Miracle-dz/UniFAR-FTUs huggingface dataset:
Concept for Teaching Theory of Composition and Form Formation

In the article, the author shares her experience of teaching the disciplines Form Formation and Theory of Composition to students of Engineering Design in the Forestry University in Sofia. The goals of the two disciplines are set out, namely: the aim of the discipline of Form Formation is to teach students the objective laws of the organization of the form and form formation in the world, knowledge attained thus far by the natural sciences. The aim of the discipline Theory of Composition is to teach students the laws of human perception, knowledge attained by the humanities. The focus is on a number of modern sciences, such as - in Form Formation - sciences of the organization of the matter in systems, structures, constructs and shapes; sciences of symmetry and rhythm; Synergetics; fractal geometry; bionics, etc. In Theory of Composition, these are respectively, psychology of perception, Gestalt psychology, semiotics, poetics, theory of manipulation, etc. Examples are given of the application of new teaching methods, including interactive forms of education and the organization of extracurricular events such as competitions, public readings, debating sessions, etc. The conclusions stress the importance of keeping the two disciplines up-to-date with the developments in modern science, and respectively, with the new requirements regarding design.
Review of Author Name Disambiguation Techniques for Citation Analysis

In citation analysis, author names are often used as the unit of analysis and some authors are indexed under the same name in bibliographic databases where the citation counts are obtained from. There are many techniques for author name disambiguation, using supervised, unsupervised, or semisupervised learning algorithms. Unsupervised approach uses machine learning algorithms to extract necessary bibliographic information from large-scale databases and digital libraries, while supervised approaches use manually built training datasets for clustering author groups for combining them with learning algorithms for author name disambiguation. The study examines various techniques for author name disambiguation in the hope for finding an aid to improve the precision of citation counts in citation analysis, as well as for better results in information retrieval.
Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data

In practical scenario, relation extraction needs to first identify entity pairs that have relation and then assign a correct relation class. However, the number of non-relation entity pairs in context (negative instances) usually far exceeds the others (positive instances), which negatively affects a model’s performance. To mitigate this problem, we propose a multi-task architecture which jointly trains a model to perform relation identification with cross-entropy loss and relation classification with ranking loss. Meanwhile, we observe that a sentence may have multiple entities and relation mentions, and the patterns in which the entities appear in a sentence may contain useful semantic information that can be utilized to distinguish between positive and negative instances. Thus we further incorporate the embeddings of character-wise/word-wise BIO tag from the named entity recognition task into character/word embeddings to enrich the input representation. Experiment results show that our proposed approach can significantly improve the performance of a baseline model with more than 10% absolute increase in F1-score, and outperform the state-of-the-art models on ACE 2005 Chinese and English corpus. Moreover, BIO tag embeddings are particularly effective and can be used to improve other models as well.
Reducing test-data volume and test-power simultaneously in LFSR reseeding-based compression environment

This paper presents a new test scheme based on scan block encoding in a linear feedback shift register (LFSR) reseeding-based compression environment. Meanwhile, our paper also introduces a novel algorithm of scan-block clustering. The main contribution of this paper is a flexible test-application framework that achieves significant reductions in switching activity during scan shift and the number of specified bits that need to be generated via LFSR reseeding. Thus, it can significantly reduce the test power and test data volume. Experimental results using Mintest test set on the larger ISCAS'89 benchmarks show that the proposed method reduces the switching activity significantly by 72%–94% and provides a best possible test compression of 74%–94% with little hardware overhead.
The REASONS Survey: Resolved Millimeter Observations of a Large Debris Disk around the Nearby F Star HD 170773

Debris disks are extrasolar analogs to our own Kuiper Belt and they are detected around at least 17% of nearby Sun-like stars. The morphology and dynamics of a disk encode information about its history, as well as that of any exoplanets within the system. We used the Atacama Large Millimeter/submillimeter Array (ALMA) to obtain 1.3 mm observations of the debris disk around the nearby F5V star HD 170773. We image the face-on ring and determine its fundamental parameters by forward-modeling the interferometric visibilities through a Markov Chain Monte Carlo approach. Using a symmetric Gaussian surface density profile, we find a 71 ± 4 au wide belt with a radius of au, a relatively large radius compared with most other millimeter-resolved belts around late A/early F type stars. This makes HD 170773 part of a group of four disks around A and F stars with radii larger than expected from the recently reported planetesimal belt radius—stellar luminosity relation. Two of these systems are known to host directly imaged giant planets, which may point to a connection between large belts and the presence of long-period giant planets. We also set upper limits on the presence of CO and CN gas in the system, which imply that the exocomets that constitute this belt have CO and HCN ice mass fractions of <77% and <3%, respectively. This is consistent with solar system comets and other exocometary belts.
Cylindrically Symmetric Spiraling Accretion in Power-law and Logarithmic Potentials

