^n\ll\#D_n(\theta)\ll\sqrt n \theta^n.$ (2) $\theta$ is of height $\le \lfloor\theta\rfloor$., Comment: two pages

Let $\theta\in(1,2)$, and $\mu_{\theta}$ be the Bernoulli convolution parametrized by $\theta$, that is, the measure corresponding to the distribution of the random variable $\sum_{n=1}^{\infty} a_n\theta^{-n}$, where the $a_n$ are i.i.d. with probability of $a_n=0$ equal to $\frac12$. As is well known, $\mu_\theta$ is either equivalent to the Lebesgue measure on $\text{supp}(\mu_\theta)$, or singular. Recall that an algebraic integer $>1$ is called Pisot if all its other Galois conjugates are smaller than 1 in modulus. It is known that $\mu_\theta$ is singular with $\dim\mu_\theta<1$ if $\theta$ is Pisot. An algebraic integer $\theta$ greater than 1 is called a Salem number if all its other Galois conjugates are of modulus 1, except $\theta^{-1}$. I shall prove that (1) $\dim\mu_\theta=1$ if $\theta$ is an algebraic non-Pisot number. (2) if $\theta$ is Salem, then $\mu_\theta$ is equivalent to the Lebesgue measure on $\text{supp}(\mu_\theta)$, with an unbounded density in $L^p(\text{supp}(\mu_\theta))$ for all $p<\infty$. (3) Define \[ \beta_{\theta,x,n}=\#\left\{a_1\dots a_n: \exists a_{n+1}\dots\text{such that\ } x=\sum_{k=1}^{\infty}a_n\theta^{-k}\right\}. \] Then \[ \lim_{n\to\infty}\sqrt[n]{\beta_{\theta,x,n}}=\theta^{\dim\mu_\theta}\text{\ for}\ \mu_\theta-\text{a.e.} x. \] (4) Put \[ \bigcup_{n=1}^\infty\left\{\sum_{k=1}^{n}a_k\theta^k\mid a_k\in\{-1,0,1\}\right\}= \{y_0(\theta)

The Volterra integral-functional series is the classic approach for nonlinear black box dynamical systems modeling. It is widely employed in many domains including radiophysics, aerodynamics, electronic and electrical engineering and many other. Identifying the time-varying functional parameters, also known as Volterra kernels, poses a difficulty due to the curse of dimensionality. This refers to the exponential growth in the number of model parameters as the complexity of the input-output response increases. The least squares method (LSM) is widely acknowledged as the standard approach for tackling the issue of identifying parameters. Unfortunately, the LSM suffers with many drawbacks such as the sensitivity to outliers causing biased estimation, multicollinearity, overfitting and inefficiency with large datasets. This paper presents alternative approach based on direct estimation of the Volterra kernels using the collocation method. Two model examples are studied. It is found that the collocation method presents a promising alternative for optimization, surpassing the traditional least squares method when it comes to the Volterra kernels identification including the case when input and output signals suffer from considerable measurement errors.

The notion of complex dimension of a one-dimensional Cantor set $C=\bigcap_{n=1}^\infty C_n$ dates back decades. It is defined as the set of poles of the meromorphic $\zeta$-function $\zeta(s)=\sum_{n=1}^{\infty}d_j^s$, where $\Re s>0$, and $d_j$ is the length of the $j$th interval in $C_n$. Following the trend, I switch from sets to measures, which will allow me to generalize the construction to iterated function schemes that do not necessarily satisfy the Open Set Condition., Comment: 3 pages, no figures

The theory of linear Fredholm integral-functional equations of the second kind with linear functionals and with a parameter is considered. The necessary and sufficient conditions are obtained for the coefficients of the equation and those parameter values, in the nighbohood of which the equation has solutions. The leading terms of the asymptotics of the solutions are constructed. The constructive method is proposed for constructing a solution both in the regular case and in the irregular one. In the regular case, the solution is constructed as a Taylor series in powers of the parameter. In the irregular case, the solution is constructed as a Laurent series in powers of the parameter. Constructive theory and method is demonstrated on the model example.

