Miracle-dz/UniFAR-FTUs huggingface dataset:
Segment Aggregation for short utterances speaker verification using raw waveforms

Most studies on speaker verification systems focus on long-duration utterances, which are composed of sufficient phonetic information. However, the performances of these systems are known to degrade when short-duration utterances are inputted due to the lack of phonetic information as compared to the long utterances. In this paper, we propose a method that compensates for the performance degradation of speaker verification for short utterances, referred to as "segment aggregation". The proposed method adopts an ensemble-based design to improve the stability and accuracy of speaker verification systems. The proposed method segments an input utterance into several short utterances and then aggregates the segment embeddings extracted from the segmented inputs to compose a speaker embedding. Then, this method simultaneously trains the segment embeddings and the aggregated speaker embedding. In addition, we also modified the teacher-student learning method for the proposed method. Experimental results on different input duration using the VoxCeleb1 test set demonstrate that the proposed technique improves speaker verification performance by about 45.37% relatively compared to the baseline system with 1-second test utterance condition.
GACELA: A Generative Adversarial Context Encoder for Long Audio Inpainting of Music

In this article, we introduce GACELA, a conditional generative adversarial network (cGAN) designed to restore missing audio data with durations ranging between hundreds of milliseconds and a few seconds, i.e., to perform long-gap audio inpainting. While previous work either addressed shorter gaps or relied on exemplars by copying available information from other signal parts, GACELA addresses the inpainting of long gaps in two aspects. First, it considers various time scales of audio information by relying on five parallel discriminators with increasing resolution of receptive fields. Second, it is conditioned not only on the available information surrounding the gap, i.e., the context, but also on the latent variable of the cGAN. This addresses the inherent multi-modality of audio inpainting for such long gaps while providing the user with different inpainting options. GACELA was evaluated in listening tests on music signals of varying complexity and varying gap durations from 375 to 1500 ms. Under laboratory conditions, our subjects were often able to detect the inpainting. However, the severity of the inpainted artifacts was rated between not disturbing and mildly disturbing. GACELA represents a framework capable of integrating future improvements such as processing of more auditory-related features or explicit musical features. Our software and trained models, complemented by instructive examples, are available online.
Hierarchical Regression Network for Spectral Reconstruction from RGB Images

Capturing visual image with a hyperspectral camera has been successfully applied to many areas due to its narrowband imaging technology. Hyperspectral reconstruction from RGB images denotes a reverse process of hyperspectral imaging by discovering an inverse response function. Current works mainly map RGB images directly to corresponding spectrum but do not consider context information explicitly. Moreover, the use of encoder-decoder pair in current algorithms leads to loss of information. To address these problems, we propose a 4-level Hierarchical Regression Network (HRNet) with PixelShuffle layer as inter-level interaction. Furthermore, we adopt a residual dense block to remove artifacts of real world RGB images and a residual global block to build attention mechanism for enlarging perceptive field. We evaluate proposed HRNet with other architectures and techniques by participating in NTIRE 2020 Challenge on Spectral Reconstruction from RGB Images. The HRNet is the winning method of track 2 - real world images and ranks 3rd on track 1 - clean images.
Response of soil bacteria to Hg(II) in relation to soil characteristics and cell location

The response of indigenous bacterial communities to the addition of inorganic mercury (50 μM of Hg(II)) was compared over a 30 day period in four soils of contrasting properties. Hg(II) impact was estimated by following population dynamics of viable heterotrophic bacteria (VH) and mercury resistant bacteria (HgR) by indirect enumeration in unfractionated soil and in the inner and outer parts of soil aggregates obtained by successive washings. Numbers of VH bacteria in unfractionated soils were not affected by mercury in any of the studied soils, whereas an increase in resistant bacteria was observed in all of them. The lag phase before the increase of HgR bacteria and the magnitude of the enrichment could be related to soil organic matter and clay contents suggesting that these factors govern mercury bioavailability. In fractionated soil, the increase of resistant populations was more pronounced in the outer part (representing mainly bacteria in macropores) than in the inner part of soil aggregates. This difference in response was clearer in soils with well developed aggregates. Qualitative analysis revealed that exposure to Hg caused a large increase in the proportion of Gram-negative strains among HgR bacteria in both soil parts.
Leveraging elastic instabilities for amplified performance: Spine-inspired high-speed and high-force soft robots