We study cylindrically symmetric steady-state accretion of polytropic test matter spiraling onto the symmetry axis in power-law and logarithmic potentials. The model allows one to qualitatively understand the accretion process in a symmetry different from that of the classical Bondi accretion. We study the integral curves as level lines of some Hamiltonian and also apply this method to Bondi accretion. The isothermal solutions in power-law potentials (as well as in any radius-dependent potential) can be expressed in exact form in terms of the Lambert W function, while in the case of logarithmic potential, exact solutions can be found for any polytropic exponent.
Search for gas from the disintegrating rocky exoplanet K2-22b

Context. The red dwarf star K2-22 is transited every 9.14 h by an object which is best explained by being a disintegrating rocky exoplanet featuring a variable comet-like dust tail. While the dust is thought to dominate the transit light curve, gas is also expected to be present, either from being directly evaporated off the planet or by being produced by the sublimation of dust particles in the tail. Aims. Both ionized calcium and sodium have large cross-sections, and although present at low abundance, exhibit the strongest atomic absorption features in comets. We therefore also identify these species as the most promising tracers of circumplanetary gas in evaporating rocky exoplanets and search for them in the tail of K2-22 b to constrain the gas-loss and sublimation processes in this enigmatic object. Methods. We observed four transits of K2-22 b with X-shooter on the Very Large Telescope operated by ESO to obtain time series of intermediate-resolution (R ~ 11 400) spectra. Our analysis focussed on the two sodium D lines (588.995 and 589.592 nm) and the Ca+ triplet (849.802, 854.209, and 866.214 nm). The stellar calcium and sodium absorption was removed using the out-of-transit spectra. We searched for planet-related absorption in the velocity rest frame of the planet, which changes from approximately −66 to +66 km s−1 during the transit. Results. Since K2-22 b exhibits highly variable transit depths, we analysed the individual nights and their average. By injecting signals we reached 5σ upper limits on the individual nights that range from 11–13% and 1.7–2.0% for the sodium and ionized calcium absorption of the tail, respectively. Night 1 was contaminated by its companion star so we considered weighted averages with and without Night 1 and quote conservative 5σ limits without Night 1 of 9 and 1.4%, respectively. Assuming their mass fractions to be similar to those in the Earth’s crust, these limits correspond to scenarios in which 0.04 and 35% of the transiting dust is sublimated and observed as absorbing gas. However, this assumes the gas to be co-moving with the planet. We show that for the high irradiation environment of K2-22 b, sodium and ionized calcium could be quickly accelerated to 100s of km s−1 owing to radiation pressure and entrainment by the stellar wind, making these species much more difficult to detect. No evidence for such possibly broad and blue-shifted signals are seen in our data. Conclusions. Future observations aimed at observing circumplanetary gas should take into account the possible broad and blue-shifted velocity field of atomic and ionized species.
Evolving Self-supervised Neural Networks: Autonomous Intelligence from Evolved Self-teaching

This paper presents a technique called evolving self-supervised neural networks - neural networks that can teach themselves, intrinsically motivated, without external supervision or reward. The proposed method presents some sort-of paradigm shift, and differs greatly from both traditional gradient-based learning and evolutionary algorithms in that it combines the metaphor of evolution and learning, more specifically self-learning, together, rather than treating these phenomena alternatively. I simulate a multi-agent system in which neural networks are used to control autonomous foraging agents with little domain knowledge. Experimental results show that only evolved self-supervised agents can demonstrate some sort of intelligent behaviour, but not evolution or self-learning alone. Indications for future work on evolving intelligence are also presented.
Estimation of a Low-rank Topic-Based Model for Information Cascades

We consider the problem of estimating the latent structure of a social network based on the observed information diffusion events, or cascades, where the observations for a given cascade consist of only the timestamps of infection for infected nodes but not the source of the infection. Most of the existing work on this problem has focused on estimating a diffusion matrix without any structural assumptions on it. In this paper, we propose a novel model based on the intuition that an information is more likely to propagate among two nodes if they are interested in similar topics which are also prominent in the information content. In particular, our model endows each node with an influence vector (which measures how authoritative the node is on each topic) and a receptivity vector (which measures how susceptible the node is for each topic). We show how this node-topic structure can be estimated from the observed cascades, and prove the consistency of the estimator. Experiments on synthetic and real data demonstrate the improved performance and better interpretability of our model compared to existing state-of-the-art methods.
Consistent KK truncations for M5-branes wrapped on Riemann surfaces

