Miracle-dz/UniFAR-FTUs huggingface dataset:
An In-Vehicle Keyword Spotting System with Multi-Source Fusion for Vehicle Applications

In order to maximize detection precision rate as well as the recall rate, this paper proposes an in-vehicle multisource fusion scheme in Keyword Spotting (KWS) System for vehicle applications. Vehicle information, as a new source for the original system, is collected by an in-vehicle data acquisition platform while the user is driving. A Deep Neural Network (DNN) is trained to extract acoustic features and make a speech classification. Based on the posterior probabilities obtained from DNN, the vehicle information including the speed and direction of vehicle is applied to choose the suitable parameter from a pair of sensitivity values for the KWS system. The experimental results show that the KWS system with the proposed multi-source fusion scheme can achieve better performances in term of precision rate, recall rate, and mean square error compared to the system without it.
ROLEPLAYING AS A CREATION FORM AND A SOURCE OF INSPIRATION IN RPG VIRTUAL WORLDS

Online roleplaying games, also called MMORPGs, are the most popular virtual worlds. A recurrent criticism about them is that they would offer only a succession of repetitive tasks. Exceptionally rich and complex player creations, challenge these comments. In this paper, we will study a specific aspect of these worlds: the “roleplay”, that is at the same time a creation form and a source of inspiration. We will begin by explaining what roleplaying means, in what way this practice is different in virtual worlds than in tabletop and live action roleplaying games. We will then examine player creations, divided in three parts: rule systems and events, writings and visual creations, as well as their motivations and creation process.
Hierarchical Congestion Control (HCC): Cooperation of Uncorrelated Flows for Better Fairness and Throughput

Congestion control protocols face several challenges for achieving max-min fairness and high throughput. First, each flow has a limited view of the network state. In the absence of a centralized congestion control entity, coordination is left (directly or indirectly) to individual flows. Second, most flows are very volatile by nature: flow rates/demands change significantly from one instant to another. In this poster, we present a hierarchical congestion control scheme to tackle these challenges. We aggregate flows with low correlation in a hierarchical manner, and recursively compute and allocate rates to these flows. Our preliminary experimental results show significant promise in terms of fairness and throughput.
The Fiscal Policy and the Stability of the nominal Sector: The Romanian Case (revisited version)

The fiscal policies in the contemporaneous economic systems heavy influence both the real and nominal sectors. These effects could be located at the primary distribution of the social resources as will as at level their redistribution one. The aims of this paper are: (1) to review the literature of the main conceptual frameworks which link the fiscal policy and the dynamic of real sector, especially on the inflation side (2) to advance an empirical analyze of these link for the Romanian case and (3) to draw some conclusion about desirable framework of the fiscal policy for the current period in the perspective of Romanian access to European Union.
A Tunable Bandstop Filter Based on Source Follower

A tunable bandstop filter based on source follower is presented. The filter is designed using a source follower architecture with partial positive feedback and feed-forward paths using 0. 18µm CMOS technology. The filter consumes about 1.4 mW from a 1.8V power supply, and it is designed to have 1.5GHz center frequency with 360 MHz bandwidth and 80 dB attenuation. The filter achieves 10 dBm IIP3 for two tones at 1.495GHz and 1.505 GHz.
Are Subtitling Corpora really Subtitle-like?

Growing needs in translating multimedia content have resulted in Neural Machine Translation (NMT) gradually becoming an established practice in the field of subtitling. Contrary to text translation, subtitling is subject to spatial and temporal constraints, which greatly increase the postprocessing effort required to restore the NMT output to a proper subtitle format. In this work, we explore whether existing subtitling corpora conform to the constraints of: 1) length and reading speed; and 2) proper line breaks. We show that the process of creating parallel sentence alignments removes important time and line break information and propose practices for creating resources for subtitlingoriented NMT faithful to the subtitle format.
Recurrent averaging inequalities, opinion formation and distributed algorithms