Bistable spined soft robots enable high-speed cheetah-like galloping and fast-speed swimming, as well as high-force manipulation. Soft machines typically exhibit slow locomotion speed and low manipulation strength because of intrinsic limitations of soft materials. Here, we present a generic design principle that harnesses mechanical instability for a variety of spine-inspired fast and strong soft machines. Unlike most current soft robots that are designed as inherently and unimodally stable, our design leverages tunable snap-through bistability to fully explore the ability of soft robots to rapidly store and release energy within tens of milliseconds. We demonstrate this generic design principle with three high-performance soft machines: High-speed cheetah-like galloping crawlers with locomotion speeds of 2.68 body length/s, high-speed underwater swimmers (0.78 body length/s), and tunable low-to-high-force soft grippers with over 1 to 103 stiffness modulation (maximum load capacity is 11.4 kg). Our study establishes a new generic design paradigm of next-generation high-performance soft robots that are applicable for multifunctionality, different actuation methods, and materials at multiscales.
A Unified Weight Learning and Low-Rank Regression Model for Robust Face Recognition

Regression-based error modelling has been extensively studied for face recognition in recent years. The most important problem in regression-based error model is fitting the complex representation error caused by various corruptions and environment changes. However, existing works are not robust enough to model the complex corrupted errors. In this paper, we address this problem by a unified sparse weight learning and low-rank approximation regression model and applied it to the robust face recognition in the presence of varying types and levels of corruptions, such as random pixel corruptions and block occlusions, or disguise. The proposed model enables the random noise and contiguous occlusions to be addressed simultaneously. For the random noise, we proposed a generalized correntropy (GC) function to match the error distribution. For the structured error caused by occlusion or disguise, we proposed a GC function based rank approximation to measure the rank of error matrix. An effective iterative optimization is developed to solve the optimal weight learning and low-rank approximation. Extensive experimental results on three public face databases show that the proposed model can fit the error distribution and structure very well, thus obtain better recognition accuracy in comparison with the existing methods.
Cross-Language Transfer Learning, Continuous Learning, and Domain Adaptation for End-to-End Automatic Speech Recognition

In this paper, we demonstrate the efficacy of transfer learning and continuous learning for various automatic speech recognition (ASR) tasks. We start with a pre-trained English ASR model and show that transfer learning can be effectively and easily performed on: (1) different English accents, (2) different languages (German, Spanish and Russian) and (3) application-specific domains. Our experiments demonstrate that in all three cases, transfer learning from a good base model has higher accuracy than a model trained from scratch. It is preferred to fine-tune large models than small pre-trained models, even if the dataset for fine-tuning is small. Moreover, transfer learning significantly speeds up convergence for both very small and very large target datasets.
Monte-Carlo wavefunction approach for the spin dynamics of recombining radicals

We adapt the Monte-Carlo wavefunction (MCWF) approach to treat the open-system spin dynamics of radical pairs subject to spin-selective recombination reactions. For these systems, non-Lindbladian master equations are widely employed, which account for recombination via the non trace-preserving Haberkorn superoperator in combination with reaction-dependent exchange and singlet–triplet dephasing terms. We show that this type of master equation can be accommodated in the MCWF approach, by introducing a second type of quantum jump that accounts for the reaction simply by suitably terminating the propagation. In this way, we are able to evaluate approximate solutions to the time-dependent radical pair survival probability for systems that have been considered untreatable with the master equation approach until now. We explicate the suggested approach with calculations for radical pair reactions that have been suggested to be relevant for the quantum compass of birds and related phenomena.
PageRank and The K-Means Clustering Algorithm

We introduce a graph clustering algorithm that generalizes $k$-means to graphs. Our method utilizes PageRank measures on graphs to quickly and robustly compute centrality of nodes in a given graph. Furthermore, we show how our method can be generalized to metric spaces and apply it to other domains such as point clouds and triangulated meshes.
HNet: Graphical Hypergeometric Networks

Motivation: Real-world data often contain measurements with both continuous and discrete values. Despite the availability of many libraries, data sets with mixed data types require intensive pre-processing steps, and it remains a challenge to describe the relationships between variables. The data understanding phase is an important step in the data mining process, however, without making any assumptions on the data, the search space is super-exponential in the number of variables. Methods: We propose graphical hypergeometric networks (HNet), a method to test associations across variables for significance using statistical inference. The aim is to determine a network using only the significant associations in order to shed light on the complex relationships across variables. HNet processes raw unstructured data sets and outputs a network that consists of (partially) directed or undirected edges between the nodes (i.e., variables). To evaluate the accuracy of HNet, we used well known data sets and in addition generated data sets with known ground truth. The performance of HNet is compared to Bayesian structure learning. Results: We demonstrate that HNet showed high accuracy and performance in the detection of node links. In the case of the Alarm data set we can demonstrate on average an MCC score of 0.33 + 0.0002 (P<1x10-6), whereas Bayesian structure learning resulted in an average MCC score of 0.52 + 0.006 (P<1x10-11), and randomly assigning edges resulted in a MCC score of 0.004 + 0.0003 (P=0.49). Conclusions: HNet can process raw unstructured data sets, allows analysis of mixed data types, it easily scales up in number of variables, and allows detailed examination of the detected associations. Availability: this https URL
Ecological analogies between estuarine bottom trawl fish assemblages from Patos Lagoon, Rio Grande do Sul, Brazil and York River, Virginia, USA

