Miracle-dz/UniFAR-FTUs huggingface dataset:
CASIA Voice Conversion System for the Voice Conversion Challenge 2020

This paper presents our CASIA (Chinese Academy of Sciences, Institute of Automation) voice conversion system for the Voice Conversation Challenge 2020 (VCC 2020). The CASIA voice conversion system can be separated into two modules: the conversion model and the vocoder. We ﬁrst extract linguistic features from the source speech. Then, the conversion model takes these linguistic features as the inputs, aiming to predict the acoustic features of the target speaker. Finally, the vocoder utilizes these predicted features to generate the speech waveform of the target speaker. In our system, we utilize the CBHG conversion model and the LPCNet vocoder for speech generation. To better control the prosody of the converted speech, we utilize acoustic features of the source speech as additional inputs, including the pitch, voiced/unvoiced ﬂag and band aperiodicity. Since the training data is limited in VCC 2020, we build our system by combining the initialization using a multi-speaker data and the adaptation using limited data of the target speaker. The results of VCC 2020 rank our CASIA system in the second place with an overall mean opinion score of 3.99 for speaker quality and 84% accuracy for speaker similarity.
Semantic labeling of lidar point clouds for UAV applications

Small Unmanned Aerial Vehicle (UAV) platforms equipped with compact laser scanners provides a low-cost option for many applications, including surveillance, mapping, and reconnaissance. For these applications, semantic segmentation or semantic labeling of each point in the lidar point cloud, is important for scene-understanding. In this work, we evaluate methods for semantic segmentation of three-dimensional (3D) point clouds of outdoor scenes measured with a laser scanner mounted on a small UAV. We compare the performance of four different semantic segmentation methods, which are all applied in a scan-by-scan fashion, on semi-sparse laser data. The best method achieves 95.3% on the three classes ground, vegetation, and vehicle in terms of mean intersection over union (mIoU) on a previously unseen scene from a different geographical area. The results demonstrate that it is possible to achieve good performance on the semantic segmentation task on data measured using a combination of a small UAV and a compact laser scanner.
Oligonucleotide Therapies in the Treatment of Inflammatory Joint Disease

Osteoarthritis and rheumatoid arthritis are two of the most common chronic inflammatory joint diseases, for which there remains a great clinical need to develop safer and more efficacious pharmacological treatments. The pathology of both osteoarthritis and rheumatoid arthritis involves multiple tissues within the joint, including the synovial joint lining and the bone, as well as the articular cartilage in osteoarthritis. In this review, we discuss the potential for the development of oligonucleotide therapies for these disorders by examining the evidence that oligonucleotides can modulate the key cellular pathways that drive the pathology of the inflammatory diseased joint pathology as well as evidence in preclinical in vivo models that oligonucleotides can modify disease progression.
Restricting the timing of Elective CS: evidence from Brazil

Abstract Brazil has one of the highest Cesarean Section (CS) rates in the world. It is a share of 58.3% reported by the Living Births Information System (SINASC) 2015-2017. It is well above the maximum rate of 15% recommended by the World Health Organization (WHO). In this paper, we estimate impacts and unintended consequences of the Resolution 2,144 from the Federal council of Medicine (CFM) on outcomes of Low Risk First Born births (LRFB). The Resolution introduces a minimum of 39th weeks of gestation for Elective CS. Elective CS before the 39th week rate dropped 2.78 percentage points, which is statistically significant and equivalent to a 24% decrease in this outcome’s mean. We also find increases in birth’s time length: the percentage of births happening before the 39th week decreased 2.34 percentage points, which is a decrease of 6% in its average. Our results suggest that Elective CS’s were postponed from the 37-38th week to after the begin of the 39th week. We show that the policy had an unintended consequence once it seems to have changed the way potential spontaneous Natural Deliveries are anticipated from weekends to weekdays through Scheduled CS.
Casual Conversations: A dataset for measuring fairness in AI