Models of iterative averaging constitute an important and extensively studied class of discrete-time dynamical systems over networks. In such models, each node of a network is associated to a value (or opinion), which is updated, at each iteration, to a convex combination of itself and the opinions of the neighboring nodes. It is well known that, under natural assumptions on the network's graph connectivity, this local averaging procedure eventually leads to global consensus of opinions at all nodes. The idea of iterative averaging actually lies at the heart of many algorithms for distributed optimization, for solution of linear and nonlinear equations, for multi-agent coordination and for opinion formation in social groups. Although these algorithms have similar structures, the mathematical techniques used for their analysis are manifold, and the conditions for their convergence and stability differ from case to case. We show in this paper that the properties of such algorithms can be analyzed in a unified way by using a novel tool based on recurrent averaging inequalities (RAI). In this paper, we develop a theory for RAI, and apply it to the analysis of several important multi-agent algorithms and models of opinion formation recently proposed in the literature.
Impact of nonlocal electron heat transport on the high temperature plasmas of LHD

Edge cooling experiments with a tracer-encapsulated solid pellet in the large helical device (LHD) show a significant rise in core electron temperature (the maximum rise is around 1 keV) as well as in many tokamaks. This experimental result indicates the possible presence of the nonlocality of electron heat transport in plasmas where turbulence as a cause of anomalous transport dominates. The nonlocal electron temperature rise in the LHD takes place in almost the same parametric domain (e.g. in a low density) as in the tokamaks. Meanwhile, the experimental results of LHD show some new aspects of nonlocal electron temperature rise, for example the delay in the nonlocal rise of core electron temperature relative to the pellet penetration time increases with the increase both in the collisionality in the core plasma and the electron temperature gradient scale length in the outer region of the plasma.
Low-Complexity Transceiver for GFDM systems with Partially Allocated Subcarriers

The conventional receiver designs of generalized frequency division multiplexing (GFDM) system assume full subcarrier allocation. In this case, the optimal linear receivers can be implemented with low-complexity channel equalization followed by zero-forcing (ZF) demodulation. In some use cases, e.g. multiuser, only a subset of the subcarriers is active for data transmission. Therefore, the optimal receiver design needs to consider the effective joint channel and modulation matrix, which complicates the practical implementation. To maintain low-complexity realization in these cases, full allocation can still be assumed, however, the performance loss is remarkable. In this paper, we propose an efficient transceiver design for non-fully allocated GFDM system. In the proposed approach, the frequency-domain (FD) sparsity of GFDM is exploited to represent the transmitted signal by means of an effective small-size GFDM model with one non-active subcarrier. Therefore, the assumption of full allocation becomes more realistic. Moreover, the received signal can be further reformulated with fully allocated system, but at the cost of altering the effective channel gains. The proposed design significantly reduces the computation cost of the practical GFDM receiver, whereas the performance still approaches the counterpart optimal linear receiver.
Cost reduction and continuous improvement: a longitudinal case study

In today’s environment one of the top priorities for organizations is to costs without affecting dramatically organization survival. Therefore, the aim of this paper is provide evidence on (1) the importance of Management Accounting information for cost reduction purposes and (2) the role of some Control System components in order to carry out this strategy. A longitudinal case study has been conducted to this purpose at a Spanish subsidiary of a multinational chemical group. This chemical plant introduced a cost reduction project in the mid 1990s. The most salient results were on the one hand, the implementation of quality circles and internal benchmarking practices and, on the other hand the importance of both formal and non-formal Control System components in order to successfully achieve the project’s objectives.
Beta-Band Resonance and Intrinsic Oscillations in a Biophysically Detailed Model of the Subthalamic Nucleus-Globus Pallidus Network

