TUNet: A Block-online Bandwidth Extension Model based on Transformers and Self-supervised Pretraining

We introduce a block-online variant of the temporal feature-wise linear modulation (TFiLM) model to achieve bandwidth extension. The proposed architecture simplifies the UNet backbone of the TFiLM to reduce inference time and employs an efficient transformer at the bottleneck to alleviate performance degradation. We also utilize self-supervised pretraining and data augmentation to enhance the quality of bandwidth extended signals and reduce the sensitivity with respect to downsampling methods. Experiment results on the VCTK dataset show that the proposed method outperforms several recent baselines in both intrusive and non-intrusive metrics. Pretraining and filter augmentation also help stabilize and enhance the overall performance.Comment: Published as a conference paper at ICASSP 2022, 5 pages, 4 figures, 3 table

Fermion masses in the economical 3-3-1 model

We show that, in frameworks of the economical 3-3-1 model, all fermions get masses. At the tree level, one up-quark and two down-quarks are massless, but the one-loop corrections give all quarks the consistent masses. This conclusion is in contradiction to the previous analysis in which, the third scalar triplet has been introduced. This result is based on the key properties of the model: First, there are three quite different scales of vacuum expectation values: \om \sim {\cal O}(1) \mathrm{TeV}, v \approx 246 \mathrm{GeV} and $u \sim {\cal O}(1) \mathrm{GeV}$. Second, there exist two types of Yukawa couplings with different strengths: the lepton-number conserving couplings $h$'s and the lepton-number violating ones $s$'s satisfying the condition in which the second are much smaller than the first ones: $s \ll h$. With the acceptable set of parameters, numerical evaluation shows that in this model, masses of the exotic quarks also have different scales, namely, the $U$ exotic quark ($q_U = 2/3$) gains mass $m_U \approx 700$ GeV, while the D_\al exotic quarks (q_{D_\al} = -1/3) have masses in the TeV scale: m_{D_\al} \in 10 \div 80 TeV.Comment: 20 pages, 8 figure

Channel and spatial attention mechanism for fashion image captioning

Image captioning aims to automatically generate one or more description sentences for a given input image. Most of the existing captioning methods use encoder-decoder model which mainly focus on recognizing and capturing the relationship between objects appearing in the input image. However, when generating captions for fashion images, it is important to not only describe the items and their relationships, but also mention attribute features of clothes (shape, texture, style, fabric, and more). In this study, one novel model is proposed for fashion image captioning task which can capture not only the items and their relationship, but also their attribute features. Two different attention mechanisms (spatial-attention and channel-wise attention) is incorporated to the traditional encoder-decoder model, which dynamically interprets the caption sentence in multi-layer feature map in addition to the depth dimension of the feature map. We evaluate our proposed architecture on Fashion-Gen using three different metrics (CIDEr, ROUGE-L, and BLEU-1), and achieve the scores of 89.7, 50.6 and 45.6, respectively. Based on experiments, our proposed method shows significant performance improvement for the task of fashion-image captioning, and outperforms other state-of-the-art image captioning methods

Conditional Support Alignment for Domain Adaptation with Label Shift

Unsupervised domain adaptation (UDA) refers to a domain adaptation framework in which a learning model is trained based on the labeled samples on the source domain and unlabelled ones in the target domain. The dominant existing methods in the field that rely on the classical covariate shift assumption to learn domain-invariant feature representation have yielded suboptimal performance under the label distribution shift between source and target domains. In this paper, we propose a novel conditional adversarial support alignment (CASA) whose aim is to minimize the conditional symmetric support divergence between the source's and target domain's feature representation distributions, aiming at a more helpful representation for the classification task. We also introduce a novel theoretical target risk bound, which justifies the merits of aligning the supports of conditional feature distributions compared to the existing marginal support alignment approach in the UDA settings. We then provide a complete training process for learning in which the objective optimization functions are precisely based on the proposed target risk bound. Our empirical results demonstrate that CASA outperforms other state-of-the-art methods on different UDA benchmark tasks under label shift conditions

Superconductivity under pressure in the Dirac semimetal PdTe2

The Dirac semimetal PdTe$_2$ was recently reported to be a type-I superconductor ($T_c =$1.64 K, $\mu_0 H_c (0) = 13.6$ mT) with unusual superconductivity of the surface sheath. We here report a high-pressure study, $p \leq 2.5$ GPa, of the superconducting phase diagram extracted from ac-susceptibility and transport measurements on single crystalline samples. $T_c (p)$ shows a pronounced non-monotonous variation with a maximum $T_c =$1.91 K around 0.91 GPa, followed by a gradual decrease to 1.27 K at 2.5 GPa. The critical field of bulk superconductivity in the limit $T \rightarrow 0$, $H_c(0,p)$, follows a similar trend and consequently the $H_c(T,p)$-curves under pressure collapse on a single curve: $H_c(T,p)=H_c(0,p)[1-(T/T_c(p))^2]$. Surface superconductivity is robust under pressure as demonstrated by the large superconducting screening signal that persists for applied dc-fields $H_a > H_c$. Surprisingly, for $p \geq 1.41$ GPa the superconducting transition temperature at the surface $T_c^S$ is larger than $T_c$ of the bulk. Therefore surface superconductivity may possibly have a non-trivial nature and is connected to the topological surface states detected by ARPES. We compare the measured pressure variation of $T_c$ with recent results from band structure calculations and discuss the importance of a Van Hove singularity.Comment: manuscript 9 pages with 8 figures + supplemental material 3 pages with 6 figure

Anisotropic Magneto-Thermopower: the Contribution of Interband Relaxation

Spin injection in metallic normal/ferromagnetic junctions is investigated taking into account the anisotropic magnetoresistance (AMR) occurring in the ferromagnetic layer. It is shown, on the basis of a generalized two channel model, that there is an interface resistance contribution due to anisotropic scattering, beyond spin accumulation and giant magnetoresistance (GMR). The corresponding expression of the thermopower is derived and compared with the expression for the thermopower produced by the GMR. First measurements of anisotropic magnetothermopower are presented in electrodeposited Ni nanowires contacted with Ni, Au and Cu. The results of this study show that while the giant magnetoresistance and corresponding thermopower demonstrates the role of spin-flip scattering, the observed anisotropic magnetothermopower indicates interband s-d relaxation mechanisms.Comment: 20 pages, 4 figure

The Higgs sector in the minimal 3-3-1 model with the most general lepton-number conserving potential

The Higgs sector of the minimal 3 - 3 - 1 model with three triplets and one sextet is investigated in detail under the most general lepton--number conserving potential. The mass spectra and multiplet decompostion structure are explicitly given in a systematic order and a transparent way allowing they to be easily checked and used in further investigations. A previously arising problem of inconsistent signs of f_{2} is also automatically solved