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The mysterious metallic phase showing perfect $T$-linear resistivity and a universal scattering rate $1/\tau = \alpha_P k_B T /\hbar$ with a universal prefactor $\alpha_P \sim 1$ and logarithmic-in-temperature singular specific heat coefficient, so-called Planckian metal phase was observed in various overdoped high-$T_c$ cuprate superconductors over a finite range in doping. Here, we propose a microscopic mechanism for this exotic state based on quantum-critical bosonic charge Kondo fluctuations coupled to both spinon and a heavy conduction-electron Fermi surfaces within the heavy-fermion formulation of the slave-boson $t$-$J$ model. Using a controlled perturbative renormalization group (RG) analysis, we examine the competition between the pseudogap phase, characterized by Anderson's Resonating-Valence-Bond spin-liquid, and the Fermi-liquid state, characterized by the electron hoping (effective charge Kondo effect). We find a quantum-critical metallic phase with a universal Planckian $\hbar \omega/k_B T$ scaling in scattering rate near a localized-delocalized (pseudogap-to-Fermi liquid) charge Kondo breakdown transition. Our results are in excellent agreement with the recent experimental observations on optical conductivity (without fine-tuning) in Nat. Commun. 14, 3033 (2023), universal doping-independent field-to-temperature scaling in magnetoresistance in Nature 595, 661 (2021), and the marginal Fermi-liquid spectral function observed in ARPES (Science 366, 1099 (2019)) as well as Hall coefficient in various overdoped cuprates in Nature 595, 661 (2021) and Annu. Rev. Condens. Matter Phys. 10, 409 (2019). Our mechanism offers a microscopic understanding of the quantum-critical Planckian metal phase observed in cuprates d-wave superconducting, and Fermi liquid phases., Comment: 43 pages, 13 figures

Singing technique conversion (STC) refers to the task of converting from one voice technique to another while leaving the original singer identity, melody, and linguistic components intact. Previous STC studies, as well as singing voice conversion research in general, have utilized convolutional autoencoders (CAEs) for conversion, but how the bottleneck width of the CAE affects the synthesis quality has not been thoroughly evaluated. To this end, we constructed a GAN-based multi-domain STC system which took advantage of the WORLD vocoder representation and the CAE architecture. We varied the bottleneck width of the CAE, and evaluated the conversion results subjectively. The model was trained on a Mandarin dataset which features four singers and four singing techniques: the chest voice, the falsetto, the raspy voice, and the whistle voice. The results show that a wider bottleneck corresponds to better articulation clarity but does not necessarily lead to higher likeness to the target technique. Among the four techniques, we also found that the whistle voice is the easiest target for conversion, while the other three techniques as a source produce more convincing conversion results than the whistle., Comment: The original edition of this paper will be published in the CMMR 2023 Proceedings. This ArXiv publication is a copy

Spin-triplet $p$-wave superconductors are promising candidates for topological superconductors. They have been proposed in various heterostructures where a material with strong spin-orbit interaction is coupled to a conventional $s$-wave superconductor by proximity effect. However, topological superconductors existing in nature and driven purely by strong electron correlations are yet to be studied. Here we propose a realization of such a system in a class of Kondo lattice materials in the absence of spin-orbit coupling and proximity effect. Therein, the odd-parity Kondo hybridization mediates ferromagnetic spin-spin coupling and leads to spin-triplet resonant-valence-bond ($t$-RVB) pairing between local moments. Spin-triplet $p\pm i p^\prime$-wave topological superconductivity is reached when Kondo effect co-exists with $t$-RVB. We identify the topological nature by the non-trivial topological invariant and the Majorana fermions at edges. Our results offer a comprehensive understanding of experimental observations on UTe$_2$, a U-based ferromagnetic heavy-electron superconductor., Comment: 13 pages, 7 figures

The COVID-19 pandemic has been severely impacting global society since December 2019. Massive research has been undertaken to understand the characteristics of the virus and design vaccines and drugs. The related findings have been reported in biomedical literature at a rate of about 10,000 articles on COVID-19 per month. Such rapid growth significantly challenges manual curation and interpretation. For instance, LitCovid is a literature database of COVID-19-related articles in PubMed, which has accumulated more than 200,000 articles with millions of accesses each month by users worldwide. One primary curation task is to assign up to eight topics (e.g., Diagnosis and Treatment) to the articles in LitCovid. Despite the continuing advances in biomedical text mining methods, few have been dedicated to topic annotations in COVID-19 literature. To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature. The BioCreative LitCovid dataset, consisting of over 30,000 articles with manually reviewed topics, was created for training and testing. It is one of the largest multilabel classification datasets in biomedical scientific literature. 19 teams worldwide participated and made 80 submissions in total. Most teams used hybrid systems based on transformers. The highest performing submissions achieved 0.8875, 0.9181, and 0.9394 for macro F1-score, micro F1-score, and instance-based F1-score, respectively. The level of participation and results demonstrate a successful track and help close the gap between dataset curation and method development. The dataset is publicly available via https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/ for benchmarking and further development.

Perfect $T$-linear resistivity associated with universal scattering rate: $1/\tau =\alpha k_B T/\hbar$ with $\alpha \sim 1$, so-called Planckian metal state, has been observed in the normal state of a variety of strongly correlated superconductors close to a quantum critical point. However, the microscopic origin of this intriguing phenomena and its link to quantum criticality still remains an outstanding open problem. In this work, we observe the quantum-critical $T/B$-scaling of the Planckian metal state in the resistivity and heat capacity of heavy-electron superconductor Ce$_{1-x}$Nd$_x$CoIn$_5$ in magnetic fields near the edge of antiferromagnetism, driven by critical Kondo hybridization at the critical doping $x_c \sim 0.03$. We further provide the first microscopic mechanism to account for the Planckian state in a quantum critical system based on the critical charge fluctuations near Kondo breakdown transition at $x_c$ within the quasi-two-dimensional Kondo-Heisenberg lattice model. This mechanism simultaneously captures the observed universal Planckian scattering rate as well as the quantum-critical scaling and power-law divergence in thermodynamic observables near criticality. Our mechanism is generic to Planckian metal states in a variety of quantum critical superconductors near Kondo destruction., Comment: 14 pages, 7 figure, 1 table

Singing voice synthesis (SVS) is a task that aims to generate audio signals according to musical scores and lyrics. With its multifaceted nature concerning music and language, producing singing voices indistinguishable from that of human singers has always remained an unfulfilled pursuit. Nonetheless, the advancements of deep learning techniques have brought about a substantial leap in the quality and naturalness of synthesized singing voice. This paper aims to review some of the state-of-the-art deep learning-driven SVS systems. We intend to summarize their deployed model architectures and identify the strengths and limitations for each of the introduced systems. Thereby, we picture the recent advancement trajectory of this field and conclude the challenges left to be resolved both in commercial applications and academic research.