Miracle-dz/UniFAR-FTUs huggingface dataset:
Speech enhancement by bit-rate extension based on Time-frequency simultaneous-constrained

Different from the conventional approach of bandwidth extension (BWE), we propose an algorithm to enhance speech by bit-rate extension (BRE). Due to the different resolution of amplitude, low bit-rate (LB) signals contain much more quantization noise than high bit-rate (HB) signals. Theoretically, simply applying conventional speech enhancement algorithms can enhance LB signals. However, even if LB signals are corrupted by quantization noise, the quantization mechanism makes the remaining upper bits of LB signals identical to the corresponding HB signals, and conventional speech enhancement algorithms may corrupt the “correct” upper bits of LB signals. In this paper, we focus on that the “correct” upper bits of LB signals will follow a timedomain constraint, and use it to support enhancement to achieve a better performance. Therefore, after the general amplitude-based speech enhancement, we also designed a time-frequency simultaneously constrained Griffin-Lim algorithm (TFC-GLA) to ensure that the HB signal estimations satisfy the time-domain constraint while maintaining the estimated amplitude in frequency-domain. Objective evaluations show that our proposed BRE algorithm is effective in enhancing speech.
Real-World Impact of Immune Checkpoint Inhibitors in Metastatic Uveal Melanoma

Uveal melanoma (UM) is the most common intraocular malignancy in adults and shows a high rate of metastatic spread. As randomized clinical trials with immune checkpoint inhibitors (ICI) have not been performed in patients with metastatic UM, we analyzed the real-world outcomes in a nationwide population-based study. Clinical data of patients with UM were extracted from the Danish Metastatic Melanoma database, a nationwide database containing unselected records of patients diagnosed with metastatic melanoma in Denmark. Survival before (pre-ICI, n = 32) and after (post-ICI, n = 94) the approval of first-line treatment with ICI was analyzed. A partial response to first-line treatment was observed in 7% of patients treated with anti-programmed cell death protein (PD)-1 monotherapy and in 21% with combined anti-cytotoxic T lymphocyte antigen (CTLA)-4 plus anti-PD-1 therapy. Median progression-free survival was 2.5 months for patients treated in the pre-ICI era compared to 3.5 months in the post-ICI era (hazard ratio (HR) 0.43; 95% confidence interval (CI) 0.28–0.67; p < 0.001). The estimated one-year overall survival rate increased from 25.0% to 41.9% and the median overall survival improved from 7.8 months to 10.0 months, respectively (HR 0.52; 95% CI 0.34–0.79; p = 0.003). Thus, the introduction of ICI as first-line treatment appears to have significantly improved the real-world survival of patients with metastatic UM, despite relatively low response rates compared to cutaneous melanoma. With the lack of therapies proven effective in randomized trials, these data support the current treatment with ICI in patients with metastatic UM.
Parallel Corpora Preparation for Machine Translation of Low-Resource Languages: Turkish to English Cardiology Corpora

High quality clean parallel corpora is a must for creating statistical machine translation or neural machine translation systems. Although high quality parallel corpora is largely available for official languages of the European Union, the United Nations and other organization, it is hard to encounter enough amount of open parallel corpora for languages such as Turkish, which, in turn, leads to lower quality Machine Translation for these languages. In this study, we use automatic and semi-automatic procedures to collect and prepare parallel corpora in cardiology domain. We crawl a journal website and obtain 6500 Turkish abstracts and their English translations by using HTTrack. By aligning these abstracts and converting them into a translation memory in a computer-aided translation tool environment, we make it possible to use the corpora for machine translation training as well as term extraction. We argue that new tools integrating and streamlining the web crawling, alignment and cleaning steps are needed in order to support the preparation of parallel corpora for low-resource languages.
Optimization and validation of a high-performance liquid chromatography method for the determination of amlodipine in patients plasma after gastrectomy – pilot study

Background. A sensitive, rapid and simple high-performance liquid chromatography (HPLC) method has been developed for the assay of amlodipine in human plasma, by off-line solid-phase extraction followed by HPLC coupled with ultraviolet Dual Absorbance Detector (UV). Material and methods. Separation of compounds was achieved on a Symmetry, C18 (250 mm × 4.6 mm, 5 mm) analytical column using a mobile phase consisting of 0.1 M ammonium octane in water/acetonitrile (40:60, v/v), at a flow rate of 1.0 ml/min, with diltiazem added as internal standard. Linear calibration curves in human plasma were generated over the range of 2-16 ng/ml with values for the coefficient of determination of 0.997. Amlodipine was detected by UV at 239 nm. Plasma (1000 μl) was diluted 1:1 with 2% phosphoric acid subjected to a solid-phase extraction on a C18 cartridge. After matrix components elimination, amlodipine was subsequently eluted with 1000 μl of methanol. The resulting eluate was evaporated under nitrogen at room temperature and the residue was reconstituted in mobile phase and a volume of 50 μl was injected onto the HPLC column. Results. The calibration curves are linear between 2.0 and 16.0 ng/ml. The method is precise with mean inter-day coefficients of variation (CV) within 5.4-10.6%, and accurate (range of inter-day deviations -9.5 to +8.5%). Conclusion. This method can be used in routine clinical practice to monitor plasma amlodipine concentrations in patients. (Farm Współ 2018; 11: 3-7)
Approximation Analysis of Convolutional Neural Networks

