Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Training Target of DNN Used for Casa-Based Speech Enhancement

Efficient training target plays a vital role in any training model for speech enhancement. In the Computational Auditory Scene Analysis method based on Deep Neural Networks, the ideal ratio mask or square-root ideal ratio mask is usually considered as the effective training target. In this paper, we propose a novel training target method for speech enhancement. This new training target takes into consideration the inter channel correlations of the power spectra of the noisy speech, clean speech and noise to more efficiently retain speech components and mask the noise components. Additionally, the channel-weight contour based on the equal loudness hearing attribute is introduced to revise the training target in each Gammatone channel to make the resynthesized signal more suitable for hearing characteristics. Moreover, the training target is smoothed to further improve the accuracy. Experiments show that using the proposed training target achieves better performances in terms of speech quality and intelligibility.
ARBITER+: Adaptive Rate-Based InTElligent HTTP StReaming Algorithm for Mobile Networks

Dynamic adaptive streaming over HTTP (DASH) is widely adopted for video transport by major content providers. However, the inherent high variability in both encoded video and network rates represents a key challenge for designing efficient adaptation algorithms. Accommodating such variability in the adaptation logic design is essential for achieving a high user quality of Experience (QoE). In this paper, we present ARBITER+ as a novel adaptation algorithm for DASH. ARBITER+ integrates different components that are designed to ensure a high video QoE while accommodating inherent system variabilities. These components include a tunable adaptive target rate estimator, hybrid throughput sampling, controlled switching, and short-term actual video rate tracking. We extensively evaluate the streaming performance using real video and cellular network traces. We show that ARBITER+ components work in harmony to balance temporal and visual QoE aspects. Additionally, we show that ARBITER+ enjoys a noticeable QoE margin in comparison to state-of-the-art adaptation approaches in various operating conditions. Furthermore, we show that ARBITER+ also achieves the best application-level fairness when a group of mobile video clients shares a cellular base station.
A Coupled Land Surface and Radiative Transfer Models Based on Relief Correction for a Reliable Land Data Assimilation Over Mountainous Terrain

This letter presents a new approach to incorporate topographic relief effect in land data assimilation system over mountainous terrain. The conventional radiative transfer model (RTM) used for assimilation of microwave brightness temperature (Tb) is subjected to systematic bias owing to its flat earth model assumption. This is theoretically important for direct Tb assimilation system since a difference between simulated and observed Tb may disarray the assimilation system. Here, we consider three crucial relief effects, namely: 1) change in local incidence angle; 2) rotation of the plane of polarization; and 3) effect of pixels in shadow not visible to radiometer. Results indicate that the RTM formulation with relief effects (Topo) significantly reduce the bias as compared to the conventional RTM (Flat). The simulated Tb for the Topo case shows improved sensitivity toward soil moisture as compared to the Flat case. The proposed land surface model (LSM)–RTM assimilation framework has an immense potential source for initialization of LSM for seasonal climate prediction over mountainous terrain.
Enlarged Optic Nerve Axons and Reduced Visual Function in Mice with Defective Microfibrils

Abstract Glaucoma is a leading cause of irreversible vision loss due to retinal ganglion cell (RGC) degeneration that develops slowly with age. Elevated intraocular pressure (IOP) is a significant risk factor, although many patients develop glaucoma with IOP in the normal range. Mutations in microfibril-associated genes cause glaucoma in animal models, suggesting the hypothesis that microfibril defects contribute to glaucoma. To test this hypothesis, we investigated IOP and functional/structural correlates of RGC degeneration in mice of either sex with abnormal microfibrils due to heterozygous Tsk mutation of the fibrilin-1 gene (Fbn1Tsk /+). Although IOP was not affected, Fbn1Tsk /+ mice developed functional deficits at advanced age consistent with glaucoma, including reduced RGC responses in electroretinogram (ERG) experiments. While RGC density in the retina was not affected, the density of RGC axons in the optic nerve was significantly reduced in Fbn1Tsk /+ mice. However, reduced axon density correlated with expanded optic nerves, resulting in similar numbers of axons in Fbn1Tsk /+ and control nerves. Axons in the optic nerves of Fbn1Tsk /+ mice were significantly enlarged and axon diameter was strongly correlated with optic nerve area, as has been reported in early pathogenesis of the DBA/2J mouse model of glaucoma. Our results suggest that microfibril abnormalities can lead to phenotypes found in early-stage glaucomatous neurodegeneration. Thinning of the elastic fiber-rich pia mater was found in Fbn1Tsk /+ mice, suggesting mechanisms allowing for optic nerve expansion and a possible biomechanical contribution to determination of axon caliber.
Biomass recalcitrance in barley, wheat and triticale straw: Correlation of biomass quality with classic agronomical traits

