Miracle-dz/UniFAR-FTUs huggingface dataset:
Noise Robust TTS for Low Resource Speakers using Pre-trained Model and Speech Enhancement

With the popularity of deep neural network, speech synthesis task has achieved significant improvements based on the end-to-end encoder-decoder framework in the recent days. More and more applications relying on speech synthesis technology have been widely used in our daily life. Robust speech synthesis model depends on high quality and customized data which needs lots of collecting efforts. It is worth investigating how to take advantage of low-quality and low resource voice data which can be easily obtained from the Internet for usage of synthesizing personalized voice. In this paper, the proposed end-to-end speech synthesis model uses both speaker embedding and noise representation as conditional inputs to model speaker and noise information respectively. Firstly, the speech synthesis model is pre-trained with both multi-speaker clean data and noisy augmented data; then the pre-trained model is adapted on noisy low-resource new speaker data; finally, by setting the clean speech condition, the model can synthesize the new speaker's clean voice. Experimental results show that the speech generated by the proposed approach has better subjective evaluation results than the method directly fine-tuning pre-trained multi-speaker speech synthesis model with denoised new speaker data.
Fractional Coulomb blockade for quasi-particle tunneling between edge channels

Magnetic field periodic conductance of a quantum dot in fractional quantum Hall regime indicates fractional charge tunneling. In the fractional quantum Hall effect, the elementary excitations are quasi-particles with fractional charges as predicted by theory and demonstrated by noise and interference experiments. We observe Coulomb blockade of fractional charges in the measured magneto-conductance of a 1.4-micron-wide quantum dot. Interaction-driven edge reconstruction separates the dot into concentric compressible regions with fractionally charged excitations and incompressible regions acting as tunnel barriers for quasi-particles. Our data show the formation of incompressible regions of filling factors 2/3 and 1/3. Comparing data at fractional filling factors to filling factor 2, we extract the fractional quasi-particle charge e*/e = 0.32 ± 0.03 and 0.35 ± 0.05. Our investigations extend and complement quantum Hall Fabry-Pérot interference experiments investigating the nature of anyonic fractional quasi-particles.
GECToR – Grammatical Error Correction: Tag, Not Rewrite

In this paper, we present a simple and efficient GEC sequence tagger using a Transformer encoder. Our system is pre-trained on synthetic data and then fine-tuned in two stages: first on errorful corpora, and second on a combination of errorful and error-free parallel corpora. We design custom token-level transformations to map input tokens to target corrections. Our best single-model/ensemble GEC tagger achieves an F_0. 5 of 65.3/66.5 on CONLL-2014 (test) and F_0. 5 of 72.4/73.6 on BEA-2019 (test). Its inference speed is up to 10 times as fast as a Transformer-based seq2seq GEC system.
No silver bullets in correlative ecological niche modelling: insights from testing among many potential algorithms for niche estimation

The field of ecological niche modelling or species distribution modelling has seen enormous activity and attention in recent years, in the light of exciting biological inferences that can be drawn from correlational models of species' environmental requirements (i.e. ecological niches) and inferences of potential geographic distributions. Among the many methods used in the field, one or two are in practice assumed to be ‘best’ and are used commonly, often without explicit testing. We explore herein implications of the ‘no free lunch’ theorem, which suggests that no single optimization approach will prove to be best under all circumstances: we developed diverse virtual species with known niche and dispersal properties to test a suite of niche modelling algorithms designed to estimate potential areas of distribution. The result was that (i) indeed, no single ‘best’ algorithm was found and (ii) different algorithms performing very different manners depending on the particularities of the virtual species. The conclusion is that niche or distribution modelling studies should begin by testing a suite of algorithms for predictive ability under the particular circumstances of the study and choose an algorithm for a particular challenge based on the results of those tests. Studies that do not take this step may use algorithms that are not optimal for that particular challenge.
What am I Searching for: Zero-shot Target Identity Inference in Visual Search