We construct a consistent Kaluza–Klein reduction of D = 11 supergravity on , where or H2, or a quotient thereof, at the level of the bosonic fields. The result is a gauged N = 4, D = 5 supergravity theory coupled to three vector multiplets, with the gauging lying in an subgroup of the global symmetry group of the ungauged theory. For , the D = 5 theory has a maximally supersymmetric AdS5 vacuum which uplifts to the known solution of D = 11 supergravity corresponding to M5-branes wrapping a Riemann surface with genus greater than one and dual to an N = 2 SCFT in d = 4. For , we find two AdS5 solutions, one of which is new, and both of which are unstable. There is an additional subtruncation to an N = 2 gauged supergravity coupled to two vector multiplets, with very special real manifold , and a single hypermultiplet, with quaternionic Kähler manifold and gauging associated with an subgroup.
Reconstructing the spectral shape of a stochastic gravitational wave background with LISA

We present a set of tools to assess the capabilities of LISA to detect and reconstruct the spectral shape and amplitude of a stochastic gravitational wave background (SGWB) . We first provide the LISA power-law sensitivity curve and binned power-law sensitivity curves, based on the latest updates on the LISA design. These curves are useful to make a qualitative assessment of the detection and reconstruction prospects of a SGWB . For a quantitative reconstruction of a SGWB with arbitrary power spectrum shape, we propose a novel data analysis technique: by means of an automatized adaptive procedure, we conveniently split the LISA sensitivity band into frequency bins, and fit the data inside each bin with a power law signal plus a model of the instrumental noise. We apply the procedure to SGWB signals with a variety of representative frequency profiles, and prove that LISA can reconstruct their spectral shape. Our procedure, implemented in the code SGWBinner, is suitable for homogeneous and isotropic SGWBs detectable at LISA, and it is also expected to work for other GW observatories.
New Partial Symmetries from Group Algebras for Lepton Mixing

<jats:p>Recent stringent experiment data of neutrino oscillations induces partial symmetries such as <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" id="M1"><mml:msub><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math> and <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" id="M2"><mml:msub><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mo>×</mml:mo><mml:mtext>CP</mml:mtext></mml:math> to derive lepton mixing patterns. New partial symmetries expressed with elements of group algebras are studied. A specific lepton mixing pattern could correspond to a set of equivalent elements of a group algebra. The transformation which interchanges the elements could express a residual <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" id="M3"><mml:mtext>CP</mml:mtext></mml:math> symmetry. Lepton mixing matrices from <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" id="M4"><mml:msub><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msub></mml:math> group algebras are of the trimaximal form with the <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" id="M5"><mml:mi>μ</mml:mi><mml:mo>−</mml:mo><mml:mi>τ</mml:mi></mml:math> reflection symmetry. Accordingly, elements of <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" id="M6"><mml:msub><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msub></mml:math> group algebras are equivalent to <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" id="M7"><mml:msub><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mo>×</mml:mo><mml:mtext>CP</mml:mtext></mml:math>. Comments on <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" id="M8"><mml:msub><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msub></mml:math> group algebras are given. The predictions of <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" id="M9"><mml:msub><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mo>×</mml:mo><mml:mtext>CP</mml:mtext></mml:math> broken from the group <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" id="M10"><mml:msub><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msub></mml:math> with the generalized <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" id="M11"><mml:mtext>CP</mml:mtext></mml:math> symmetry are also obtained from elements of <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" id="M12"><mml:msub><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msub></mml:math> group algebras. </jats:p>
Evolution Attack On Neural Networks

Many studies have been done to prove the vulnerability of neural networks to adversarial example. A trained and well-behaved model can be fooled by a visually imperceptible perturbation, i.e., an originally correctly classified image could be misclassified after a slight perturbation. In this paper, we propose a black-box strategy to attack such networks using an evolution algorithm. First, we formalize the generation of an adversarial example into the optimization problem of perturbations that represent the noise added to an original image at each pixel. To solve this optimization problem in a black-box way, we find that an evolution algorithm perfectly meets our requirement since it can work without any gradient information. Therefore, we test various evolution algorithms, including a simple genetic algorithm, a parameter-exploring policy gradient, an OpenAI evolution strategy, and a covariance matrix adaptive evolution strategy. Experimental results show that a covariance matrix adaptive evolution Strategy performs best in this optimization problem. Additionally, we also perform several experiments to explore the effect of different regularizations on improving the quality of an adversarial example.
Large gaps between sums of two squares

Let $\mathcal S=\{s_1<s_2<s_3<\ldots\}$ be the sequence of all natural numbers which can be represented as a sum of two squares of integers. For $X\ge2$ we denote by $g(X)$ the largest gap between consecutive elements of $\mathcal S$ that do not exceed $X$. We prove that for $X \to +\infty$ the lower bound $$g(X)\geq \left(\frac{390}{449}-o(1)\right)\ln X$$ holds. This estimate is twice the recent estimate by R. Dietmann and C. Elsholtz.
A Novel Fuzzy Search Approach over Encrypted Data with Improved Accuracy and Efficiency