The global production of cereal straw as an agricultural by-product presents a significant source of biomass, which could be used as feedstock for the production of second generation biofuels by fermentation. The production of sugars for fermentation is an important measure of straw quality and in its suitability for biofuel production. In this paper, we present a characterization of straw digestibility from a wide range of cereal. Our main objective is to evaluate the variability of fermentable sugars released from different species including wheat (Triticum durum L., Triticum aestivum L.), barley (Hordeum vulgare L.) and triticale (X Triticosecale Wittmack). To this end, we adapted a saccharification method (IAS Method) capable of detecting significant differences of released sugars between cultivars and species, while using separately another method that would serve as a control and with which we could contrast our results (CNAP method). ANOVA analyses revealed that barley has a higher saccharification potential than wheat and triticale and shows more variation between genotypes. Thus, populations derived from crosses among them such as Steptoe × Morex and OWB Dominant × OWB Recessive hold potential for the identification of genetic basis for saccharification-related traits. The correlation of glucose released between the two methods was moderate (R2 = 0.57). An evaluation of the inter- and intra- specific correlation between a number of chemical and agronomical parameters and saccharification suggests that the cell wall thickness and lignin content in straw could be used in breeding programs for the improvement of the saccharification potential. Finally, the lack of correlation between grain yield and saccharification suggests that it would be possible to make a selection of genotypes for dual purpose, low recalcitrance and grain yield.
Who Let The Trolls Out?: Towards Understanding State-Sponsored Trolls

Recent evidence has emerged linking coordinated campaigns by state-sponsored actors to manipulate public opinion on the Web. Campaigns revolving around major political events are enacted via mission-focused ? trolls. " While trolls are involved in spreading disinformation on social media, there is little understanding of how they operate, what type of content they disseminate, how their strategies evolve over time, and how they influence the Web's in- formation ecosystem. In this paper, we begin to address this gap by analyzing 10M posts by 5.5K Twitter and Reddit users identified as Russian and Iranian state-sponsored trolls. We compare the behavior of each group of state-sponsored trolls with a focus on how their strategies change over time, the different campaigns they embark on, and differences between the trolls operated by Russia and Iran. Among other things, we find: 1) that Russian trolls were pro-Trump while Iranian trolls were anti-Trump; 2) evidence that campaigns undertaken by such actors are influenced by real-world events; and 3) that the behavior of such actors is not consistent over time, hence detection is not straightforward. Using Hawkes Processes, we quantify the influence these accounts have on pushing URLs on four platforms: Twitter, Reddit, 4chan's Politically Incorrect board (/pol/), and Gab. In general, Russian trolls were more influential and efficient in pushing URLs to all the other platforms with the exception of /pol/ where Iranians were more influential. Finally, we release our source code to ensure the reproducibility of our results and to encourage other researchers to work on understanding other emerging kinds of state-sponsored troll accounts on Twitter.
Model-Based Phase Recovery of Spectrograms via Optimization on Riemannian Manifolds

