Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hierarchical Speech Emotion Classification Framework based on Joint Triplet-Center Loss

Automatic speech emotion recognition task is crucial to the development of human-computer interaction systems. However, the ambiguity of emotion categories and the subjectivity of human annotations make it hard to extract discriminative emotional features and improve the classification accuracy. In this paper, we propose a Joint Triplet-Center Loss based hierarchical learning method. On the one hand, the proposed Joint Triplet-Center Loss function can learn discriminative emotional features through reducing the intra-class distance and increasing the inter-class distance. On the other hand, the hierarchical learning method can enhance the stability of the model by considering the consistency of annotations. The experimental results show that our proposed method has obvious performance improvement compared with previous works, and gets better generalization performance.
EFFECT OF HIGH DILUTIONS OF ACETYLCHOLINE AND ATROPINE ON HEART RATE: A STUDY ON ISOLATED RABBIT'S PERFUSED HEART

Effects of Acetylcholine (ACh) on heat are same as those of vagal stimulation thus; it slows the rate and diminishes the force of beat, which is abolished by Atropine (Atr). It is not known that high dilution (beyond Avagadro’s number) of ACh and Atr can produce same pharmacological effect or not, that is reported for other substances (Jerman, et al.,1999) with their simple and succussed dilutions. In the present study, such dilutions (ranging from 10 -3 to 10 -36 ) of both the drugs have been tested on the isolated Rabbit’s heart perfused by using Langendorff assembly. Statistically significant difference has been observed between both series at 10 -4 (ACh) and 10
A density functional study of superlattice formation in binary hard-sphere mixtures

A theoretical investigation of a binary mixture of hard spheres confirms that stable superlattice structures, with a complex long-range order of the AB13 type, can form in these simple systems for intermediate values of the diameter ratio, in agreement with recent computer simulations and experimental studies of colloidal suspensions. It is shown that the larger entropy of mixing of the AB13 structure relative to that of the competing structures is responsible for its thermodynamic stability.
Occurence of benzo(a)pyrene in some foods of animal origin in the Slovak Republic

SUMMARY A total of 10 841 food products of animal origin such as smoked meat products, canned meat products, poultry products, fish and fish products and animal fats to be sold on the Slovakian market were analysed for the presence of benzo[a]pyrene (BaP) in accredited state control laboratories in the frame of official controls and monitoring of food quality during 1994–2005. Results were summarized and compared with maximum acceptable levels set by European Commission Regulation (EC) No 1881/2006. As found, BaP contents were above the limit in 1.6% of all foods. The maximum acceptable BaP contents of 5 μg. kg-1 were above the limit mainly in smoked meat products, where also extremely high BaP content at the level of 700 μg. kg-1 was found in sausage smoked at uncontrolled domestic conditions. Total average BaP contents in controlled food commodities were as follows: fish and fish products 1.15 μg. kg-1, smoked meat products - 1.02 μg. kg-1, animal fats - 0.35 μg. kg-1, poultry products - 0.30 μg. kg-1 and canned meat products - 0.25 μg. kg-1.
Joint Geometry and Color Point Cloud Denoising Based on Graph Wavelets

A point cloud is an effective 3D geometrical presentation of data paired with different attributes such as transparency, normal and color of each point. The imperfect acquisition process of a 3D point cloud usually generates a significant amount of noise. Hence, point cloud denoising has received a lot of attention. Most of the existing techniques perform point cloud denoising based only on the geometry information of the neighbouring points; there are very few works considering the problem of denoising of color attributes of a point cloud, and taking advantage of the correlation between geometry and color. In this article, we introduce a novel non-iterative set-up for the denoising of point cloud based on spectral graph wavelet transform (SGW) that jointly exploits geometry and color to perform denoising of geometry and color attributes in graph spectral domain. The designed framework is based on the construction of joint geometry and color graph that compacts the energy of smooth graph signals in the low-frequency bands. The noise is then removed from the spectral graph wavelet coefficients by applying data-driven adaptive soft-thresholding. Extensive simulation results show that the proposed denoising technique significantly outperforms state-of-the-art methods using both subjective and objective quality metrics.
A simplified load voltage control scheme for power conversion applications using modular multilevel converters