This paper introduces a novel "fairness" dataset to measure the robustness of AI models to a diverse set of age, genders, apparent skin tones and ambient lighting conditions. Our dataset is composed of 3,011 subjects and contains over 45,000 videos, with an average of 15 videos per person. The videos were recorded in multiple U.S. states with a diverse set of adults in various age, gender and apparent skin tone groups. A key feature is that each subject agreed to participate for their likenesses to be used. Additionally, our age and gender annotations are provided by the subjects themselves. A group of trained annotators labeled the subjects’ apparent skin tone using the Fitzpatrick skin type scale [6]. Moreover, annotations for videos recorded in low ambient lighting are also provided. As an application to measure robustness of predictions across certain attributes, we evaluate the state-of-the-art apparent age and gender classification methods. Our experiments provides a through analysis on these models in terms of fair treatment of people from various backgrounds.
Surface Defect Detection of Nonburr Cylinder Liner Based on Improved YOLOv4

Cylinder liner plays an important role in the internal combustion engine. +e surface defects of cylinder liner will directly affect the safety and service life of the internal combustion engine. At present, the surface defect detection of cylinder liner mainly relies on manual visual inspection, which is easily affected by subjective factors of inspectors. Aiming at the bottleneck of traditional visual inspection technology in appearance inspection, this paper proposes a surface defect detection algorithm based on deep learning to realize defect location and classification. Based on the characteristics of the research object in this paper, the surface defect detection algorithm based on the improved YOLOv4 model is proposed, the model framework is constructed, and the data enhancement method and verification method are proposed. Experiments show that the proposed method can improve the detection accuracy and speed and can meet the requirements of the nonburr cylinder surface defect detection. At the same time, the method can be extended to other surface defect detection applications.
Sex-Specific Differences in Toxicity Following Systemic Paclitaxel Treatment and Localized Cardiac Radiotherapy

Simple Summary The objective of the present study was to investigate the impact of sex in the development of long-term toxicities affecting quality of life in cancer survivors after systemic paclitaxel treatment and cardiac irradiation. Sex-specific differences may affect tumor biology, drug pharmacokinetics and dynamics, and response to local treatment such as radiation therapy (RT). However, sex is rarely taken into consideration when administering cancer therapies. Interestingly, female mice are protected from paclitaxel-induced neurotoxicity as well as from radiotherapy-induced cardiotoxicity, and deficiency in the small GTPase RhoB reversed the protection in females but not in males. In conclusion, our results are the first to identify sex- and organ-specific responses to systemic paclitaxel administration and localized RT. These results may have important implications for the management of cancer patients and implementation of personalized medicine in oncology. Abstract The impact of sex in the development of long-term toxicities affecting the quality of life of cancer survivors has not been investigated experimentally. To address this issue, a series of neurologic and cardiologic endpoints were used to investigate sex-based differences triggered by paclitaxel treatment and radiotherapy exposure. Male and female wild-type (WT) mice were treated with paclitaxel (150 and 300 mg/kg) administered weekly over 6 weeks or exposed to 19 Gy cardiac irradiation. Cohorts were analyzed for behavioral and neurobiologic endpoints to assess systemic toxicity of paclitaxel or cardiovascular endpoints to assess radiotherapy toxicity. Interestingly, female WT mice exhibited enhanced tolerance compared to male WT mice regardless of the treatment regimen. To provide insight into the possible sex-specific protective mechanisms, rhoB-deficient animals and elderly mice (22 months) were used with a focus on the possible contribution of sex hormones, including estrogen. In females, RhoB deficiency and advanced age had no impact on neurocognitive impairment induced by paclitaxel but enhanced cardiac sensitivity to radiotherapy. Conversely, rhoB-deficiency protected males from radiation toxicity. In sum, RhoB was identified as a molecular determinant driving estrogen-dependent cardioprotection in female mice, whereas neuroprotection was not sex hormone dependent. To our knowledge, this study revealed for the first time sex- and organ-specific responses to paclitaxel and radiotherapy.
Expanded view of electron-hole recollisions in solid-state high-harmonic generation: Significance of full-Brillouin-zone tunneling and imperfect recollisions