We propose and explore the feasibility of a novel Ramsey interferometer created by a trapped two-state Bose-Einstein condensate (BEC) driven by dipole oscillations and gravitational sag. The BEC is formed in a pure cigar shaped compressed magnetic trap (CMT) via a dilute atom cloud of $^{87}Rb$ atoms in state $\vert F=2, m_F=+2 \rangle$ $(\vert +2 \rangle)$ of the $5 ^2S_{\frac{1}{2}}$ ground state. Here, Rmasey interferometry is performed with states $\vert F=2, m_F=+1 \rangle$ $(\vert +1 \rangle)$ and $\vert +2 \rangle$. The proposed interferometer utilises the response of atoms to the harmonic oscillator trapping potential and the gravitational sag due to the variation in the $m_F$ state. Briefly, the state $\vert +1 \rangle$ experiences a shallower radial trap with a larger gravitational sag; whereas, state $\vert +2 \rangle$ experiences a tighter radial trap with a gravitational sag which is half of state $\vert +1 \rangle$. Due to this, a superposition between the states $\vert +1 \rangle$ and $\vert +2 \rangle$ experiences multipath propagation resulting in an interference pattern. This may be utilised to measure local gravitational fields and measure inter-sate scattering lengths. Here, a theoretical framework is reported which is developed via the two-level system in combination with the Gross-Pitaevskii equation (GPE). Further, the development of a simulation tool via GPELabs in MATLAB that explores the prosed interferometer is reported along with key insights and findings., Comment: 16 pages, 13 figures

Our work here presents the analytical expressions for a typical Ramsey interferometric sequence for a three- and a five-level system. The analytical expressions are derived starting from the first principals of unitary time evolution operators. We focus on the three- and five-level systems because we propose a novel Ramsey interferometer created by a trapped two-state Bose-Einstein Condensate driven by dipole oscillations and gravitational sag. It involves the $^{87}Rb$ atoms in states $\vert F=2, m_F=+2 \rangle$ $(\vert +2 \rangle)$ and $\vert F=2, m_F=+1 \rangle$ $(\vert +1 \rangle)$ of the $5 ^2S_{\frac{1}{2}}$ ground state. Though the interferometer focusses on the two-levels, the experimental readouts involve all the five states in $F = 2$ hyperfine manifold. Therefore, the analytical derivation was first tested for three-levels and then expanded to five-levels. We developed the expressions for five-levels for greater analytical accuracy of the experimental scenario. This work provides a step-by-step outline for the derivation and methodology for the analytical expressions. These analytical formulae denote the population variation during Rabi and Ramsey oscillations for each state as well as the overall average for both the three- and five-level cases. The expressions are derived within the rotating wave approximation (RWA) under the equal Rabi condition. Further, by following the derivation methodology, these analytical expressions can be easily expanded for Ramsey sequences with unequal pulses, and Ramsey sequences with spin echo techniques., Comment: 19 pages, 7 figures

In this paper, we investigate the issue of hate speech by presenting a novel task of translating hate speech into non-hate speech text while preserving its meaning. As a case study, we use Spanish texts. We provide a dataset and several baselines as a starting point for further research in the task. We evaluated our baseline results using multiple metrics, including BLEU scores. The aim of this study is to contribute to the development of more effective methods for reducing the spread of hate speech in online communities.

In this paper, we present a parallel Spanish-Mazatec and Spanish-Mixtec corpus for machine translation (MT) tasks, where Mazatec and Mixtec are two indigenous Mexican languages. We evaluated the usability of the collected corpus using three different approaches: transformer, transfer learning, and fine-tuning pre-trained multilingual MT models. Fine-tuning the Facebook M2M100-48 model outperformed the other approaches, with BLEU scores of 12.09 and 22.25 for Mazatec-Spanish and Spanish-Mazatec translations, respectively, and 16.75 and 22.15 for Mixtec-Spanish and Spanish-Mixtec translations, respectively. The findings show that the dataset size (9,799 sentences in Mazatec and 13,235 sentences in Mixtec) affects translation performance and that indigenous languages work better when used as target languages. The findings emphasize the importance of creating parallel corpora for indigenous languages and fine-tuning models for low-resource translation tasks. Future research will investigate zero-shot and few-shot learning approaches to further improve translation performance in low-resource settings. The dataset and scripts are available at \url{https://github.com/atnafuatx/Machine-Translation-Resources}, Comment: Accepted to Third Workshop on NLP for Indigenous Languages of the Americas