Modular Multilevel converters (MMCs) have gained popularity in power conversion applications such as HVDC and renewable energy systems. A major challenge in these applications is the simultaneous control of capacitor voltages, arm currents, and output voltage, since the control of one has impact on the other. This paper presents a novel control scheme to regulate the output voltage and capacitor voltages of MMC using a simplified control loop utilizing its inherent voltage balancing property. The proposed scheme reduces the number of control loops required, is easy to implement, and is scalable to higher number of sub modules. Unlike the existing control, this scheme does not use additional control loops for individual sub modules and has less computational load on the controller. The proposed method is successfully applied to control a 1.6 kVA MMC with four sub modules in the leg. Simulation and experimental results presented prove its efficacy in terms of steady state and dynamic performances.
Influence of polar and unpolar silica functionalization on the dielectric properties of PP/POE nanocomposites

This study focuses on the influence of polar and unpolar surface functionalization of silica on the dielectric performance of PP/POE (polypropylene/poly(ethylene-co-octene)) nanocomposites. The silica fillers were surface-modified with unpoalr trimethylethoxysilane (TMES) or polar 3aminopropyl triethoxysilane (APTES). Fourier-Transform Infrared Spectroscopy and Thermogravimetric analysis were performed and confirmed qualitatively and quantitatively the successful silica-silane modification. Silica/PP/POE nanocomposite films were prepared using a mini-extruder. Scanning Electron Microscope images showed good dispersion of the modified silica in the PP/POE matrix. However, the polar silica cluster size (300 nm) is slightly larger than the one of the unpolar silica (100 nm). Thermally Stimulated Depolarization Current (TSDC) characterization data showed that the polar silica introduced deeper charge traps than the unpolar one, which also showed higher trap density. The apparent conductivity measured during the TSDC poling phase indicated that the polar silica filled nanocomposites featured very fast polarization to reach the saturation state, while the unpolar silica had a rather slow polarization rate. From Pulsed Electroacoustic Analysis it was obvious that the space charge is suppressed by addition of the polar silica, but increased by the presence of the unpolar silica.
Eyes speak out Mind: Deep models for Gaze-based Analysis of Bilingual And Monolingual Reading

This paper presents an approach that attempts to explore effects of bilingualism in reading by analysis of eye-gaze data. As readers are more comfortable in reading first language (L1) compared to second language (L2), their eyes move differently in these two cases. Being motivated by this hypothesis, a deep learning model is developed to predict whether a reader is reading first or second language by analyzing the reading behavior given by the eye-tracking data. Exposure to different languages simultaneously may influence the proficiency in native language reading and this matter is also investigated by using deep learning model which can differentiate monolingual readers from bilingual readers analyzing their eye movement during reading in their respective native language. Experiments are conducted on GECO Corpus [1] which provides eye-tracking data of 19 readers reading a novel in L1 and L2 along with eye-tracking data of several monolingual readers reading same text in their native language. Experimental results show that the proposed models are quite efficient in capturing the differences in eye gaze pattern for reading L1 and L2 for a reader as well as in identifying monolingual and bilingual readers by processing eye-gaze data.
Marine Cold Chain Transportation Monitoring and Route Scheduling Optimization Based on IoV-BDS

