Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deep-Bayesian Framework for Adaptive Speech Duration Modification

We propose the first method to adaptively modify the duration of a given speech signal. Our approach uses a Bayesian framework to define a latent attention map that links frames of the input and target utterances. We train a masked convolutional encoder-decoder network to produce this attention map via a stochastic version of the mean absolute error loss function; our model also predicts the length of the target speech signal using the encoder embeddings. The predicted length determines the number of steps for the decoder operation. During inference, we generate the attention map as a proxy for the similarity matrix between the given input speech and an unknown target speech signal. Using this similarity matrix, we compute a warping path of alignment between the two signals. Our experiments demonstrate that this adaptive framework produces similar results to dynamic time warping, which relies on a known target signal, on both voice conversion and emotion conversion tasks. We also show that our technique results in a high quality of generated speech that is on par with state-of-the-art vocoders.
HOMRS: High Order Metamorphic Relations Selector for Deep Neural Networks

Deep Neural Networks (DNN) applications are increasingly becoming a part of our everyday life, from medical applications to autonomous cars. Traditional validation of DNN relies on accuracy measures, however, the existence of adversarial examples has highlighted the limitations of these accuracy measures, raising concerns especially when DNN are integrated into safety-critical systems. In this paper, we present HOMRS, an approach to boost metamorphic testing by automatically building a small optimized set of high order metamorphic relations from an initial set of elementary metamorphic relations. HOMRS' backbone is a multi-objective search; it exploits ideas drawn from traditional systems testing such as code coverage, test case, path diversity as well as input validation. We applied HOMRS to MNIST/LeNet and SVHN/VGG and we report evidence that it builds a small but effective set of high-order transformations that generalize well to the input data distribution. Moreover, comparing to similar generation technique such as DeepXplore, we show that our distribution-based approach is more effective, generating valid transformations from an uncertainty quantification point of view, while requiring less computation time by leveraging the generalization ability of the approach.
Collective intelligence and the blockchain: Technology, communities and social experiments

Blockchains are still perceived chiefly as a new technology. But each blockchain is also a community and a social experiment, built around social consensus. Here I discuss three examples showing how collective intelligence can help, threat or capitalize on blockchainbased ecosystems. They concern the immutability of smart contracts, code transparency and new forms of property. The examples show that more research, new norms and, eventually, laws are needed to manage the interaction between collective behaviour and the blockchain technology. Insights from researchers in collective intelligence can help society rise up to the challenge.
Astro-COLIBRI: a new platform for real-time multi-messenger astrophysics

Flares of known astronomical sources and new transient phenomena occur on different timescales, from sub-seconds to several days or weeks. The discovery potential of both serendipitous observations and multi-messenger and multi-wavelength follow-up observations could be maximized with a tool which allows for quickly acquiring an overview over both persistent sources as well as transient events in the relevant phase space. We here present COincidence LIBrary for Real-time Inquiry (Astro-COLIBRI), a novel and comprehensive tool for this task. Astro-COLIBRI's architecture comprises a RESTful API, a real-time database, a cloud-based alert system and a website (https://astro-colibri.com) as well as apps for iOS and Android as clients for users. The structure of Astro-COLIBRI is optimized for performance and reliability and exploits concepts such as multi-index database queries, a global content delivery network (CDN), and direct data streams from the database to the clients. Astro-COLIBRI evaluates incoming VOEvent messages of astronomical observations in real time, filters them by user-specified criteria and puts them into their MWL and MM context. The clients provide a graphical representation with an easy to grasp summary of the relevant data to allow for the fast identification of interesting phenomena and provides an assessment of observing conditions at a large selection of observatories around the world. We here summarize the key features of Astro-COLIBRI, the architecture and used data resources. We specifically provide examples for applications and use cases. Focussing on the high-energy domain, we showcase how Astro-COLIBRI facilitates the search for high-energy gamma-ray counterparts to high-energy neutrinos and scheduling of follow-up observations of a large variety of transient phenomena like gamma-ray bursts, gravitational waves, TDEs, FRBs, and others.
Using Optimization Algorithms for Control of Multiple Output DC-DC Converters