Can we infer intentions from a person's actions? As an example problem, here we consider how to decipher what a person is searching for by decoding their eye movement behavior. We conducted two psychophysics experiments where we monitored eye movements while subjects searched for a target object. We defined the fixations falling on non-target objects as "error fixations". Using those error fixations, we developed a model (InferNet) to infer what the target was. InferNet uses a pre-trained convolutional neural network to extract features from the error fixations and computes a similarity map between the error fixations and all locations across the search image. The model consolidates the similarity maps across layers and integrates these maps across all error fixations. InferNet successfully identifies the subject's goal and outperforms competitive null models, even without any object-specific training on the inference task.
MVIN: Learning Multiview Items for Recommendation

Researchers have begun to utilize heterogeneous knowledge graphs(KGs) as auxiliary information in recommendation systems to mitigate the cold start and sparsity issues. However, utilizing a graph neural network (GNN) to capture information in KG and further apply in RS is still problematic as it is unable to see each item's properties from multiple perspectives. To address these issues, we propose the multi-view item network (MVIN), a GNN-based recommendation model that provides superior recommendations by describing items from a unique mixed view from user and entity angles. MVIN learns item representations from both the user view and the entity view. From the user view, user-oriented modules score and aggregate features to make recommendations from a personalized perspective constructed according to KG entities which incorporates user click information. From the entity view, the mixing layer contrasts layer-wise GCN information to further obtain comprehensive features from internal entity-entity interactions in the KG. We evaluate MVIN on three real-world datasets: MovieLens-1M (ML-1M), LFM-1b 2015 (LFM-1b), and Amazon-Book (AZ-book). Results show that MVIN significantly outperforms state-of-the-art methods on these three datasets. Besides, from user-view cases, we find that MVIN indeed captures entities that attract users. Figures further illustrate that mixing layers in a heterogeneous KG plays a vital role in neighborhood information aggregation.
FT Speech: Danish Parliament Speech Corpus

This paper introduces FT Speech, a new speech corpus created from the recorded meetings of the Danish Parliament, otherwise known as the Folketing (FT). The corpus contains over 1,800 hours of transcribed speech by a total of 434 speakers. It is significantly larger in duration, vocabulary, and amount of spontaneous speech than the existing public speech corpora for Danish, which are largely limited to read-aloud and dictation data. We outline design considerations, including the preprocessing methods and the alignment procedure. To evaluate the quality of the corpus, we train automatic speech recognition systems on the new resource and compare them to the systems trained on the Danish part of Sprakbanken, the largest public ASR corpus for Danish to date. Our baseline results show that we achieve a 14.01 WER on the new corpus. A combination of FT Speech with in-domain language data provides comparable results to models trained specifically on Sprakbanken, showing that FT Speech transfers well to this data set. Interestingly, our results demonstrate that the opposite is not the case. This shows that FT Speech provides a valuable resource for promoting research on Danish ASR with more spontaneous speech.
Crystallization of cocoa butter with and without polar lipids evaluated by rheometry, calorimetry and polarized light microscopy