Recently, the research of the internet of vessels system is a hotspot in the marine engineering area. In this article, the Internet of Vessels based on Beidou satellite navigation system (IoV-BDS) architecture is proposed for marine cold chain transportation monitoring and scheduling. An improved communication method using Beidou short-message communication (SMC) is designed to ensure the reliability of ship-shore data transmission. Additionally, based on the IoV-BDS, we proposed a novel route scheduling optimization model which considers the key costs for marine cold chain transportation, such as fuel, catches damage, and refrigeration energy consumption, and designed a speed selection mechanism. Furthermore, an improved ant colony optimization (IACO) algorithm was utilized to optimize the whole fleet transportation routes, which takes advantage of the A-star algorithm to speed up the convergence of ant colony algorithm (ACO), a novel state transition rule was used to prevent falling into a locally optimal solution. The applicability of IoV-BDS was verified by the shipboard data acquisition and Beidou SMC experiments. The performance evaluation and analysis of route optimization schemes were presented by simulations and comparison with other schemes. The results demonstrated the benefit of our proposed schemes, in terms of solution quality and convergence of the algorithm.
Algorithm for Fog-degraded Image Enhancement Based on Adaptive Fractional-order PDE

In this study, an image enhancement model based on an inverse diffusion equation with a Riesz fractional derivative is developed. The optimal orders of fractional derivatives for the images are obtained through experiments, and six quantities associated with the brightness and texture of each haze image—mean, variance, skewness, kurtosis, two-norm, and contrast—are calculated. By regression analysis, the linear relationship between the optimal order of the fractional derivative and these statistical quantities is acquired. Finally, the empirical formula for the order of the fractional derivative for the differential equation model is obtained. Experimental results show that the adaptive order of the fractional differential obtained using our approach is close to the optimal order obtained using artificial experiments and that the enhancement effect is optimal. Compared with other image enhancement algorithms, our algorithm can better improve the brightness and contrast of images and provide a better visual effect while defogging. Two objective evaluation indexes— information entropy and average gradient—also indicate the effectiveness of our method.
Interpreting a Black-Box Model used for SCADA Attack detection in Gas Pipelines Control System

Various Machine Learning techniques are considered to be "black-boxes" because of their limited interpretability and explainability. This cannot be afforded, especially in the domain of Cyber-Physical Systems, where there can be huge losses of infrastructure of industries and Governments. Supervisory Control And Data Acquisition (SCADA) systems need to detect and be protected from cyber-attacks. Thus, we need to adopt approaches that make the system secure, can explain predictions made by model, and interpret the model in a human-understandable format. Recently, Autoencoders have shown great success in attack detection in SCADA systems. Numerous interpretable machine learning techniques are developed to help us explain and interpret models. The work presented here is a novel approach to use techniques like Local Interpretable Model-Agnostic Explanations (LIME) and Layer-wise Relevance Propagation (LRP) for interpretation of Autoencoder networks trained on a Gas Pipelines Control System to detect attacks in the system.
The High-Temperature Phases of WO3

Abstract High-temperature, high-resolution neutron powder diffraction experiments were performed to investigate the phases of WO 3 between room temperature and 850°C. Two phases were found and characterized by Rietveld refinements: orthorhombic β -WO 3 ( Pbcn , a =7.3331(2), b =7.5733(2), c =7.7401(3) A at 350°C, tilt system a 0 b + c − ) and tetragonal α -WO 3 ( P 4/ ncc , a = 5.2759(1), b =5.2759(1), c =7.8462(3) A at 800°C, tilt system a 0 a 0 c − ). The sequence of temperature-induced phase transitions in WO 3 can be rationalized in terms of changes in the octahedral tilt systems and/or displacements of the tungsten out of the center of the WO 6 octahedron. Above room temperature the two phase transitions are driven by sucessive softening of phonon modes, M 3 at the α - to β -transition and R 25 at the β - to γ -transition.
Magnetic properties of Mn, Ni and Fe based metal-organic complexes