Increased beta-band oscillatory activity in the basal ganglia network is associated with Parkinsonian motor symptoms and is suppressed with medication and deep brain stimulation (DBS). The origins of the beta-band oscillations, however, remains unclear with both intrinsic oscillations arising within the subthalamic nucleus (STN)—external globus pallidus (GPe) network and exogenous beta-activity, originating outside the network, proposed as potential sources of the pathological activity. The aim of this study was to explore the relative contribution of autonomous oscillations and exogenous oscillatory inputs in the generation of pathological oscillatory activity in a biophysically detailed model of the parkinsonian STN-GPe network. The network model accounts for the integration of synaptic currents and their interaction with intrinsic membrane currents in dendritic structures within the STN and GPe. The model was used to investigate the development of beta-band synchrony and bursting within the STN-GPe network by changing the balance of excitation and inhibition in both nuclei, and by adding exogenous oscillatory inputs with varying phase relationships through the hyperdirect cortico-subthalamic and indirect striato-pallidal pathways. The model showed an intrinsic susceptibility to beta-band oscillations that was manifest in weak autonomously generated oscillations within the STN-GPe network and in selective amplification of exogenous beta-band synaptic inputs near the network's endogenous oscillation frequency. The frequency at which this resonance peak occurred was determined by the net level of excitatory drive to the network. Intrinsic or endogenously generated oscillations were too weak to support a pacemaker role for the STN-GPe network, however, they were considerably amplified by sparse cortical beta inputs and were further amplified by striatal beta inputs that promoted anti-phase firing of the cortex and GPe, resulting in maximum transient inhibition of STN neurons. The model elucidates a mechanism of cortical patterning of the STN-GPe network through feedback inhibition whereby intrinsic susceptibility to beta-band oscillations can lead to phase locked spiking under parkinsonian conditions. These results point to resonance of endogenous oscillations with exogenous patterning of the STN-GPe network as a mechanism of pathological synchronization, and a role for the pallido-striatal feedback loop in amplifying beta oscillations.
Epigenetic Programing of B-Cell Lymphoma by BCL6 and Its Genetic Deregulation

B cell lymphoma is a clinically heterogeneous and pathologically diverse group of diseases with a strong epigenetic component. The B cell lymphoma 6 (BCL6) gene encodes a transcription factor that is critical for normal germinal center reaction B cell development by maintaining an epigenetic and transcriptional state that is permissive for cellular proliferation and DNA damage. The activity of BCL6 can be deregulated by a variety of mechanisms and contributes to the development of B-cell lymphoma. Here we review the direct and indirect mechanisms BCL6 dysregulation in B cell lymphoma, including transcriptional and post-translational regulation of BCL6 expression and activity, and the perturbation of BCL6-regulated epigenetic programs by cooperating chromatin modifying gene mutations. We underscore the critical importance of BCL6 and its associated epigenetic programs in the development of B-cell lymphoma, and discuss avenues for the therapeutic targeting of BCL6 in this context.
Fractal Distribution of Medical Data in Neural Network

Nowadays the topic of deep learning is becoming more and more popular. Moreover, almost every organization want to have at least one specialist in this area, because artificial intelligence can help your medicine to grow and to increase its productivity. Research of one of the types of neural network – fractal neural network. Testing and comparing with other neural networks. We will take one dataset and test it on our neural networks and then compare the results. Trained and tested neural networks with graphs and comparisons of their output. In the current paper we implemented custom neural network and fractal neural network. Then we trained and tested them on CIFAR-10 dataset. Custom neural network showed us worse results, but each iteration took up to 10 seconds, when 1 iteration of fractal neural network took up to 3 minutes. Moreover, our network is pretty simple, so we can say that that is suits better for datasets with lower quantity of classes. Fractal neural network showed us pretty good results, but I am sure that with more powerful computing resources and more time it can perform much better.
Molecular Aspects of an Emerging Poplar Canker Caused by Lonsdalea populi