The weighted voltage mode control represents a method for control of multiple outputs DC-DC converters. Accordingly, the weighted control redistributes the error among the outputs of these converters, and the regulation error can be reduced by adjusting the weighting factors. But the problem is that most designs are performed on the trial-and-error basis, and the results were rather inconsistent. Also, in conventional mathematical approaches, this factor is designed for converters by given parameters. In this paper, three optimization algorithms namely Imperialist Competitive Algorithm (ICA), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are proposed for a quick and accurate estimation of the optimal weighting factors and improve the amount of regulation on outputs of multiple outputs forward DC-DC converters. Furthermore, Fuzzy Logic Controller (FLC) is utilized to minimize the total steady-state error and improve transient characteristics by controlling the duty cycle of the PWM controller. Simulations have been performed in several cases and results show that the proposed method improves the outputs cross regulations in multiple outputs forward DC-DC converters significantly. ICA based weighting factor estimator has higher speed and accuracy in comparison with two other presented algorithms (ACO and PSO) and so is more effective in comparison with them.
Towards Accurate Localization by Instance Search

Visual object localization is the key step in a series of object detection tasks. In the literature, high localization accuracy is achieved with the mainstream strongly supervised frameworks. However, such methods require object-level annotations and are unable to detect objects of unknown categories. Weakly supervised methods face similar difficulties. In this paper, a self-paced learning framework is proposed to achieve accurate object localization on the rank list returned by instance search. The proposed framework mines the target instance gradually from the queries and their corresponding top-ranked search results. Since a common instance is shared between the query and the images in the rank list, the target visual instance can be accurately localized even without knowing what the object category is. In addition to performing localization on instance search, the issue of few-shot object detection is also addressed under the same framework. Superior performance over state-of-the-art methods is observed on both tasks.
Alexander invariants and cohomology jump loci in group extensions

We study the integral, rational, and modular Alexander invariants, as well as the cohomology jump loci of groups arising as extensions with trivial algebraic monodromy. Our focus is on extensions of the form 1→K→G→Q→1, where Q is an abelian group acting trivially on H1(K;ℤ), with suitable modifications in the rational and mod-p settings. We find a tight relationship between the Alexander invariants, the characteristic varieties, and the resonance varieties of the groups K and G. This leads to an inequality between the respective Chen ranks, which becomes an equality in degrees greater than 1 for split extensions.
Unclonable Anti‐Counterfeiting Labels Based on Microlens Arrays and Luminescent Microparticles

Micron‐scale randomness during manufacturing can create unique and unclonable anti‐counterfeiting labels. The security of such labels typically comes at the expense of complex hardware being required for authentication. This work demonstrates unclonable labels that can be authenticated using simple hardware such as a standard light‐emitting diode and smartphone camera. These labels consist of a microlens array laminated to a luminescent‐microparticle‐doped polymer film, and thereby present a new method of making microscopic particle distributions visible on the macroscopic scale. The current novel design offers two significant practical advantages: 1) use of an incoherent source; and 2) authentication independent of the detector position. A comparison of 100 test images against 100 different reference images (total of 10,000 comparisons out of which 100 should authenticate and 9900 should not), demonstrates that authentication is robust with an estimated probability of a false positive on the order of 10−15. Finally, a proof‐of‐concept is demonstrated through successful authentication of a label by a single smartphone, simultaneously providing both excitation and detection on the front side of the label.
Recent advances in Bayesian optimization with applications to parameter reconstruction in optical nano-metrology