This dissertation presents the investigation of magnetic exchange and anisotropy in novel metal-organic complexes containing minimum number of magnetic ions. Such complexes can serve as a model system to understand the exciting magnetic phenomenas in such class of materials and also can put forward as candidates for the so called molecular nanomagnets. A direct assessment of the effective magnetic moment and the effective interaction between the metal ions in the complex can be done using magnetization measurements. Here the magnetization studies are performed as a function of temperature and field using a SQUID magnetometer. Yet another powerful tool to characterize and determine the spin levels, the ESR spectroscopic methods, has also been exploited. The study of the dynamical properties of this class of materials was relevant to understand the relaxation mechanism in the low temperatures. For this a new ac susceptometer has been built in house which was another main objective of this dissertation work. The design, fabrication, calibration and automation done on this device is presented in this thesis. The device has been tested using the known molecular magnet Mn12 acetate, and the antiferromagnet Dy2PdSi3. The present work is mainly focused on the magnetic properties of Mn, Ni and Fe based organometallic complexes. The studied Mn dimer with different acceptor and donor ligands exhibit the fine tuning of the electron density at the core of molecular complex by variation in ligands. This in turn shows that the change in peripheral ligands can control the magnetism of the molecule. The influence of the change in Ni-S-Ni bond angle in the magnetic exchange interaction is studied in a Ni(II) dimer and a Ni(II)trimer complex. The Ni dimer complex shows a ferromagnetic interaction (J = −42 K) whereas trimer shows an antiferromagnetic interaction (J = 140 K). Another Ni based complex bridged via phosphorous has been studied which shows the existence of glassy nature at low temperature. Also a polymeric chain compound based on Fe is studied and presented. All these phosphorous or sulphur bridged complexes are novel materials and these are the first data on these complexes. This page intentionally contains only this sentence.
Incorporating Unmodeled Dynamics Into First-Principles Models Through Machine Learning

First-principles modeling of dynamical systems is a cornerstone of science and engineering and has enabled rapid development and improvement of key technologies such as chemical reactors, electrical circuits, and communication networks. In various disciplines, scientists structure the available domain knowledge into a system of differential equations. When designed, calibrated, and validated appropriately, these equations are used to analyze and predict the dynamics of the system. However, perfect knowledge is usually not accessible in real-world problems. The incorporated knowledge thus is a simplification of the real system and is limited by the underlying assumptions. This limits the extent to which the model reflects reality. The resulting lack of predictive power severely hampers the application potential of such models. Here we introduce a framework that incorporates machine learning into existing first-principles modeling. The machine learning model fills in the knowledge gaps of the first-principles model, capturing the unmodeled dynamics and thus improving the representativeness of the model. Moreover, we show that this approach lowers the data requirements, both in quantity and quality, and improves the generalization ability in comparison with a purely data-driven approach. This approach can be applied to any first-principles model with sufficient data available and has tremendous potential in many fields.
Aging in CMOS RF Linear Power Amplifiers: An Experimental Study

An extensive experimental analysis of the hot carrier injection (HCI) and bias temperature instability (BTI) aging effects on RF linear power amplifiers (PAs) is presented in this article. Two different 2.45-GHz PA topologies have been implemented in a CMOS 65-nm technology: one based on a classical common-source (CS) and choke inductor and another one based on a complementary current-reuse (CR) circuit, both of them producing similar gain and output 1-dB compression point (<inline-formula> <tex-math notation="LaTeX">$P_{-1\,\text {dB}}$ </tex-math></inline-formula>). These circuits have been stressed to produce accelerated aging degradation, by applying increasing supply (<inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {DD}}$ </tex-math></inline-formula>) voltages or increasing RF input powers (<inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {IN}}$ </tex-math></inline-formula>). The degradation on the transistor parameters (threshold voltage and mobility), dc bias point (<inline-formula> <tex-math notation="LaTeX">$I_{\mathrm {dc}}$ </tex-math></inline-formula> current), and RF performance (gain, matching, and compression point) has been simultaneously measured. This has allowed us to observe how the reduced transistor degradation in CR PA results in higher robustness in its RF parameters compared with the CS PA circuit. The equivalent root-mean-square (rms) voltages have been proposed as an observable metric to assess the combined <inline-formula> <tex-math notation="LaTeX">$\text {dc} + \text {RF}$ </tex-math></inline-formula> stress in a PA circuit. This has been applied to a semianalytical model, providing comprehension of the link between the conditions under which a circuit is operated, the degradation of the transistor parameters, and the effects on the dc current and RF performance.
MENPS: A Decentralized Distributed Shared Memory Exploiting RDMA

