Number Sequence Prediction Problems for Evaluating Computational Powers of Neural Networks

Inspired by number series tests to measure human intelligence, we suggest number sequence prediction tasks to assess neural network models' computational powers for solving algorithmic problems. We define the complexity and difficulty of a number sequence prediction task with the structure of the smallest automaton that can generate the sequence. We suggest two types of number sequence prediction problems: the number-level and the digit-level problems. The number-level problems format sequences as 2-dimensional grids of digits and the digit-level problems provide a single digit input per a time step. The complexity of a number-level sequence prediction can be defined with the depth of an equivalent combinatorial logic, and the complexity of a digit-level sequence prediction can be defined with an equivalent state automaton for the generation rule. Experiments with number-level sequences suggest that CNN models are capable of learning the compound operations of sequence generation rules, but the depths of the compound operations are limited. For the digit-level problems, simple GRU and LSTM models can solve some problems with the complexity of finite state automata. Memory augmented models such as Stack-RNN, Attention, and Neural Turing Machines can solve the reverse-order task which has the complexity of simple pushdown automaton. However, all of above cannot solve general Fibonacci, Arithmetic or Geometric sequence generation problems that represent the complexity of queue automata or Turing machines. The results show that our number sequence prediction problems effectively evaluate machine learning models' computational capabilities.Comment: Accepted to 2019 AAAI Conference on Artificial Intelligenc

Lattice-coupled Antiferromagnet on Frustrated Lattices

Lattice-coupled antiferromagnetic spin model is analyzed for a number of frustrated lattices: triangular, Kagome, and pyrochlore. In triangular and Kagome lattices where ground state spins are locally ordered, the spin-lattice interaction does not lead to a static deformation of the lattice. In the pyrochlore structure, spin-lattice coupling supports a picture of the hexagon spin cluster proposed in the recent experiment[S. H. Lee et al. Nature, 418, 856 (2002)]. Through spin-lattice interaction a uniform contraction of the individual hexagons in the pyrochlore lattice can take place and reduce the exchange energy. Residual hexagon-hexagon interaction takes the form of a 3-states Potts model where the preferred directions of the spin-loop directors for nearby hexagons are mutually orthogonal

Neural Sequence-to-grid Module for Learning Symbolic Rules

Logical reasoning tasks over symbols, such as learning arithmetic operations and computer program evaluations, have become challenges to deep learning. In particular, even state-of-the-art neural networks fail to achieve \textit{out-of-distribution} (OOD) generalization of symbolic reasoning tasks, whereas humans can easily extend learned symbolic rules. To resolve this difficulty, we propose a neural sequence-to-grid (seq2grid) module, an input preprocessor that automatically segments and aligns an input sequence into a grid. As our module outputs a grid via a novel differentiable mapping, any neural network structure taking a grid input, such as ResNet or TextCNN, can be jointly trained with our module in an end-to-end fashion. Extensive experiments show that neural networks having our module as an input preprocessor achieve OOD generalization on various arithmetic and algorithmic problems including number sequence prediction problems, algebraic word problems, and computer program evaluation problems while other state-of-the-art sequence transduction models cannot. Moreover, we verify that our module enhances TextCNN to solve the bAbI QA tasks without external memory.Comment: 9 pages, 9 figures, AAAI 202

Singlet Fermionic Dark Matter with Dark ZZ

We present a fermionic dark matter model mediated by the hidden gauge boson. We assume the QED-like hidden sector which consists of a Dirac fermion and U(1)$_X$ gauge symmetry, and introduce an additional scalar electroweak doublet field with the U(1)$_X$ charge as a mediator. The hidden U(1)$_X$ symmetry is spontaneously broken by the electroweak symmetry breaking and there exists a massive extra neutral gauge boson in this model which is the mediator between the hidden and visible sectors. Due to the U(1)$_X$ charge, the additional scalar doublet does not couple to the Standard Model fermions, which leads to the Higgs sector of type I two Higgs doublet model. The new gauge boson couples to the Standard Model fermions with couplings proportional to those of the ordinary $Z$ boson but very suppressed, thus we call it the dark $Z$ boson. We study the phenomenology of the dark $Z$ boson and the Higgs sector, and show the hidden fermion can be the dark matter candidate.Comment: 10 pages, 3 figure

Potassium-doped BaFe2As2 superconducting thin films with a transition temperature of 40 K

We report the growth of potassium-doped BaFe2As2 thin films, where the major charge carriers are holes, on Al2O3 (0001) and LaAlO3 (001) substrates by using an ex-situ pulsed laser deposition technique. The measured Tc's are 40 and 39 K for the films grown on Al2O3 and LaAlO3, respectively and diamagnetism indicates that the films have good bulk superconducting properties below 36 and 30 K, respectively. The X-ray diffraction patterns for both films indicated a preferred c-axis orientation, regardless of the substrate structures of LaAlO3 and Al2O3. The upper critical field at zero temperature was estimated to be about 155 T.Comment: 6 pages including 3 figure

K-pop Lyric Translation: Dataset, Analysis, and Neural-Modelling

Lyric translation, a field studied for over a century, is now attracting computational linguistics researchers. We identified two limitations in previous studies. Firstly, lyric translation studies have predominantly focused on Western genres and languages, with no previous study centering on K-pop despite its popularity. Second, the field of lyric translation suffers from a lack of publicly available datasets; to the best of our knowledge, no such dataset exists. To broaden the scope of genres and languages in lyric translation studies, we introduce a novel singable lyric translation dataset, approximately 89\% of which consists of K-pop song lyrics. This dataset aligns Korean and English lyrics line-by-line and section-by-section. We leveraged this dataset to unveil unique characteristics of K-pop lyric translation, distinguishing it from other extensively studied genres, and to construct a neural lyric translation model, thereby underscoring the importance of a dedicated dataset for singable lyric translations

Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles

We study contextual linear bandit problems under uncertainty on features; they are noisy with missing entries. To address the challenges from the noise, we analyze Bayesian oracles given observed noisy features. Our Bayesian analysis finds that the optimal hypothesis can be far from the underlying realizability function, depending on noise characteristics, which is highly non-intuitive and does not occur for classical noiseless setups. This implies that classical approaches cannot guarantee a non-trivial regret bound. We thus propose an algorithm aiming at the Bayesian oracle from observed information under this model, achieving $\tilde{O}(d\sqrt{T})$ regret bound with respect to feature dimension $d$ and time horizon $T$. We demonstrate the proposed algorithm using synthetic and real-world datasets.Comment: 30 page

In-situ fabrication of cobalt-doped SrFe2As2 thin films by using pulsed laser deposition with excimer laser

The remarkably high superconducting transition temperature and upper critical field of iron(Fe)-based layered superconductors, despite ferromagnetic material base, open the prospect for superconducting electronics. However, success in superconducting electronics has been limited because of difficulties in fabricating high-quality thin films. We report the growth of high-quality c-axis-oriented cobalt(Co)-doped SrFe2As2 thin films with bulk superconductivity by using an in-situ pulsed laser deposition technique with a 248-nm-wavelength KrF excimer laser and an arsenic(As)-rich phase target. The temperature and field dependences of the magnetization showing strong diamagnetism and transport critical current density with superior Jc-H performance are reported. These results provide necessary information for practical applications of Fe-based superconductors.Comment: 8 pages, 3figures. to be published at Appl. Phys. Let