Parameter reconstruction is a common problem in optical nano metrology. It generally involves a set of measurements, to which one attempts to fit a numerical model of the measurement process. The model evaluation typically involves to solve Maxwell's equations and is thus time consuming. This makes the reconstruction computationally demanding. Several methods exist for fitting the model to the measurements. On the one hand, Bayesian optimization methods for expensive black-box optimization enable an efficient reconstruction by training a machine learning model of the squared sum of deviations. On the other hand, curve fitting algorithms, such as the Levenberg-Marquardt method, take the deviations between all model outputs and corresponding measurement values into account which enables a fast local convergence. In this paper we present a Bayesian Target Vector Optimization scheme which combines these two approaches. We compare the performance of the presented method against a standard Levenberg-Marquardt-like algorithm, a conventional Bayesian optimization scheme, and the L-BFGS-B and Nelder-Mead simplex algorithms. As a stand-in for problems from nano metrology, we employ a non-linear least-square problem from the NIST Standard Reference Database. We find that the presented method generally uses fewer calls of the model function than any of the competing schemes to achieve similar reconstruction performance.
Trends in Na-Ion Solvation with Alkyl-Carbonate Electrolytes for Sodium-Ion Batteries: Insights from First-Principles Calculations

Classical molecular dynamics (MD) simulations and M06-2X hybrid density functional theory calculations have been performed to investigate the interaction of various nonaqueous organic electrolytes with Na+ ion in rechargeable Na-ion batteries. We evaluate trends in solvation behavior of seven common electrolytes namely pure carbonate solvents (ethylene carbonate (EC), vinylene carbonate (VC), propylene carbonate (PC), butylene carbonate (BC), dimethyl carbonate (DMC), ethyl methyl carbonate (EMC), and diethyl carbonate (DEC)) and four binary mixtures of carbonates (EC:PC, EC:DMC, EC:EMC, and EC:DEC). Thermochemistry calculations for the interaction of pure and binary mixtures of carbonate solvents with Na+ ion, Na+ ion coordinated with carbonate clusters obtained from molecular dynamics simulations, show that the formation of Na-carbonate complexes is exothermic and proceeds favorably. Based on the highest binding energy (ΔEb), enthalpy of solvation (ΔH(sol)), and Gibbs free energy of solvation (ΔG(sol)) .
On the Stable category of maximal Cohen-Macaulay modules over Gorenstein rings

Let ( A, m ) , ( B, n ) be Gorenstein local rings and let CM( A ) be its stable category of ﬁnitely generated maximal Cohen-Macaulay A -modules. Suppose CM( A ) ∼ = CM( B ) as triangulated categories. Then we show (1) If A is an abstract complete of codimension c then so is B . A,B are Henselian and hypersurfaces then dim A B . A complete hypersurface and multiplicity of least three even. We also give some applications of our results. It should be remarked that if R,S are complete Cohen-Macaulay but not necessarily Gorenstein and if there is an triangle isomorphism between the singularity categories of R and S then it is possible that dim R − dim S is odd, see M. Kalck; Adv. Math. 390 (2021), Paper No. 107913.
BCNet: A Deep Convolutional Neural Network for Breast Cancer Grading

Breast cancer has become one of the most prevalent cancers by which people all over the world are affected and is posed serious threats to human beings, in a particular woman. In order to provide effective treatment or prevention of this cancer, disease diagnosis in the early stages would be of high importance. There have been various methods to detect this disorder in which using images have to play a dominant role. Deep learning has been recently adopted widely in different areas of science, especially medicine. In breast cancer detection problems, some diverse deep learning techniques have been developed on different datasets and resulted in good accuracy. In this article, we aimed to present a deep neural network model to classify histopathological images from the Databiox image dataset as the first application on this image database. Our proposed model named BCNet has taken advantage of the transfer learning approach in which VGG16 is selected from available pertained models as a feature extractor. Furthermore, to address the problem of insufficient data, we employed the data augmentation technique to expand the input dataset. All implementations in this research, ranging from pre-processing actions to depicting the diagram of the model architecture, have been carried out using tf.keras API. As a consequence of the proposed model execution, the significant validation accuracy of 88% and evaluation accuracy of 72% obtained.
A theoretical look at ordinal classification methods based on reference sets composed of characteristic actions