The spread of RDMA-capable interconnects on supercomputers has enabled the middleware developers to explore new design options for runtime systems based on efficient communications. Observing low-latency networks and shared-memory infrastructure for multi-core processors, we have focused on extending shared-memory abstraction into multiple nodes exploiting RDMA, i.e., Distributed Shared Memory (DSM). We have found that the traditional protocols of DSM designed for two-sided communications cannot fully exploit the performance of RDMA, which necessitates decentralization and coarse-grained communications. To solve this problem, we introduced two methods for the DSM coherence protocol to exploit RDMA and implemented a DSM library MENPS using this protocol. Our evaluation shows that MENPS could accelerate two of five shared-memory applications with minimal modifications and beat an existing RDMA-based DSM runtime.
Finite State Machine-Based Motion-Free Learning of Biped Walking

Recently, deep reinforcement learning (DRL) is commonly used to create controllers for physically simulated characters. Among DRL-based approaches, imitation learning for character control using motion capture clips as tracking references has shown successful results in controlling various motor skills with natural movement. However, the output motion tends to be constrained close to the reference motion, and thus the learning of various styles of motion requires many motion clips. In this paper, we present a DRL method for learning a finite state machine (FSM) based policy in a motion-free manner (without the use of any motion data), which controls a simulated character to produce a gait as specified by the desired gait parameters. The control policy learns to output the target pose for each FSM state and transition timing between states, based on the character state at the beginning of each step and the user-specified gait parameters, such as the desired step length or maximum swing foot height. The combination of FSM-based policy learning and simple linear balance feedback embedded in the base controller has a positive synergistic effect on the performance of the learned policy. The learned policy allows the simulated character to walk as instructed by the continuously changing the gait parameters while responding to external perturbations. We demonstrate the effectiveness of our approach through interactive control, external push, comparison, and ablation studies.
Investigation of Dielectric Properties of Composite Films of Bacterial Cellulose with Carbon Nanotubes

The results of research of dielectric properties of samples of composite material made of nano-gel film of bacterial cellulose with the addition of carbon nanotubes with different concentrations are presented. The morphology of the obtained materials is determined. It was found that the specific electrical resistance of the material samples was in the range from 1 T$\Omega$/m to 80 G$\Omega$/m depending on the concentration of nanotubes, and the breakdown electric field strength at alternating current was from 7 kV/mm to 16 kV/mm. It is shown that the composite film with the addition of carbon nanotubes significantly longer resists against through electrical micro-discharges than paper made of vegetable cellulose.
Silica surface modification with liquid rubbers & functional groups for new polyolefin-based dielectric nano-composites

A novel hybrid approach for surface modification of nano-silica for polyolefin-based dielectric composites is presented. A fumed nano-silica was modified with long oligomeric chains of a liquid rubber, connected via urethane bonds to the silica surface increasing the compatibility with the matrix. Additionally, a secondary amino-silane was grafted in order to alter the charge trapping properties of the silica. The long oligomeric chains provide good dispersibility of the nanosilica in polypropylene/poly(ethylene-co-octene) (PP/POE) blends, while the functional urethane group along with a secondary amino-silane affected the charge trapping properties of the nano-composites by reducing the charge traps density and simultaneously increasing their depth. Scanning Electron Microscopy along with Differential Scanning Calorimetry revealed that the silica is preferably located in the PP phase, most likely due to its lower viscosity in comparison to POE. The silica particles exhibit a nucleating effect visible in a noticeable increase of the crystallization temperature of the PP phase, especially when the silica surface treatment is performed.