The structure of estuarine fish assemblages at temperate latitudes in Patos Lagoon (32o05'S, 52o04'W), Rio Grande do Sul, Brazil and York River (37o17'N, 76o33'W), Virginia, USA was compared using mid and late 1970's data from bottom trawl collection to investigate whether geographically isolated fish assemblages have similar ecological structure given similar latitudinal positions on the warmtemperate southwestern and northwestern Atlantic regions, respectively. Since estuarine species often exhibit an ontogenetic shift in habitat requirements or preferences we examined Capture per Unity of Effort by size class (CPUE-SC) and split species into "size ecological taxa" (SET) for analysis. The use of CPUE-SC also allowed the abundance of a SET to be computed by summing the mean CPUE of each size class within that SET and use this information to follows SET's temporal and or spatial abundance. A total of 65 and 63 species was collected during a year of bottom trawling in the Patos Lagoon and York River estuaries, respectively. In both localities the strongest modal size class was < 80 mm TL, and several abundant species were smaller than 100 mm TL. The size between 80 and 100 TL effectively separated several species into discrete SET's in both systems. Those SET's could have different ecological preferences, temporal and spatial distributions and so identified as different "ecological taxa". In warm months, when predation by large fish is most likely, the abundance of fish between 80 and 100 mm TL in "bottom trawl" demersal fish assemblages was low in both systems. Only the sea catfishes, in Patos Lagoon, protected by strong dorsal and pectoral spines, and the Hogchoker, in the York River, protected by burrowing in the bottom substrate, peak in abundance at this size class. The seasonal pattern of estuarine use was similar between localities and did not differ from other warm-temperate estuarine fish assemblages.
A Weighted Difference of Anisotropic and Isotropic Total Variation for Relaxed Mumford-Shah Color and Multiphase Image Segmentation

In a class of piecewise-constant image segmentation models, we propose to incorporate a weighted difference of anisotropic and isotropic total variation (AITV) to regularize the partition boundaries in an image. To deal with the nonconvex AITV term, we apply the difference-of-convex algorithm (DCA), in which the subproblems can be minimized by the primal-dual hybrid gradient method with line search. We can prove that the DCA iterations converge to a limit point of the proposed model. We discuss the AITV extension of the Chan-Vese model and the fuzzy region competition model. A generalization to color image segmentation is also explained. In the numerical experiments, we compare our proposed models with the classic convex approaches and the two-stage segmentation methods (denoising and then thresholding) on various images, showing that our models are effective in image segmentation and robust with respect to impulsive noises.
Effective Laplacian for bilayer coupling

Networks under bilayer coupling topology are studied. A reduction approach is proposed where a single Laplacian matrix (called the effective Laplacian) is constructed out of the pair of Laplacian matrices, each describing a separate coupling layer. The presented construction generalizes the parallel sum of matrices. For a network of identical LC tanks under bilayer RL coupling, it is shown that the oscillators asymptotically synchronize if and only if the complex-valued effective Laplacian has a single eigenvalue on the imaginary axis.
Consistent Network Alignment with Node Embedding

Network alignment, the process of finding correspondences between nodes in different graphs, has significant scientific and industrial applications. We find that many existing network alignment methods fail to achieve accurate alignments because they break up node neighborhoods during alignment, failing to preserve matched neighborhood consistency. To improve this, we propose CONE-Align, which matches nodes based on embeddings that model intra-network proximity and are aligned to be comparable across networks. Experiments on diverse, challenging datasets show that CONE-Align is robust and obtains up to 49% greater accuracy than the state-of-the-art graph alignment algorithms.
Effects of Nile perch, Lates niloticus, on functional and specific fish diversity in Uganda's Lake Kyoga system