In its simplest form, convolution neural networks (CNNs) consist of a fully connected two-layer network g composed with a sequence of convolution layers T . Although g is known to have the universal approximation property, it is not known if CNNs, which have the form g ◦ T inherit this property, especially when the kernel size in T is small. In this paper, we show that under suitable conditions, CNNs do inherit the universal approximation property and its sample complexity can be characterized. In addition, we discuss concretely how the nonlinearity of T can improve the approximation power. Finally, we show that when the target function class has a certain compositional form, convolutional networks are far more advantageous compared with fully connected networks, in terms of the number of parameters needed to achieve the desired accuracy.
Smart Charging of Future Electric Vehicles Using Roadway Infrastructure

Inspired by the fact that there is an immense amount of renewable energy sources available on the roadways such as mechanical pressure and frictional heat, this project presents the development and implementation of an innovative charging technique for future electric vehicles (EVs) by fully utilizing the existing roadways and the state-of-the-art nanotechnology. The project introduces a novel wireless charging system that uses LEDs embedded within fiber reinforced polymer (FRP) coated pavement powered by piezoelectric nanomaterials as the energy transmitter source and thin film solar panels placed at the bottom of the EVs as the receiver, which is then poised to deliver the harvested energy to the vehicle’s battery.
XD: CROSS-LINGUAL KNOWLEDGE DISTILLATION

Current state-of-the-art results in multilingual natural language inference (NLI) are based on tuning XLM (a pre-trained polyglot language model) separately for each language involved, resulting in multiple models. We reach significantly higher NLI results with a single model for all languages via multilingual tuning. Furthermore, we introduce cross-lingual knowledge distillation (XD), where the same polyglot model is used both as teacher and student across languages to improve its sentence representations without using the end-task labels. When used alone, XD beats multilingual tuning for some languages and the combination of them both results in a new state-of-the-art of 79.2% on the XNLI dataset, surpassing the previous result by absolute 2.5%. The models and code for reproducing our experiments will be made publicly available after de-anonymization.
Evaluation of Power-Ratings for Active Series Filters under Unbalanced Conditions For a Three Phase - Four Wire System

The optimal power rating of a series active filter is important not only in design of compensator for a specific application, but also in determining the best compensation strategy for a specific application. This paper focus mainly on the confront of the power rating of a combined system consisting of two small rating series active filters and a shunt passive filter generally designed for balanced condition, and the power rating required when it's working under unbalanced conditions. This paper highlights the overload imposed to the active series filters for two different degree of imbalance showing that even if the overloaded doesn't exist the system wont be free of harmonic pollution, exceeding the IEEE 519 recommendations
Effective Method for Creation of Domain Module Information from Electronic Textbook

In the majority education situation technology supported systems have turned out to be very supportive. This type of systems has need of the idyllic re-presentation of the information to become learned, called domain module. The domain module authoring is charge and effort demanding, however its progress charge may be decreased simply by earning through semi-automatic Site Module authoring methods along with encouraging again and again use of information. Natural language processing techniques, ontologies, heuristic reasoning is used typically for DOMSortze system to the semi-automatic building of the domain module from electronic-textbooks. It has been examined with an electronic-textbook to locate how it assist in the domain module authoring methods and the collected information has been compared through the domain module , that instructional designers build up by hand. How the accuracy of identification of pedagogical relationships is increased is analyzed. This system shows DOM-Sortze and explains the research takeaway and used for present result of different type of the electronic textbooks and result generated will be the domain module information which stores more appropriate pedagogical relation.
Applying CNN to Infrared Thermography for Preventive Maintenance of Electrical Equipment

This paper presents the new method to classify the condition of electrical equipment such as main circuit breaker, magnetic contactor, in term of thermal radiation effect using infrared thermography. The conventional neural network (CNN) is one of deep learning method which is widely used in pattern recognition and object detection. In this paper, the thermal image processing was used to determine the critical temperature on equipment and, the deep learning technology was applied to identify the type of equipment. Therefore, we can know the condition with the type of electrical equipment for maintenance purpose in real-time. An accuracy of our method is 91% for identifying type of equipment. This technique can be implemented to an automatic alarm annunciation system for other dangerous equipment with sensitive to thermal as well.
mutagenesis studies viruses : common patterns revealed by site-directed Mutational fitness effects in RNA and single-stranded DNA