The Gram-negative bacterium Lonsdalea populi causes a lethal disease known as bark canker on Populus × euramericana in China and Europe. Typical symptoms of bark canker include an abundant white-colored fluid, which oozes from the infected tissues. The availability of the genomic sequence of the bacterium provided the necessary resource to launch genome-scale investigations into the mechanisms fundamental to pathogenesis. Functional analyses of a diverse group of genes encoding virulence factors and components of signaling pathways indicate that successful bark infection depends on specific responses by the pathogen to various stresses, including oxidative stress. Although physiology of resistance is well studied, the molecular processes underlying the defense responses and the genetic basis of resistance to L. populi and in other poplar species remain largely unknown. Control of the disease has relied on chemical measures. Due to the genetic amenability of Lonsdalea and poplar, this pathosystem will become an important model system to unravel molecular mechanisms of bacterial pathogenicity on woody plants. Increased understanding of pathogenesis and signaling in the interaction will facilitate the management of this kind of poplar canker.
Fault-Tolerant Control of Modular Permanent Magnet Synchronous Motor Under Open-Circuit Faults

The paper studies a novel fault-tolerant strategy of the modular permanent magnet synchronous motor under open-circuit faults, which is called as the extended open-circuit fault-tolerant control (EOCFTC) strategy. The faulty modular motor based on the EOCFTC strategy can achieve the high fault-tolerance and maximum output torque capability by making the most of the remaining healthy phases. First, the mathematical model of the modular motor with n modules is described. Then based on the characteristics of the modular motor, a novel winding reconstruction strategy is proposed to deal with multiphase open-circuit faults in different modules. All remaining healthy phases are reasonably reconstructed according to the maximum output torque principle. Then these new modules can operate well by employing the armature magnetomotive force (MMF) compensation and the field-orientated control (FOC) strategy. Finally, all cases of open-circuit faults and the corresponding processing methods are discussed. The extreme case of open-circuit faults is described in detail, which shows the high fault-tolerance of the modular motor with the EOCFTC strategy. The experimental results verify the rationality and feasibility of the EOCFTC strategy on a two-module modular motor.
Recent research trends and future prospects in nanozymes

Recently, nanomaterial-based enzyme mimetics (nanozymes) have attracted enormous interest. They exhibit unique advantages such as excellent robustness, stability, and low-cost production with easy scale-up, which are critically needed as an alternative to natural enzymes. These nanozymes exhibit natural enzyme-like activity and have been applied to various kinds of detection and treatment methods for biomolecules such as DNA, proteins, cells, and small molecules including glucose. To highlight progress in the field of nanozymes, this review discusses recent nanozyme-based research results and their applications for the development of novel biosensor, immunoassay, cancer diagnostics, therapeutics, and environmental engineering technologies. Current challenges and future prospects of nanozymes for widespread use in biotechnology are also discussed.
Bacterial Growth and Development in the Rhizosphere of Gnotobiotic Cereal Plants

The growth and development of a Curtobacterium sp. , a Mycoplana sp. and a Pseudomonas sp. in the rhizosphere of gnotobiotic wheat, barley and maize plants were studied. When inoculated singly in the wheat or barley rhizosphere, each of the three species grew exponentially during the first 48-72 h and slowly thereafter. However, in the maize rhizosphere the Curtobacterium sp. showed only a small increase in numbers and the Mycoplana sp. had a long lag phase, a slower growth rate and a smaller final population than when it was inoculated into the wheat or barley rhizosphere. Interactions between these three species in various combinations and in different rhizospheres are described and some of the possible mechanisms involved are discussed.
Coherent Financial Cycles for G-7 Countries: Why Extending Credit Can Be an Asset

We extract and analyse financial cycles for 13 European Union countries using a quarterly dataset spanning over 1971-2013. For identification of financial cycles, we employ a novel spectral approach determining the most important common cyclical fluctuations across total credit, residential property prices, equity prices, and benchmark bond yields. Results suggest that the most important financial cycles are on average 12 years, but with some dispersion across countries. Compared to business cycles, financial cycles have more important fluctuations in the medium term (8-20 years); but less important fluctuations in the short run (2.5-8 years). Regarding the extracted financial cycles, credit and residential property prices best summarize contemporaneous movements across financial indicators in almost all country cases.
Domain Knowledge Guided Deep Atrial Fibrillation Classification and Its Visual Interpretation