As cloud computing becomes prevalent in recent years, more and more enterprises and individuals outsource their data to cloud servers. To avoid privacy leaks, outsourced data usually is encrypted before being sent to cloud servers, which disables traditional search schemes for plain text. To meet both end of security and searchability, search-supported encryption is proposed. However, many previous schemes suffer severe vulnerability when typos and semantic diversity exist in query requests. To overcome such flaw, higher error-tolerance is always expected for search-supported encryption design, sometimes defined as 'fuzzy search'. In this paper, we propose a new scheme of multi-keyword fuzzy search over encrypted and outsourced data. Our approach introduces a new mechanism to map a natural language expression into a word-vector space. Compared with previous approaches, our design shows higher robustness when multiple kinds of typos are involved. Besides, our approach is enhanced with novel data structures to improve search efficiency. These two innovations can work well for both accuracy and efficiency. Moreover, these designs will not hurt the fundamental security. Experiments on a real-world dataset demonstrate the effectiveness of our proposed approach, which outperforms currently popular approaches focusing on similar tasks.
Mediation analysis for zero-inﬂated mediators with applications to microbiome data

Zero-inﬂated data is commonly seen in biomedical research such as microbiome studies and single-cell sequencing studies where zero-valued sequencing reads arise due to technical and/or biological reasons. Mediation analysis approaches for analyzing zero-inﬂated mediators are still lacking largely because of challenges raised by the zero-inﬂated data structure: (a) disentangling the mediation eﬀect induced by the point mass at zero is not straightforward; and (b) identifying the observed zero-valued data points that are actually not zero (i
Singing Voice Synthesis Using Deep Autoregressive Neural Networks for Acoustic Modeling

This paper presents a method of using autoregressive neural networks for the acoustic modeling of singing voice synthesis (SVS). Singing voice differs from speech and it contains more local dynamic movements of acoustic features, e.g., vibratos. Therefore, our method adopts deep autoregressive (DAR) models to predict the F0 and spectral features of singing voice in order to better describe the dependencies among the acoustic features of consecutive frames. For F0 modeling, discretized F0 values are used and the influences of the history length in DAR are analyzed by experiments. An F0 post-processing strategy is also designed to alleviate the inconsistency between the predicted F0 contours and the F0 values determined by music notes. Furthermore, we extend the DAR model to deal with continuous spectral features, and a prenet module with self-attention layers is introduced to process historical frames. Experiments on a Chinese singing voice corpus demonstrate that our method using DARs can produce F0 contours with vibratos effectively, and can achieve better objective and subjective performance than the conventional method using recurrent neural networks (RNNs).
Local Online Motor Babbling: Learning Motor Abundance of a Musculoskeletal Robot Arm*

Motor babbling and goal babbling has been used for sensorimotor learning of highly redundant systems in soft robotics. Recent works in goal babbling have demonstrated successful learning of inverse kinematics (IK) on such systems, and suggest that babbling in the goal space better resolves motor redundancy by learning as few yet efficient sensorimotor mappings as possible. However, for musculoskeletal robot systems, motor redundancy can provide useful information to explain muscle activation patterns, thus the term motor abundance. In this work, we introduce some simple heuristics to empirically define the unknown goal space, and learn the IK of a 10 DoF musculoskeletal robot arm using directed goal babbling. We then further propose local online motor babbling guided by Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which bootstraps on the goal babbling samples for initialization, such that motor abundance can be queried online for any static goal. Our approach leverages the resolving of redundancies and the efficient guided exploration of motor abundance in two stages of learning, allowing both kinematic accuracy and motor variability at the queried goal. The result shows that local online motor babbling guided by CMA-ES can efficiently explore motor abundance at queried goal positions on a musculoskeletal robot system and gives useful insights in terms of muscle stiffness and synergy.
Mitigating Gender Bias in Natural Language Processing: Literature Review

As Natural Language Processing (NLP) and Machine Learning (ML) tools rise in popularity, it becomes increasingly vital to recognize the role they play in shaping societal biases and stereotypes. Although NLP models have shown success in modeling various applications, they propagate and may even amplify gender bias found in text corpora. While the study of bias in artificial intelligence is not new, methods to mitigate gender bias in NLP are relatively nascent. In this paper, we review contemporary studies on recognizing and mitigating gender bias in NLP. We discuss gender bias based on four forms of representation bias and analyze methods recognizing gender bias. Furthermore, we discuss the advantages and drawbacks of existing gender debiasing methods. Finally, we discuss future studies for recognizing and mitigating gender bias in NLP.