We theoretically investigate electron-hole recollisions in high-harmonic generation (HHG) in bandgap solids irradiated by linearly and elliptically polarized drivers. We find that in many cases the emitted harmonics do not originate in electron-hole pairs created at the minimum band gap, where the tunneling probability is maximized, but rather in pairs created across an extended region of the Brillouin zone (BZ). In these situations, the analogy to gas-phase HHG in terms of the shortand long-trajectory categorizations is inadequate. Our analysis methodology comprises three complementary levels of theory: the numerical solutions to the semiconductor Bloch equations, an extended semiclassical recollision model, and a quantum wave packet approach. We apply this methodology to two general material types with representative band structures: a bulk system and a hexagonal monolayer system. In the bulk, the interband harmonics generated using ellipticallypolarized drivers are found to originate not from tunneling at the minimum band gap Γ, but from regions away from it. In the monolayer system driven by linearly-polarized pulses, tunneling regions near different symmetry points in the BZ lead to distinct harmonic energies and emission profiles. We show that the imperfect recollisions, where an electron-hole pair recollide while being spatially separated, are important in both bulk and monolayer materials. The excellent agreement between our three levels of theory highlights and characterizes the complexity behind the HHG emission dynamics in solids, and expands on the notion of interband HHG as always originating in trajectories tunnelled at the minimum band gap. Our work furthers the fundamental understanding of HHG in periodic systems and will benefit the future design of experiments.
The Translation of English Politeness Marker in Giving Invitation into Indonesian: Does It Influence the Illocution?

The utterance of invitation is sometimes used to show a polite attitude to others. However it is rarely attracted linguists’ attention compared to other type utterances. Politeness strategies in invitation are not only appear in real conversation but also in literary works. In the translation of politeness markers, translators are influence with the cultural situation. Thus it raises a question how translators deal with politeness marker in giving invitation for intercultural context. This paper aims at investigating the translation of politeness markers in giving invitation used by the main characters in the novel Deception Point written in English by Dan Brown. Data were the utterances of invitation made by main characters in the novel Deception Point as source text and its two Indonesian translation versions as target texts. Data were taken by document analysis and focus group discussion in classifying the translation techniques. Then, data were analyzed by using speech act theory and felicity condition. The result show that 1) there are only four main characters who give invitation due to the felicity conditions of invitation, 2) bald on record, positive, and negative politeness strategies are used by the characters in mitigating the invitation utterances, 3) there are some changes in the illocution in translation caused by the implementation of translation technique to the politeness marker. It is suggested to use established equivalence technique in translating politeness marker to build mutual understanding and avoid modulation since it may change the illocution of the utterances.
A Spoken Goal-Oriented Dialogue System for Service Robots

In recent years, researchers have been trying to develop conversational systems capable of understanding and speaking in natural language, so that humans can interact with them easily and more naturally. Human-Computer dialogue systems, particularly goal-based dialogue systems, have been the most important component in conversational systems, since they allow users to speak naturally in order to accomplish tasks more eﬃciently. However, when deployed to production, a spoken dialogue system may encounter a variety of diﬃculties, such as a large variation in the users of the system and high noise environments. Thus, the dialogue system must be able to use statistical frameworks to handle the uncertainty in both the speech recognition and language understanding. In this work, we develop a spoken goal-oriented dialogue system capable of dealing with such uncertainties, by maintaining uncertainty about everything the user has said, and exploit the sequential nature of dialogue to disambiguate in the presence of errors. In particular, we design three main components: Natural Language Understanding (NLU), Dialogue State Tracking (DST) and Dialogue Management (DM). Furthermore, we implement the developed dialogue system in a service robot and test the complete system on a real world environment.
Netlist Decompilation Workflow for Recovered Design Verification, Validation, and Assurance