Hand-crafted features have been proven useful in solving the electrocardiograph~(ECG) classification problem. The features rely on domain knowledge and carry clinical meanings. However, the construction of the features requires tedious fine tuning in practice. Lately, a set of end-to-end deep neural network models have been proposed and show promising results in ECG classification. Though effective, such models learn patterns which usually mismatch human's concept, and thereby it is hard to get a convincing explanation with interpretation methods. This limitation significantly narrows the applicability of deep models, considering it is difficult for cardiologists to accept the unexplainable results from deep learning. To alleviate such limitation, we are bringing the best from the two worlds and propose a domain knowledge guided deep neural network. Specifically, we utilize a deep residual network as a classification framework, within which key feature ~(P-wave and R-peak position) reconstruction tasks are adopted to incorporate domain knowledge in the learning process. The reconstruction tasks make the model pay more attention to key feature points within ECG. Furthermore, we utilize occlusion method to get visual interpretation and design a visualization at both heartbeat level and feature point level. Our experiments show the superior performance of the proposed ECG classification methods compared to the model without P-wave and R-peak tasks, and the patterns learnt by our model is more explainable.
Multi-Scale Octave Convolutions for Robust Speech Recognition

We propose a multi-scale octave convolution layer to learn robust speech representations efficiently. Octave convolutions were introduced by Chen et al [1] in the computer vision field to reduce the spatial redundancy of the feature maps by decomposing the output of a convolutional layer into feature maps at two different spatial resolutions, one octave apart. This approach improved the efficiency as well as the accuracy of the CNN models. The accuracy gain was attributed to the enlargement of the receptive field in the original input space. We argue that octave convolutions likewise improve the robustness of learned representations due to the use of average pooling in the lower resolution group, acting as a low-pass filter. We test this hypothesis by evaluating on two noisy speech corpora – Aurora-4 and AMI. We extend the octave convolution concept to multiple resolution groups and multiple octaves. To evaluate the robustness of the inferred representations, we report the similarity between clean and noisy encodings using an affine projection loss as a proxy robustness measure. The results show that proposed method reduces the WER by up to 6.6% relative for Aurora-4 and 3.6% for AMI, while improving the computational efficiency of the CNN acoustic models.
Explicit Noether–Lefschetz for arbitrary threefolds

Abstract We study the Noether–Lefschetz locus of a very ample line bundle L on an arbitrary smooth threefold Y. Building on results of Green, Voisin and Otwinowska, we give explicit bounds, depending only on the Castelnuovo–Mumford regularity properties of L, on the codimension of the components of the Noether–Lefschetz locus of |L|.
Spotting Terrorists by Learning Behavior-aware Heterogeneous Network Embedding

Heterogeneous network is a useful data representation in depicting complex interactions among multi-typed entities and relations. In this work, by representing criminal and terrorism activities as a heterogeneous network, we propose a novel unsupervised method, Outlier Spotting with behavior-aware Network Embedding (OSNE), to identify terrorists among potential criminals. The basic idea of OSNE is to exploit high-order relation paths for translation-based embedding learning, and distinguish same-type entities based on behavior penalty and type-aware negative sampling. We evaluate the effectiveness of OSNE using six criminal network datasets provided by DARPA, and make comparison with strong competitors. The results exhibit the promising performance of OSNE.
Interpretable Reinforcement Learning Policies by Evolutionary Computation

In this thesis, three novel algorithms for generating interpretable policies in model-based batch reinforcement learning using particle swarm optimization and genetic programming are proposed and evaluated: FPSRL and FGPRL to generate rule-based policies, GPRL to generate equation-based policies. The interpretable policies show performance on the level of, or even surpassing that of control strategies by PID, MPC, NFQ, or neural network policies for a variety of different benchmark problems, including a novel industrial benchmark.