This paper describes CIC NLP's submission to the AmericasNLP 2023 Shared Task on machine translation systems for indigenous languages of the Americas. We present the system descriptions for three methods. We used two multilingual models, namely M2M-100 and mBART50, and one bilingual (one-to-one) -- Helsinki NLP Spanish-English translation model, and experimented with different transfer learning setups. We experimented with 11 languages from America and report the setups we used as well as the results we achieved. Overall, the mBART setup was able to improve upon the baseline for three out of the eleven languages., Comment: Accepted to Third Workshop on NLP for Indigenous Languages of the Americas

The use of multilevel quantum information carriers, also known as qudits, attracts a significant deal of interest as a way for further scalability of quantum computing devices. However, a nontrivial task is to experimentally achieve a gain in the efficiency of realizing quantum algorithms with qudits since higher qudit levels typically have relatively short coherence times compared to qubit states. Here we propose and experimentally demonstrate two approaches for the realization of continuous dynamical decoupling of magnetic-sensitive states with $m_F=\pm1$ for qudits encoded in optical transition of trapped $^{171}$Yb$^{+}$ ions. We achieve improvement in qudit levels coherence time by the order of magnitude (more than 9 ms) without any magnetic shielding, which reveals the potential advantage of the symmetry of the $^{171}$Yb$^{+}$ ion energy structure for counteracting the magnetic field noise. Our results are a step towards the realization of qudit-based algorithms using trapped ions., Comment: 12 pages, 5 figures

The use of transfer learning methods is largely responsible for the present breakthrough in Natural Learning Processing (NLP) tasks across multiple domains. In order to solve the problem of sentiment detection, we examined the performance of four different types of well-known state-of-the-art transformer models for text classification. Models such as Bidirectional Encoder Representations from Transformers (BERT), Robustly Optimized BERT Pre-training Approach (RoBERTa), a distilled version of BERT (DistilBERT), and a large bidirectional neural network architecture (XLNet) were proposed. The performance of the four models that were used to detect disaster in the text was compared. All the models performed well enough, indicating that transformer-based models are suitable for the detection of disaster in text. The RoBERTa transformer model performs best on the test dataset with a score of 82.6% and is highly recommended for quality predictions. Furthermore, we discovered that the learning algorithms' performance was influenced by the pre-processing techniques, the nature of words in the vocabulary, unbalanced labeling, and the model parameters., Comment: This submission has been removed from arXiv because the submitter did not have the authority to grant the license at the time of submission

We introduce a novel Natural Language Processing (NLP) task called Guilt detection, which focuses on detecting guilt in text. We identify guilt as a complex and vital emotion that has not been previously studied in NLP, and we aim to provide a more fine-grained analysis of it. To address the lack of publicly available corpora for guilt detection, we created VIC, a dataset containing 4622 texts from three existing emotion detection datasets that we binarized into guilt and no-guilt classes. We experimented with traditional machine learning methods using bag-of-words and term frequency-inverse document frequency features, achieving a 72% f1 score with the highest-performing model. Our study provides a first step towards understanding guilt in text and opens the door for future research in this area.

The paper discusses the process of social and economic development of municipalities. A conclusion is made that developing an adequate model of social and economic development using conventional approaches presents a considerable challenge. It is proposed to use semantic modeling to represent the social and economic development of municipalities, and cognitive mapping to identify the set of connections that occur among indicators and that have a direct impact on social and economic development., Comment: arXiv admin note: substantial text overlap with arXiv:2111.13690

In this paper, we present a study of regret and its expression on social media platforms. Specifically, we present a novel dataset of Reddit texts that have been classified into three classes: Regret by Action, Regret by Inaction, and No Regret. We then use this dataset to investigate the language used to express regret on Reddit and to identify the domains of text that are most commonly associated with regret. Our findings show that Reddit users are most likely to express regret for past actions, particularly in the domain of relationships. We also found that deep learning models using GloVe embedding outperformed other models in all experiments, indicating the effectiveness of GloVe for representing the meaning and context of words in the domain of regret. Overall, our study provides valuable insights into the nature and prevalence of regret on social media, as well as the potential of deep learning and word embeddings for analyzing and understanding emotional language in online text. These findings have implications for the development of natural language processing algorithms and the design of social media platforms that support emotional expression and communication.