Katie Liszewski Cyber Trust & Analytics Battelle Memorial Institute Columbus, OH USA liszewski@battelle.org Tim McDonley Cyber Trust & Analytics Battelle Memorial Institute Columbus, OH USA mcdonley@battelle.org Josh Delozier Cyber Trust & Analytics Battelle Memorial Institute Columbus, OH USA delozier@battelle.org Andrew Elliott Cyber Trust & Analytics Battelle Memorial Institute Columbus, OH USA elliottas@battelle.org
iBlurDetect: Image Blur Detection Techniques Assessment and Evaluation Study

The quality of images is essential in computer vision, image processing, and other related fields. Image restoration is one of the categories in image processing, where the quality of an image plays a vital role in the process. Blur detection is a pre-processing stage in image restoration. Using different blur detection techniques, the quality of an image can identify if blurry or not. This study aims to provide a comparative performance of the available state-of-the-art blur measure operators or blur detection techniques. Python 6.3 was used for testing and evaluating the blur detection techniques. Providing the confusion matrix, precision, recall, f-measure, accuracy, and execution time were used to compare blur detection techniques. In testing, the Gaussian kernel and threshold value were set to measure the performance of each technique. Provided on the evaluation results, in terms of accuracy rate, HWT leads the best result. Based on the computed scores, FFT got the highest precision score, while LAP got the highest recall score, and HWT got the highest f-measure score. In terms of the execution time, MLAP performs the fastest processing time among them all. Likewise, results of this study can use as resources before performing the image restoration.
Failure diagnosis in photovoltaic systems: a pattern recognition approach based on artificial neural networks

— This thesis targets a method of diagnosing behaviours in photovoltaic (PV) systems using artificial intelligence technique. It uses a known model, the one diode and five parameters, and simulates the panel as set of solar cells, in order to represent mismatch faults in the panel. This simulation model is employed to create a database of characteristic curves that include the response under standard condition, but also two faulty conditions: short circuited cells and partial shading. From each curve, a few key informations are extracted: the voltage at maximum power point, the current at maximum power point, the panel temperature and the irradiance. These informations are used as inputs for training an artificial neural network (ANN) to classify the original PV panel condition. Applying normalization to the inputs increased the convergence and classification of the ANN. The trained ANN was tested with real outdoor measurements of a PV panel under all described conditions. The results showed an accuracy of 95% in the detection of a faulty condition and an accuracy of 73% in the diagnosis of the behaviour. Post conclusion several recommendations are made to improve and develop this method of diagnosis.
Design of Comminution in Ceramic Plants Using a Simulation-Based Optimization Approach Simulación para la optimización del diseño de instalaciones de molienda para la industria cerámica

The design process of a structural ceramic comminution plant typically consists of an “expert-designer” making decisions using intuitive criteria to select commercial equipment. This paper proposes a simulation optimisation approach to help decision-making. The complexity of the problem lies in selecting the model and number of equipment for each stage with the lowest economic cost while simultaneously satisfying a previously fixed production and granulometry. The proposed approach is based on a genetic algorithm to generate solutions and facilitate the optimisation process, coupled with discrete simulation to evaluate the performance of the comminution process according to its service level. Scenario analyses of the different levels of client requirements in the ceramic industry are performed to evaluate the algorithm.
Nonhomogeneous Silica Promotes the Biologically Induced Delivery of Metal Ions from Silica-Coated Magnetic Nanoparticles

Herein we report the endocytosis of magnetic nanoparticles of two different transition metal ferrites, which are coated with silica. The variation in the cytotoxicity results, which correlate with the metal ions from the magnetic cores, stems from the inhomogeneity of the silica shell and consequent partial degradation of the nanoparticles once loaded into the endosomes of Caco-2 cells.
Health care misinformation: An artificial intelligence challenge for low-resource languages