From a theoretical view, this paper addresses the general problem of designing ordinal classification methods based on comparing actions with subset of actions, which are representative of their classes (categories). The basic demand of the proposal consists in setting a relational system (D, S), where S is a reflexive relation compatible with the preferential order of the set of classes, and D is a transitive relation such that D is a subset of S. Different ordinal classification methods can be derived from diverse model of preferences fulfilling the basic conditions on S and D. Two complementary assignment procedures compose each method, which correspond through the transposition operation and should be used complementarily. The methods work under relatively slight conditions on the representative actions and satisfy several fundamental properties. ELECTRE TRI-nC, INTERCLASS-nC, and the hierarchical ELECTRE TRI-nC with interacting criteria, can be considered as particular cases of this general framework.
Dust in the wind with Resonant Drag Instabilities: I. The dynamics of dust-driven outflows in GMCs and H ii regions

Radiation-dust driven outflows, where radiation pressure on dust grains accelerates gas, occur in many astrophysical environments. Almost all previous numerical studies of these systems have assumed that the dust was perfectly-coupled to the gas. However, it has recently been shown that the dust in these systems is unstable to a large class of ‘resonant drag instabilities’ (RDIs) which de-couple the dust and gas dynamics and could qualitatively change the nonlinear outcome of these outflows. We present the first simulations of radiation-dust driven outflows in stratified, inhomogeneous media, including explicit grain dynamics and a realistic spectrum of grain sizes and charge, magnetic fields and Lorentz forces on grains (which dramatically enhance the RDIs), Coulomb and Epstein drag forces, and explicit radiation transport allowing for different grain absorption and scattering properties. In this paper we consider conditions resembling giant molecular clouds (GMCs), H ii regions, and distributed starbursts, where optical depths are modest (≲ 1), single-scattering effects dominate radiation-dust coupling, Lorentz forces dominate over drag on grains, and the fastest-growing RDIs are similar, such as magnetosonic and fast-gyro RDIs. These RDIs generically produce strong size-dependent dust clustering, growing nonlinear on timescales that are much shorter than the characteristic times of the outflow. The instabilities produce filamentary and plume-like or ‘horsehead’ nebular morphologies that are remarkably similar to observed dust structures in GMCs and H ii regions. Additionally, in some cases they strongly alter the magnetic field structure and topology relative to filaments. Despite driving strong micro-scale dust clumping which leaves some gas ‘behind,’ an order-unity fraction of the gas is always efficiently entrained by dust.
Self-Supervised Discriminative Feature Learning for Deep Multi-View Clustering

Multi-view clustering is an important research topic due to its capability to utilize complementary information from multiple views. However, there are few methods to consider the negative impact caused by certain views with unclear clustering structures, resulting in poor multi-view clustering performance. To address this drawback, we propose self-supervised discriminative feature learning for deep multi-view clustering (SDMVC). Concretely, deep autoencoders are applied to learn embedded features for each view independently. To leverage the multi-view complementary information, we concatenate all views’ embedded features to form the global features, which can overcome the negative impact of some views’ unclear clustering structures. In a self-supervised manner, pseudo-labels are obtained to build a unified target distribution to perform multi-view discriminative feature learning. During this process, global discriminative information can be mined to supervise all views to learn more discriminative features, which in turn are used to update the target distribution. Besides, this unified target distribution can make SDMVC learn consistent cluster assignments, which accomplishes the clustering consistency of multiple views while preserving their features’ diversity. Experiments on various types of multi-view datasets show that SDMVC outperforms 14 competitors including classic and state-of-the-art methods. The code is available at https://github.com/SubmissionsIn/SDMVC.
Successful treatment of atopic dermatitis with house dust mite sublingual immunotherapy tablets.