In this investigation, we evaluated the crystallization process of triacylglycerols (TAG) in unrefined cocoa butter (CB) and in CB without polar lipids (CB-WPL), under non-isothermal and isothermal conditions. These conditions were obtained by cooling from 80 °C at a cooling rate of 1 °C/min until achieving particular crystallization temperatures (i.e., 18.5°C, 19.0°C, 19.5°C, and 20 °C), at which isothermal crystallization studies were done. Phase shift angle rheograms (δ) showed that the onset of crystallization during the non-isothermal stage, independently of the crystallization temperature (T Cr ) used, was 59.8 ± 3.7 °C in CB and 39.5 ± 1.5 °C in CB-WPL. These results pointed out the nucleating role of phosphatidylcholine (26.53 ± 0.04%) and phosphatidylethanolamine (57.14 ± 0.07%), the main phospholipids present in the CB used in this investigation. Under similar crystallization conditions, differential scanning calorimetry (DSC) provided an onset of crystallization during the non-isothermal stage of 15.0 ± 0.0 °C in CB-WPL and of 15.5 ± 0.1 °C in CB. Then, δ rheograms were more sensitive to detect the nucleating role of phospholipids than DSC. Under isothermal conditions, both DSC and δ rheograms showed that shorter times were needed in CB-WPL to complete crystallization than in CB. In the same way, at all T Cr investigated, a higher crystallization rate was achieved in CB-WPL than in CB, this as measured by the crystallization rate constant (z) of the Avrami equation. These results showed that crystallization of a and p' polymorphs took longer in CB than in CB-WPL, pointing out that polar lipids delayed the α-to-β' polymorphic transition. Additional results confirmed that polar lipids affect the kinetics of TAG crystallization in CB. However, polar lipids do not affect the thermodynamic properties of the crystals [i.e., heat of fusion (ΔH M ), temperature of fusion (T M ')], the storage modulus at the end of the crystallization process (i.e., G' of pseudo-equilibrium), or the mechanism of crystal growth (i.e., the Avrami index, n).
Principal Component Analysis Based on T$\ell_1$-norm Maximization

Classical principal component analysis (PCA) may suffer from the sensitivity to outliers and noise. Therefore PCA based on $\ell_1$-norm and $\ell_p$-norm ($0 < p < 1$) have been studied. Among them, the ones based on $\ell_p$-norm seem to be most interesting from the robustness point of view. However, their numerical performance is not satisfactory. Note that, although T$\ell_1$-norm is similar to $\ell_p$-norm ($0 < p < 1$) in some sense, it has the stronger suppression effect to outliers and better continuity. So PCA based on T$\ell_1$-norm is proposed in this paper. Our numerical experiments have shown that its performance is superior than PCA-$\ell_p$ and $\ell_p$SPCA as well as PCA, PCA-$\ell_1$ obviously.
Big Rip: heating by Hawking radiation and a possible connection to conformal cyclic cosmology

In the Big Rip cosmological scenario, a FRW universe containing dark energy with $w < -1$ in its equation of state (phantom energy) expands in such way that, in a finite time span, the scale factor diverges to infinity and the size of the cosmic horizon goes to zero. Here we revisit this scenario in light of the fact that Hawking radiation is expected to be generated at the apparent horizon of a FRW universe, and show that the energy density and temperature of that radiation both diverge at the Big Rip. We then use this fact to propose a new variant of Penrose's conformal cyclic cosmology model in which the future spacetime metric becomes conformally invariant at the Big Rip instead of in the remote future of a de Sitter universe; this removes the need for mass decay from the model and makes it consistent with current physical laws.
Benchmarking Graph Data Management and Processing Systems: A Survey

The development of scalable, representative, and widely adopted benchmarks for graph data systems have been a question for which answers has been sought for decades. We conduct an in-depth study of the existing literature on benchmarks for graph data management and processing, covering 20 different benchmarks developed during the last 15 years. We categorize the benchmarks into three areas focusing on benchmarks for graph processing systems, graph database benchmarks, and bigdata benchmarks with graph processing workloads. This systematic approach allows us to identify multiple issues existing in this area, including i) few benchmarks exist which can produce high workload scenarios, ii) no significant work done on benchmarking graph stream processing as well as graph based machine learning, iii) benchmarks tend to use conventional metrics despite new meaningful metrics have been around for years, iv) increasing number of big data benchmarks appear with graph processing workloads. Following these observations, we conclude the survey by describing key challenges for future research on graph data systems benchmarking.
Gravitational waves induced by scalar perturbations with a lognormal peak