In acoustical signal processing, the importance of modifying the phase spectrogram has been shown. Recently, model-based phase recovery which is based on the sinusoidal model has been studied. Although their effectiveness has been proven, some of them deal with the phase in inflexible forms owing to the wrapping effect of phase. In addition, they need much pre-processing, including the estimation of the instantaneous frequency, which is not easy tasks. In order to overcome these issues, we propose a novel model-based phase recovery method which is formulated as an optimization over complex-valued phases. In the proposed method, the instantaneous frequency is not handled fixedly, which avoids the prior estimation of the instantaneous frequency. The technique of optimization on Riemannian manifolds is adopted for efficient computation. The proposed method is validated by noise reduction of audio signals.
Minimal/Measurable Residual Disease Monitoring in NPM1-Mutated Acute Myeloid Leukemia: A Clinical Viewpoint and Perspectives

Acute myeloid leukemia (AML) with NPM1 gene mutations is currently recognized as a distinct entity, due to its unique biological and clinical features. We summarize here the results of published studies investigating the clinical application of minimal/measurable residual disease (MRD) in patients with NPM1-mutated AML, receiving either intensive chemotherapy or hematopoietic stem cell transplantation. Several clinical trials have so far demonstrated a significant independent prognostic impact of molecular MRD monitoring in NPM1-mutated AML and, accordingly, the Consensus Document from the European Leukemia Net MRD Working Party has recently recommended that NPM1-mutated AML patients have MRD assessment at informative clinical timepoints during treatment and follow-up. However, several controversies remain, mainly with regard to the most clinically significant timepoints and the MRD thresholds to be considered, but also with respect to the optimal source to be analyzed, namely bone marrow or peripheral blood samples, and the correlation of MRD with other known prognostic indicators. Moreover, we discuss potential advantages, as well as drawbacks, of newer molecular technologies such as digital droplet PCR and next-generation sequencing in comparison to conventional RQ-PCR to quantify NPM1-mutated MRD. In conclusion, further prospective clinical trials are warranted to standardize MRD monitoring strategies and to optimize MRD-guided therapeutic interventions in NPM1-mutated AML patients.
RUNAMIC : dynamic generation of personalized running routes

: In this paper, we present a novel mobile running application, Runamic. Runamic allows to generate dynamic routes, starting from any point in the city, whilst taking user preferences into account. A user can choose how long he would like to run alongside his preferred points of interest, such as waterways, parks and/or tourist hotspots. Based on these preferences, the characteristics of the road network/environment and feedback on previous runs, a route is generated. The suggested routes are smooth, avoiding too many turns and overlapping route segments. The generated routes can be changed dynamically. After the run, the user has the ability to rate the generated route, which will inﬂuence future route generation. In most cases, the graph and R-tree based algorithm generates nice smooth routes, leading to very positive user feedback on the dynamic routing aspect.
Comparative harmonic analysis of Diode clamped multi-level inverter

Power quality has been main issue among the consumers nowadays. The consumer devices are based on power electronics devices for better efficiency which tend to introduce various current harmonics in our power system at the point of common coupling. With the accession of renewable energy sources as the better and environment friendly alternative to meet the increasing energy demand, the inverters find inevitable application in the integration of renewable energy plants with the grid, the quality of power has emerged as a future complication as these also tend to generate current harmonics in the line. It has been always difficult to maintain the quality of electric power so as to keep it within the acceptable limits. Diode clamped multilevel inverter is a matured topology used in several industries and also being used in renewable energy applications due to its several advantages over traditional two-level inverter. This paper is focused on the study of five-level and fifteen-level cascaded Hbridge inverter and total harmonic distortion in output voltage waveforms is compared. In lieu of this a diode clamped multilevel inverter is simulated in MATLAB/Simulink environment which reduces the current THD of the distorted signal below the limit defined as per the IEEE 519 Std.
Image Description Generation by Modeling the Relationship Between Objects