BACKGROUND Atopic dermatitis (AD) is the most common chronic inflammatory skin disease. Allergen-specific immunotherapy is a treatment option for selected patients with severe AD sensitization to house dust mites (HDM). OBJECTIVE To report the first case of successful treatment with HDM sublingual immunotherapy (SLIT) tablets in patients with severe AD. METHODS A Thai male patient with HDM sensitization and severe AD who had not responded to topical corticosteroids and calcineurin inhibitors underwent 1 month of HDM subcutaneous immunotherapy (SCIT), after which his skin symptoms were minimally improved. He lost follow-up SCIT and the symptoms worsened, with large wheal lesions appearing at the SCIT injection site, so we decided to switch from SCIT to HDM SLIT tablets. RESULTS After the SLIT treatment, the AD and skin lesions improved and the medication could be stopped. CONCLUSIONS HDM SLIT might be an alternative treatment in patients with HDM sensitization and severe AD who are refractory to conventional treatment.
Rational Design of Immunomodulatory Hydrogels for Chronic Wound Healing

With all the advances in tissue engineering for construction of fully functional skin tissue, complete regeneration of chronic wounds is still challenging. Since immune reaction to the tissue damage is critical in regulating both the quality and duration of chronic wound healing cascade, strategies to modulate the immune system are of importance. Generally, in response to an injury, macrophages switch from pro‐inflammatory to an anti‐inflammatory phenotype. Therefore, controlling macrophages’ polarization has become an appealing approach in regenerative medicine. Recently, hydrogels‐based constructs, incorporated with various cellular and molecular signals, have been developed and utilized to adjust immune cell functions in various stages of wound healing. Here, the current state of knowledge on immune cell functions during skin tissue regeneration is first discussed. Recent advanced technologies used to design immunomodulatory hydrogels for controlling macrophages’ polarization are then summarized. Rational design of hydrogels for providing controlled immune stimulation via hydrogel chemistry and surface modification, as well as incorporation of cell and molecules, are also dicussed. In addition, the effects of hydrogels’ properties on immunogenic features and the wound healing process are summarized. Finally, future directions and upcoming research strategies to control immune responses during chronic wound healing are highlighted.
Ocular safety of repeated intravitreal injections of Carboplatin and Digoxin: A preclinical study on the healthy rabbits

To evaluate the ocular safety of intravitreal carboplatin and digoxin injections as a new intravitreal chemotherapy option for retinoblastoma tumor vitreous seeds. Eighteen rabbits were divided randomly into three groups to receive intravitreal injection of Digoxin (6 rabbits), Carboplatin (7 rabbits), or Saline (5 rabbits). In every group, one eye randomly treated with 10 µg Digoxin in 0.1 cc or 1 µg Carboplatin or Saline, and the contralateral eye was considered as the control. All groups underwent three consecutive injections of the drugs with 1‐week intervals. Baseline electroretinography (ERG) was recorded from both eyes of all the animals prior to injection and was repeated 1st day, 1st week, and 1st month after the last injection. All rabbits were sacrificed 1 month after the last injection, and histological studies were done. Mean a and b wave amplitudes decreased significantly at 1st day, 1st week, and 1st month after the last intravitreal injection of 10 µg Digoxin in comparison with other groups (p‐value: .02). Contradictory, 1 µg Carboplatin injected eyes had minimal ERG changes. There were some nonspecific ERG changes with unclear clinical significance in non‐injected contralateral control eyes of Digoxin and Carboplatin groups in comparison with the control eyes of the Saline group. Histological studies revealed considerable neural retinal atrophy in injected eyes of the Digoxin group. Intravitreal 10 µg Digoxin might have more local ocular toxicity in comparison with intravitreal Carboplatin in albino rabbit eyes. Future studies should assess the induced toxicity of intravitreal injection of these drugs on the non‐injected contralateral eye.