The introduction of Nile perch, Lates niloticus, to Lake Victoria, East Africa, interacted with eutrophication to cause a reorganization of the lake’s food web and the extirpation of many endemic fishes. The Lake Kyoga satellite system lies downstream from Lake Victoria. It encompasses species-rich lakes where Nile perch are absent or very rare, and low diversity lakes where L. niloticus is abundant. In 1999 we surveyed seven lakes in the Kyoga system using experimental monofilament gill nets (1/4‐1 inches variable mesh). At Boston University we assessed d 15 N signatures of epaxial muscle from subsamples of the catch (n ¼ 361). These signatures are often highly correlated with the near-term mean realized trophic position of an individual organism. A neural network analysis of fish length, species name, trophic level, and lake of origin fish explained 94% of the sample variance in d 15 N. We analysed statistical patterns in these signatures at a number of spatial scales. The relationship between trophic level and d 15 N varied greatly among lakes. Higher diversity perch-free lakes had greater variance in d 15 N values and fish lengths than lower diversity Nile perch lakes, suggesting an important relationship between species diversity and functional diversity. Against expectations, lake size was negatively correlated with d 15 N. Patterns in stable isotope signatures indicated that Nile perch lakes have shorter food chains than perch-free lakes. The results throw up two management problems for the Kyoga system. Impacted lakes need to be studied to understand and ameliorate the community-level effects of Nile perch introduction, whereas the species-rich nonperch lakes, which harbour a large proportion of the remaining diversity of regionally endemic taxa, are in need of conservation planning.
End-To-End Speech Synthesis Applied to Brazilian Portuguese

Voice synthesis systems are popular in different applications, such as personal assistants, GPS applications, screen readers and accessibility tools.Voice provides a natural way for human-computer interaction. However, not all languages are on the same level when in terms of resources and systems for voice synthesis. This work consists of the creation of publicly available resources for Brazilian Portuguese in the form of a dataset and deep learning models for end-to-end voice synthesis. The dataset has 10.5 hours from a single speaker. We investigated three different architectures to perform end-to-end speech synthesis: Tacotron 1, DCTTS and Mozilla TTS. We also analysed the performance of models according to different vocoders (RTISI-LA, WaveRNN and Universal WaveRNN), phonetic transcriptions usage, transfer learning (from English) and denoising. In the proposed scenario, a model based on Mozilla TTS and RTISI-LA vocoder presented the best performance, achieving a 4.03 MOS value. We also verified that transfer learning, phonetic transcriptions and denoising are useful to train the models over the presented dataset. The obtained results are comparable to related works covering English, even while using a smaller dataset
System-Level Predictive Maintenance: Review of Research Literature and Gap Analysis

This paper reviews current literature in the field of predictive maintenance from the system point of view. We differentiate the existing capabilities of condition estimation and failure risk forecasting as currently applied to simple components, from the capabilities needed to solve the same tasks for complex assets. System-level analysis faces more complex latent degradation states, it has to comprehensively account for active maintenance programs at each component level and consider coupling between different maintenance actions, while reflecting increased monetary and safety costs for system failures. As a result, methods that are effective for forecasting risk and informing maintenance decisions regarding individual components do not readily scale to provide reliable sub-system or system level insights. A novel holistic modeling approach is needed to incorporate available structural and physical knowledge and naturally handle the complexities of actively fielded and maintained assets.
Non-stationarity and dissipative time crystals: spectral properties and finite-size effects

We discuss the emergence of non-stationarity in open quantum many-body systems. This leads us to the definition of dissipative time crystals which display experimentally observable, persistent, time-periodic oscillations induced by noisy contact with an environment. We use the Loschmidt echo and local observables to indicate the presence of a finite sized dissipative time crystal. Starting from the closed Hubbard model we then provide examples of dissipation mechanisms that yield experimentally observable quantum periodic dynamics and allow analysis of the emergence of finite sized dissipative time crystals. For a disordered Hubbard model including two-particle loss and gain we find a dark Hamiltonian driving oscillations between GHZ states in the long-time limit. Finally, we discuss how the presented examples could be experimentally realized.
Control of heat pumps with CO2 emission intensity forecasts

An optimized heat pump control for building heating was developed for minimizing CO 2 emissions from related electrical power generation. The control is using weather and CO 2 emission forecasts as inputs to a Model Predictive Control (MPC)—a multivariate control algorithm using a dynamic process model, constraints and a cost function to be minimized. In a simulation study, the control was applied using weather and power grid conditions during a full-year period in 2017–2018 for the power bidding zone DK2 (East, Denmark). Two scenarios were studied; one with a family house and one with an office building. The buildings were dimensioned based on standards and building codes/regulations. The main results are measured as the CO 2 emission savings relative to a classical thermostatic control. Note that this only measures the gain achieved using the MPC control, that is, the energy flexibility, not the absolute savings. The results show that around 16% of savings could have been achieved during the period in well-insulated new buildings with floor heating. Further, a sensitivity analysis was carried out to evaluate the effect of various building properties, for example, level of insulation and thermal capacity. Danish building codes from 1977 and forward were used as benchmarks for insulation levels. It was shown that both insulation and thermal mass influence the achievable flexibility savings, especially for floor heating. Buildings that comply with building codes later than 1979 could provide flexibility emission savings of around 10%, while buildings that comply with earlier codes provided savings in the range of 0–5% depending on the heating system and thermal mass.