References http://rstb.royalsocietypublishing. org/content/365/1548/1975.full. html#ref-list-1 This article cites 57 articles, 11 of which can be accessed free Rapid response http://rstb.royalsocietypublishing. org/letters/submit/royptb;365/1548/1975 Respond to this article Subject collections (1681 articles) evolution  (246 articles) molecular biology  Articles on similar topics can be found in the following collections
A New Belief Entropy Based on Deng Entropy

For Dempster–Shafer evidence theory, how to measure the uncertainty of basic probability assignment (BPA) is still an open question. Deng entropy is one of the methods for measuring the uncertainty of Dempster–Shafer evidence. Recently, some limitations of Deng entropy theory are found. For overcoming these limitations, some modified theories are given based on Deng entropy. However, only one special situation is considered in each theory method. In this paper, a unified form of the belief entropy is proposed on the basis of Deng entropy. In the new proposed method, the scale of the frame of discernment (FOD) and the relative scale of a focal element with reference to FOD are considered. Meanwhile, for an example, some properties of the belief entropy are obtained based on a special situation of a unified form. Some numerical examples are illustrated to show the efficiency and accuracy of the proposed belief entropy.
The Effect of Maternal Employment on the Likelihood of a Child Being Overweight (Discussion Paper: 2007/17)

Childhood obesity has increased dramatically in the developed world. One cause of this trend, suggested by studies in the United States, is the increase in maternal employment. This paper explores if the causal relationship exists in Australia. Using recent data from the Longitudinal Survey of Australian Children (LSAC), a 2SLS procedure and a Full Information Maximum Likelihood (FIML) model that jointly estimates a multinomial treatment and binary outcome is used to control for endogeneity and self-selection bias, respectively. The results consistently show that maternal employment does have an impact on the likelihood of a child being overweight and that this impact is positive and statistically significant.
Does Comprehension Time Constraint Affect Poetic Appreciation of Metaphors ?

Metaphors, such as “My love is a red rose”, are literary devices used to say something about a concept (i.e., the “topic”) figuratively in terms of a different concept (i.e., the “vehicle”). Although how metaphors are comprehended or understood has been examined by the enormous number of cognitive or psychological studies (e.g., Bowdle & Gentner, 2005; Gibbs, 2008; Glucksberg, 2003; Lakoff & Johnson, 1980), there have been relatively few attempts to examine the mechanism of metaphor appreciation. Metaphor appreciation refers to some aesthetic judgment, especially on poetic character of metaphors. Exploring the mechanism of poetic appreciation of metaphor is an important topic of research on literary texts in general and metaphors in particular, because the primary function of metaphor is to evoke poetic or aesthetic effects to the addressee’s mind, as well as to communicate what literal language cannot precisely convey. Recent arguments for “language as sexual ornament” hypothesis (e.g., Miller, 2000) on the origin of language seem to suggest that the importance of poetic or aesthetic function may be also the case with language in general. (Think of a birthday cake that makes your son, daughter or wife happy. Such cake must have not only a good sweet taste, but also beautiful decorations! ) One intriguing question that arises here is whether (and how) metaphor comprehension affects metaphor appreciation. Early studies (e.g., Gerrig & Healy, 1983; Gerrig, 1989; McCabe, 1983) reported negative findings against the relationship between metaphor comprehension and appreciation. For example, Gerrig and Healy (1983) demonstrated that ratings of metaphor quality (i.e., the goodness of metaphors) had no reliable effect on reading times of the metaphors. McCabe (1983) showed that metaphor quality was not correlated with either ratings of topic-vehicle similarity or ratings of comprehensibility when metaphors were presented in an extended natural context. On the other hand, recent studies (e.g., Utsumi, 2005, 2006; Taira, Kusumi, & Utsumi, 2012) have demonstrated the strong relationship between metaphor comprehension and appreciation. For example, Utsumi (2005) found that metaphor poeticality was correlated with the richness of interpretation (i.e., interpretive diversity). He also suggested that the relationship of comprehension and appreciation may differ between comprehensible and less comprehensible metaphors. In this paper, we address this question and examine whether metaphor comprehension affects metaphor appreciation by imposing a constraint on the comprehension time of metaphors.
Cooperative Motion Planning Method for Two Anthropomorphic Manipulators