In this paper, we motivate using state-of-the-art artificial intelligence technologies to address challenges presented by low-resource languages. We also reflect on both the importance and priorities of AI research with respect to the less wealthy economies of the world. We explore the contributions of colonialism to language (in)accessibility and public health misinformation during the Covid-19 pandemic in the African region. Using the West African country of Mali as a case study, we discuss the historic contribution of colonial educational systems to the creation of disenfranchised populations. These populations are left with limited access to important medical information that can mean life or death in the current Covid-19 pandemic. We propose a humans-in-the-loop neural machine translation, (NMT), solution to medical information translation. In our solution, the state-of-the-art NMT approach is applied to the low-resource language Bambara which is spoken by a majority of the Malian people. By implementing a crowdsourced Bambara language data collection and translation component in this machine learning problem, we engage the local Malians. The aim of this project is to address the lack of Bambara language resources and leverage current best practice in order to undo some of the artefacts of colonialism. We describe the unique challenges and research issues raised by this novel application of AI technology. Copyright © 2020 for this paper by its authors.
Supplementary File for: Intrinsic Image Harmonization

The reflectance and illumination are disentangled by encoder-decoder networks, while lighting and guiding are regressed using simple encoder networks. The reflectance encoder-decoder network uses 4-layer ResBlocks and 2layer guiding blocks, and the illumination encoder-decoder network adopts 4-layer lighting ResBlocks and 2-layer guiding blocks. The final activation function is tanh for reflectance, illumination, and guiding, while no activation but average pooling with fully-connected layer for lighting. Reflectance and illumination encoder-decoder outputs are normalized to [0, 1] to recover Ĥ. We train our model using Adam optimizer [5] with parameters of β1 = 0.5, β2 = 0.999 and learning rate α = 0.0001. We resize input images as 256×256 for training and testing, and our model produces harmonized images with the same size. Light latent code is set as an 8-dimentional vector and inharmonyfree feature maps are 32 × 32 × 256 volume. We empirically set λRH = 0.1, λIS = 0.01, λIH = 0.1 and λIF = 1 in our experiments. Our model does not use the occluded background information for training and testing. We report the implementation details of our autoencoderbased architecture for intrinsic image harmonization in Tables A (reflectance intrinsic image harmonization), B (illumination intrinsic image harmonization), C (light learning), and D (inharmony-free learning). We use standard encoder networks for both light and inharmony-free learning, and encoder-decoder networks for reflectance and illumination intrinsic image harmonization. Symbols of the operators are defined as follows:
LCCNet: LiDAR and Camera Self-Calibration using Cost Volume Network

Multi-sensor fusion is for enhancing environment perception and 3D reconstruction in self-driving and robot navigation. Calibration between sensors is the precondition of effective multi-sensor fusion. Laborious manual works and complex environment settings exist in old-fashioned calibration techniques for Light Detection and Ranging (Li-DAR) and camera. We propose an online LiDAR-Camera Self-calibration Network (LCCNet), different from the previous CNN-based methods. LCCNet can be trained end-to-end and predict the extrinsic parameters in real-time. In the LCCNet, we exploit the cost volume layer to express the correlation between the RGB image features and the depth image projected from point clouds. Besides using the smooth L1-Loss of the predicted extrinsic calibration parameters as a supervised signal, an additional self-supervised signal, point cloud distance loss, is applied during training. Instead of directly regressing the extrinsic parameters, we predict the decalibrated deviation from initial calibration to the ground truth. The calibration error decreases further with iterative refinement and the temporal filtering approach in the inference stage. The execution time of the calibration process is 24ms for each iteration on a single GPU. LCCNet achieves a mean absolute calibration error of 0.297cm in translation and 0.017° in rotation with miscalibration magnitudes of up to ±1.5m and ±20° on the KITTI-odometry dataset, which is better than the state-of-the-art CNN-based calibration methods. The code will be publicly available at https://github.com/LvXudong-HIT/LCCNet