We study the stochastic gravitational wave (GW) background induced by the primordial scalar perturbation with the spectrum having a lognormal peak of width Δ at k=k*. We derive an analytical formula for the GW spectrum ΩGW for both narrow (Δ≪1) and broad (Δ≳ 1) peaks. In the narrow-peak case, the spectrum has a double peak feature with the sharper peak at k= 2k*/√3. On the infrared (IR) side of the spectrum, we find power-law behavior with a break at k=kb in the power-law index where it chages from k3 on the far IR side to k2 on the near IR side. We find the ratio of the break frequency to the peak frequency is determined by Δ as fb/fp≈√3Δ, where fb and fp are the break and peak frequencies, respectively. In the broad-peak case, we find the GW spectrum also has a lognormal peak at k=k* but with a smaller width of Δ/√2. Using these derived analytic formulae, we also present expressions for the maximum values of ΩGW for both narrow and broad cases. Our results will provide a useful tool in searching for the induced GW signals in the coming decades.
Memory-Efficient Sampling for Minimax Distance Measures

Minimax distance measure extracts the underlying patterns and manifolds in an unsupervised manner. The existing methods require a quadratic memory with respect to the number of objects. In this paper, we investigate efficient sampling schemes in order to reduce the memory requirement and provide a linear space complexity. In particular, we propose a novel sampling technique that adapts well with Minimax distances. We evaluate the methods on real-world datasets from different domains and analyze the results.
Exploring Strongly Interacting Gapless States: Cuprates, Pair Density Waves, and Fluctuating Superconductivity

We study the physical property of pair density wave (PDW) and fluctuating PDW, and use it to build an effective theory of the strongly interacting pseudogap phase in cuprate high temperature superconductors. In Chapter2, we study how Fulde-Ferrell state, the simplest form of PDW, responds to incident light. The collective motion of the condensate plays a key role; gauge invariance guides us to the correct result. From Chapter 3 to Chapter 7, we construct a pseudogap metallic state by considering quantum fluctuating PDW. We analyze a recent scanning tunneling microscope (STM) discovery of period-8 density waves in the vortex halo of the d-wave superconductor. We put it in the context of the broader pseudogap phenomenology, and compare the experimental results with various PDW-driven models and a charge density wave (CDW) driven model. We propose experiments to distinguish these different models. We present the Bogoliubov bands of PDW. We discuss fluctuating PDW from the general perspective of fluctuating superconductivity. We discuss how Bogoliubov bands evolve when the superconducting order parameter is fluctuating. We compare theoretical predictions with existing experiments on angle-resolved photoemission spectroscopy (ARPES), infrared conductivity, diamagnetism, and lattice symmetry breaking. The material presented here is based on Ref. [38,40,41].
Mitochondrial DNA variation in the Italian Heavy Draught Horse

Background In the last decades, Italy as well as other developed countries have registered a decrease in the population size of many local horse breeds. The continuous crossbreeding has determined the dilution of genetic heritage of several native breeds. The Italian Heavy Draught Horse (IHD) is the only autochthonous Italian coldblooded horse among these breeds; therefore, it represents a resource to be preserved. In 1927, the first generation of this breed was officially created by crossing different Heavy Draught horses with local mares and recorded in a Studbook. Methodology To provide the first comprehensive overview of the genetic diversity of Italian Heavy Draught horses from Central Italy, we produced and phylogenetically analysed 52 mitochondrial DNA (mtDNA) control-region sequences. Furthermore, we evaluated data available from GenBank (N = 568) to have a more complete scenario and to understand the relationships with other European Heavy Draught horse breeds. Results Among the IHD samples that were analysed, we identified ten of the 17 haplogroups described in modern horses. Most of these sequences fell into L, G, and M lineages, thus showing the overall mtDNA legacy of the ancestral mares that were probably used at the initial stages of breeding selections a long time ago. The high mitochondrial haplotype diversity (Hd = 0.969) found in our samples reflected the multiple maternal origins of the horses. Our results highlighted a considerable percentage of haplotypes shared especially with Bardigiano and Hungarian Heavy Draught breeds. Furthermore, both the presence of four unique haplotypes detected in our samples and their absence among all equine mitochondrial published data demonstrate a mitochondrial peculiarity that needs to be further investigated and preserved with careful breeding practices.
Urethral Diverticulum following Polypropylene Mesh Midurethral Slings: A Literature Review