The development of methods for planning joint movements of two anthropomorphic manipulators in a common operational space becomes relevant. This is due to the development of anthropomorphic robots. Existing methods have relatively large computational complexity. They cannot be used on platforms with limited computing resources. This article proposes an approximate analytical method for planning of joint movements of two anthropomorphic manipulators for point-to-point movements. The analytical method is based on the geometric interpretation of the kinematic redundancy of anthropomorphic manipulators. The method includes three stages. At the first stage, the movement of the wrists of the manipulators is planned, at the second stage elbow joints is planned, and at the third stage end effectors is planned. In the event of a possible collision of manipulators, correction of their path is carried out by “pushing”. Its computational complexity is about thousand operations. Simulation of the proposed method showed its efficiency. This method has prospects for implementation in existing manipulator control systems. Keywords—anthropomorphic manipulator, robotic arm, cooperative motion planning, analytical method, geometric method, two manipulators.
IN-PACKAGE COLD PLASMA TREATMENT OF RAW MEAT

The effects of in-package cold plasma (IP-CP) treatments on the safety and quality of fresh meat were evaluated using chicken breast meat (pectoralis major) as a model. Raw breast meat was packed in trays, treated at 70 kV for different times (0, 60, 180, or 300 sec), and stored at 4oC for 5 days. Campylobacter, Salmonella, psychrophiles, and CIE L*a*b* were measured before and after the IP-CP treatments. Populations of foodborne pathogens Campylobacter and Salmonella were significantly lower and psychrophile growth was inhibited after IP-CP treatments. There were no preand post-treatment differences in L*a*b* values on the meat surface. Results indicate that IP-CP can reduce meat spoilage and foodborne pathogen risks without negatively impacting meat appearance.
ectiveness of Driver Isolation and Testing in User Space

1 e Device Drivers Problem Device Drivers have oen been identied as a major source of reliability issues in commodity operating systems (OSs). Static analyses of the Linux kernel show that the majority of detected bugs reside in driver code [3, 11]. ese ndings suggest that driver code is particularly prone to errors or soware bugs. One explanation for this observation is that driver development in kernel space is generally a challenging task requiring experience and expert knowledge. Drivers are inherently complex due to their asynchronous nature, the close interaction with kernel and hardware interfaces, and the support for dierent hardware devices (revisions) in one driver. Moreover, kernel space development suers from a lack of tool support compared to user space development [8]. Bugs in driver code have severe consequences for the whole system. In most (monolithic) OSs, drivers are heavily coupled extensions of the kernel that are linked to the kernel binary at compileor run-time. ey share the same address space with the kernel and operate with highest privileges. Hence, buggy (or malicious) drivers can read/write arbitrary memory and program hardware devices and buses with unforeseeable eects on the system, ranging from crashes to permanent data corruption. Existing approaches strive to improve the situation are manifold: (1) Driver isolation approaches (e.g. [2, 17]) attempt to contain faults and there eects such that the kernel is not aected. (2) Driver recovery (e.g. [16]) aempts to detect driver failures and (transparently) recover to a clean state. (3) Driver synthesis, new architectures and languages (e.g. [13–15]) aempt to make writing drivers easier. (4) Testing approaches (e.g. [12]) try to make testing drivers easier and more comprehensive. However, none of the existing approaches is widely adopted in practice although isolation approaches, which aempt to break the heavy kernel–driver coupling, promise ad-hoc reliability improvements.
TongAI : Helping Neuroradiologists Do Better Things

Neuroradiologists spend a large portion of their time recommending an imaging protocol based on a doctor’s patient description. In conjunction with the Stanford Hospital, we compile a novel dataset and implement state of the art Deep Learning NLP techniques to automatically make the protocol recommendation. We find that a combination of FastText embeddings and a custom FastText model variant provide great overall results. Our research indicates that this is the first time someone has attempted to automate this task.
Immersive Urban Analytics through Exploded Views

A BSTRACT Recent years have witnessed the rapid development and wide adoption of immersive head-mounted devices, such as HTC VIVE, Oculus Rift, and Microsoft HoloLens. These immersive devices have native support for displaying 3D models in an immersive environment. The capability has the potential to signiﬁcantly extend the methodology of urban visual analytics by providing critical 3D context information and creating a sense of presence. However, 3D city models may cause severe occlusion problems, blocking the path toward enabling immersive urban analytics. This paper presents a novel technique to create an exploded view in immersive environments. The 3D models are ﬁrstly exploded and grouped into layers. Then we formulate and solve a nonlinear least squares optimization problem to ensure the visibility, compactness, and alignment of all layers. Different from existing methods, our method is tailored to analyze urban data in immersive environments. We further conduct experiments to compare our method with two different methods and exam our method in various situations based on the HoloLens platform.
Genetics Identification of a Candidate Gene for Astigmatism

PURPOSE. Astigmatism is a common refractive error that reduces vision, where the curvature and refractive power of the cornea in one meridian are less than those of the perpendicular axis. It is a complex trait likely to be influenced by both genetic and environmental factors. Twin studies of astigmatism have found approximately 60% of phenotypic variance is explained by genetic factors. This study aimed to identify susceptibility loci for astigmatism.