Automatically describing the content of an image is a challenging task in computer vision that connects the machine learning and natural language processing. In this paper, we present a framework, based on modeling image context, to generate natural sentences describing an image, which consists of two parts: relation modeling and description generating. By modeling the mapping from image spatial context to the logical relationship between objects, the former is trained to maximize the likelihood of the target linguistics phrase describing the relationship between object given the training image. By taking the the advantages of the syntactic-tree based method, the latter takes the predicted relationships as key ingredients to facilitate the image description generation within tree-growth process. We conduct extensive experimental evaluations on MS COCO dataset. Our framework outperforms the state-of-the-art methods. The results demonstrates that our framework provides robust and significant improvements for the relationship prediction between objects and the image description generation.
Senior fitness test; a useful tool to measure physical fitness in persons with acquired brain injury

ABSTRACT Objectives: To evaluate the feasibility and usability of the senior fitness test (SFT) in persons with acquired brain injury (ABI). Methods: A pilot cohort design with a convenience sample of persons with ABI was used. Results: Persons with ABIs (n = 47) were younger than their healthy counterparts (n = 172) were but performed significantly worse on sit to stand, 6-min walk test (6MWT) and 2.45-m up and go. This difference was accentuated in the age groups >60 years of age. Persons with ABIs, divided into subgroups traumatic brain injury (TBI; n = 12) and cerebral insult (CI; n = 35), showed significant differences in leg strength, upper extremity flexibility and walking capacity. Persons with CI were weaker, less flexible in upper and lower extremities, walked shorter distance and were less mobile. CI but not TBI performed significantly worse when compared to healthy elderly persons. Conclusion: This study indicates that SFT is feasible, safe and useful tool for persons with ABI, to evaluate physical capacity, endurance, strength and flexibility. The submaximal test was well tolerated and could be performed by all participants irrespective of age or diagnosis. The distribution of test scores indicates responsiveness to change and no ceiling or floor effects.
ExGate: Externally Controlled Gating for Feature-based Attention in Artificial Neural Networks

The perceptual capabilities of artificial systems have come a long way since the advent of deep learning. These methods have proven to be effective, however, they are not as efficient as their biological counterparts. Visual attention is a set of mechanisms that are employed in biological visual systems to ease the computational load by only processing pertinent parts of the stimuli. This paper addresses the implementation of top-down, feature-based attention in an artificial neural network by the use of externally controlled neuron gating. Our results showed a 5% increase in classification accuracy on the CIFAR-10 dataset versus a non-gated version while adding a limited number of parameters. Our gated model also produces more reasonable errors in predictions by drastically reducing the prediction of classes that belong to a different category than the true class.
Text Image Deblurring Using Kernel Sparsity Prior

Previous methods on text image motion deblurring seldom consider the sparse characteristics of the blur kernel. This paper proposes a new text image motion deblurring method by exploiting the sparse properties of both text image itself and kernel. It incorporates the <inline-formula> <tex-math notation="LaTeX">${L} _{\boldsymbol {0}}$ </tex-math></inline-formula>-norm for regularizing the blur kernel in the deblurring model, besides the <inline-formula> <tex-math notation="LaTeX">${L} _{\boldsymbol {0}}$ </tex-math></inline-formula> sparse priors for the text image and its gradient. Such a <inline-formula> <tex-math notation="LaTeX">${L} _{\boldsymbol {0}}$ </tex-math></inline-formula>-norm-based model is efficiently optimized by half-quadratic splitting coupled with the fast conjugate descent method. To further improve the quality of the recovered kernel, a structure-preserving kernel denoising method is also developed to filter out the noisy pixels, yielding a clean kernel curve. Experimental results show the superiority of the proposed method. The source code and results are available at: <uri>https://github.com/shenjianbing/text-image-deblur</uri>.
Effect of in vivo post-translational modifications of the HMGB1 protein upon binding to platinated DNA: a molecular simulation study