Aims It is currently unknown whether an association exists between polypropylene mesh and urethral diverticulum formation following placement of polypropylene midurethral slings (MUS) for the treatment of stress urinary incontinence (SUI). We aimed to examine the literature associating MUS with the occurrence of urethral diverticula. Methods Multiple online research databases, including PubMed, Google Scholar, EBSCOhost, and the Cochrane Library, were searched, from January 2019 to February 2019, for evidence related to the occurrence of urethral diverticula following polypropylene MUS procedures. Results Four case reports were published demonstrating the occurrence of urethral diverticula following the use of polypropylene mesh for surgical treatment of SUI. Subjects of these cases were menopausal and had an elevated body mass index (BMI), recurrent urinary tract infections (UTIs), autoimmune conditions, or prior pelvic floor surgeries. A thorough urologic workup, including imaging prior to sling placement, was not always performed. Conclusion No clear association exists between polypropylene MUS placement and subsequent urethral diverticulum formation. Factors that diminish polypropylene mesh biocompatibility include elevated BMI, menopause, recurrent UTIs, prior pelvic surgeries, and preexisting medical conditions. Symptoms associated with urethral diverticula should prompt a complete urologic workup prior to MUS placement.
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection: let the virus be its own demise

There has been a collaborative global effort to construct novel therapeutic and prophylactic approaches to SARS-CoV-2 management. Although vaccine development is crucial, acute management of newly infected patients, especially those with severe acute respiratory distress syndrome, is a priority. Herein we describe the rationale and potential of repurposing a dual plasmid, Vigil (pbi-shRNAfurin-GM-CSF), now in Phase III cancer trials, for the treatment of and, in certain circumstances, enhancement of the immune response to SARS-CoV-2.
The Cataleptic, Asymmetric, Analgesic, and Brain Biochemical Effects of Parkinson's Disease Can Be Affected by Toxoplasma gondii Infection

Purpose Parkinson's disease (PD) is a neurodegenerative disorder with progressive motor defects. Therefore, the aim of the present investigation was to examine whether catalepsy, asymmetry, and nociceptive behaviors; the Nissl-body and neuron distribution; brain-derived neurotrophic factor (BDNF); malondialdehyde (MDA); total antioxidant capacity (TAC) levels; and the percentage of dopamine depletion of striatal neurons in the rat model of Parkinson's disease (PD) can be affected by Toxoplasma gondii (TG) infection. Methods Fifty rats were divided into five groups: control (intact rats), sham (rats which received an intrastriatal injection of artificial cerebrospinal fluid (ACSF)), PD control (induction of PD without TG infection), TG control (rats infected by TG without PD induction), and PD infected (third week after PD induction, infection by TG was done). PD was induced by the unilateral intrastriatal microinjection of 6-hydroxydopamine (6-OHDA) and ELISA quantified dopamine, BDNF, MDA, and TAC in the striatum tissue. Cataleptic, asymmetrical, nociceptive, and histological alterations were determined by bar test, elevated body swing test, formalin test, and Nissl-body and neuron counting in the striatal neurons. Results The results demonstrated that PD could significantly increase the number of biased swings, descent latency time, and nociceptive behavior and decrease the distribution of Nissl-stained neurons compared to the control and sham groups. TG infection significantly improved biased swing, descent latency time, nociceptive behavior, and the Nissl-body distribution in striatal neurons in comparison to the PD control group. The striatal level of BDNF in the PD-infected and TG control groups significantly increased relative to the PD control group. The striatal MDA was significantly higher in the PD control than other groups, while striatal TAC was significantly lower in the PD control than other groups. Conclusions The current study indicates that TG infection could improve the cataleptic, asymmetric, nociceptive and behaviors; the level of striatal dopamine release; BDNF levels; TAC; and MDA in PD rats.