Abstract Cisplatin is one of the most widely used anticancer drugs. Its efficiency is unfortunately severely hampered by resistance. The High Mobility Group Box (HMGB) proteins may sensitize tumor cells to cisplatin by specifically binding to platinated DNA (PtDNA) lesions. In vivo, the HMGB/PtDNA binding is regulated by multisite post-translational modifications (PTMs). The impact of PTMs on the HMGB/PtDNA complex at atomistic level is here investigated by enhanced sampling molecular simulations. The PTMs turn out to affect the structure of the complex, the mobility of several regions (including the platinated site), and the nature of the protein/PtDNA non-covalent interactions. Overall, the multisite PTMs increase significantly the apparent synchrony of all the contacts between the protein and PtDNA. Consequently, the hydrophobic anchoring of the side chain of F37 between the two cross-linked guanines at the platinated site—a key element of the complexes formation - is more stable than in the complex without PTM. These differences can account for the experimentally measured greater affinity for PtDNA of the protein isoforms with PTMs. The collective behavior of multisite PTMs, as revealed here by the synchrony of contacts, may have a general significance for the modulation of intermolecular recognitions occurring in vivo.
Solving Jigsaw Puzzles By the Graph Connection Laplacian

We propose a novel mathematical framework to address the problem of automatically solving large jigsaw puzzles. This problem assumes a large image which is cut into equal square pieces that are arbitrarily rotated and shifted and asks to recover the original image given the transformed pieces. The main contribution of this work is a theoretically-guaranteed method for recovering the unknown orientations of the puzzle pieces by using the graph connection Laplacian associated with the puzzle. Iterative application of this method and other methods for recovering the unknown shifts result in a solution for the large jigsaw puzzle problem. This solution is not greedy, unlike many other solutions. Numerical experiments demonstrate the competitive performance of the proposed method.
Invasive Meningococcal Disease in the Vaccine Era

Infection with the meningococcus is one of the main causes of meningitis and septicaemia worldwide. Humans are the only natural reservoir for the meningococcus which is found primarily as a commensal inhabitant in the nasopharynx in ~10% of adults, and may be found in over 25% of individuals during adolescence. Prompt recognition of meningococcal infection and early aggressive treatment are essential in order to reduce mortality, which occurs in up to 10% of those with invasive meningococcal disease (IMD). This figure may be significantly higher in those with inadequate or delayed treatment. Early administration of effective parenteral antimicrobial therapy and prompt recognition and appropriate management of the complications of IMD, including circulatory shock and raised intracranial pressure (ICP), are critical to help improve patient outcome. This review summarizes clinical features of IMD and current treatment recommendations. We will discuss the evidence for immunization and effects of vaccine strategies, particularly following implementation of effective vaccines against Group B meningococcus.
Evaluating the Complementarity of Taxonomic Relation Extraction Methods Across Different Languages

Modern information systems are changing the idea of "data processing" to the idea of "concept processing", meaning that instead of processing words, such systems process semantic concepts which carry meaning and share contexts with other concepts. Ontology is commonly used as a structure that captures the knowledge about a certain area via providing concepts and relations between them. Traditionally, concept hierarchies have been built manually by knowledge engineers or domain experts. However, the manual construction of a concept hierarchy suffers from several limitations such as its coverage and the enormous costs of its extension and maintenance. Ontology learning, usually referred to the (semi-)automatic support in ontology development, is usually divided into steps, going from concepts identification, passing through hierarchy and non-hierarchy relations detection and, seldom, axiom extraction. It is reasonable to say that among these steps the current frontier is in the establishment of concept hierarchies, since this is the backbone of ontologies and, therefore, a good concept hierarchy is already a valuable resource for many ontology applications. The automatic construction of concept hierarchies from texts is a complex task and much work have been proposing approaches to better extract relations between concepts. These different proposals have never been contrasted against each other on the same set of data and across different languages. Such comparison is important to see whether they are complementary or incremental. Also, we can see whether they present different tendencies towards recall and precision. This paper evaluates these different methods on the basis of hierarchy metrics such as density and depth, and evaluation metrics such as Recall and Precision. Results shed light over the comprehensive set of methods according to the literature in the area.