Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal joint Subchannel and Power Allocation for Green Underlay Device-to-Device Communication

In this paper, we provide an optimal solution of sub-channel and power allocation for the green underlay device to device (D2D) communication. The D2D communication is deployed by reusing sub-channels of a macrocell network for improving capacity and reducing power consumption in wireless network. We formulate an optimization problem to minimize the total CO2 emission while protecting the cellular users (CUs) and minimum data rate requirement of D2D pairs. Then, we find an optimal solution of subchannel and power allocation using a distributed algorithm based on the decomposition method. Finally, we show the proposed framework converging to an optimal solution.
RESEARCH ON AN AUTONOMOUS VISION-GUIDED HELICOPTER

We present an overview of the autonomous helicopter project at Carnegie Mellon's Robotics Institute. The goal of this project is to autonomously fly helicopters using computer vision closely integrated with other on-board sensors. We discuss a concrete example mission designed to demonstrate the viability of vision-based helicopter flight and specify the components necessary to accomplish this mission. Major components include customized vision processing hardware designed for high bandwidth and low latency processing and 6-degree-of-freedom test stand designed for realistic and safe indoor experiments using model helicopters. We describe our progress in accomplishing an indoor mission and show experimental results of estimating helicopter state with computer vision during actual flight experiments.
Proficiency of Graphene Oxide in Adsorption of Zn(II) Ions from Aqueous Solution

Graphene oxide proficient adsorption of metals are often affected by preparation method. Graphene oxide was prepared using atypical modified hummers method for adsorptive removal of Zn2+ from aqueous solution. The experimental data fitted into the Langmuir isotherm. The reaction was irreversible, but had a small energy of adsorption which was indicated by Freundlich isotherm model. Tempkin model and Dubinin Radushkevich revealed a physical adsorption process driven by a slow adsorptive process. The Pseudo 2nd order had the best correlation coefficient at 0.9958 while Langmuir was 0.9545. Elovich function indicated a non-spontaneous reaction, while the intra-particle diffusion was not the rate-determining step. The slow adsorption rate in pseudo 1st order correlated with the small energy of adsorption shown by Freundlich isotherm, and correlated the small boundary layer thickness shown by intra particle diffusion. All adsorptive functions showed that some other competing mechanisms were existing. XRD showed an increased number of graphene layers, d-spacing and improved crystallite size. FTIR data showed the oxygen containing groups on the graphene oxide surface. Hence, the study showed that the prepared graphene oxide proficiently adsorbed Zn2+ and affected by method of synthesis.
Overcoming forgetting in Batch-RL

We consider the task of estimating Q-function using Neural Nets as tools of function approximation, by experience replay in Batch Reinforcement learning setting. Based on lecture discussions, we believe that learning the network in this setting could be forgetful if the data generated by the given policy is replayed a large number of times. The larger number of replays might drift the learned parameters of the Q-network towards the most recently seen set of experiences, and this set of converged parameters might not represent the correct Q-values for the other sets of observed experience. We propose a way to allow learning in a Batch-RL setup to be less forgetful, which involves associating an importance value with each parameter of the network and imposing a penalty on the shift of highly important parameters.
Audio-Visual Autoencoding for Privacy-Preserving Video Streaming

The demand of sharing video streaming extremely increases due to the proliferation of Internet of Things (IoT) devices in recent years, and the explosive development of artificial intelligent (AI) detection techniques has made visual privacy protection more urgent and difficult than ever before. Although a number of approaches have been proposed, their essential drawbacks limit the effect of visual privacy protection in real applications. In this article, we propose a cycle vector-quantized variational autoencoder (cycle-VQ-VAE) framework to encode and decode the video with its extracted audio, which takes the advantage of multiple heterogeneous data sources in the video itself to protect individuals’ privacy. In our cycle-VQ-VAE framework, a fusion mechanism is designed to integrate the video and its extracted audio. Particularly, the extracted audio works as the random noise with a nonpatterned distribution, which outperforms the noise that follows a patterned distribution for hiding visual information in the video. Under this framework, we design two models, including the frame-to-frame (F2F) model and video-to-video (V2V) model, to obtain privacy-preserving video streaming. In F2F, the video is processed as a sequence of frames; while, in V2V, the relations between frames are utilized to deal with the video, greatly improving the performance of privacy protection, video compression, and video reconstruction. Moreover, the video streaming is compressed in our encoding process, which can resist side-channel inference attack during video transmission and reduce video transmission time. Through the real-data experiments, we validate the superiority of our models (F2F and V2V) over the existing methods in visual privacy protection, visual quality preservation, and video transmission efficiency. The codes of our model implementation and more experimental results are now available at https://github.com/ahahnut/cycle-VQ-VAE.
Transient Mitigation Using an Auxiliary Circuit in Cascaded DC–DC Converter Systems With Virtual Impedance Control

Most dc power distribution architectures are organized as a typical cascaded converter system that is composed of source and load converters, and may experience a system oscillation due to the negative impedance effect of constant power loads. The load-side virtual impedance method is a possible solution to such stability problem, which however makes the dynamic response of the load converter slow, and thus increases the requirement of the filter capacitance. In this article, an auxiliary circuit that is connected to the output port of the load converter is proposed to enhance the transient performance of the power converter system, while the system can be stabilized by the virtual impedance method. The control and parameter design of the proposed auxiliary circuit is introduced. The capacitance requirement of the system for an expected transient deviation of the output voltage is significantly reduced by using the proposed auxiliary circuit. An example design is given to verify the feasibility of the proposed method with simulation analysis and experimental results.
A BAYESIAN DERIVATION OF GENERALIZED COHERENCE DETECTORS Songsri Sirianunpiboon

The generalized coherence (GC) estimate is a well studied statistic for detection of a common but unknown signal on several noisy channels. In this paper, it is shown that the GC detector arises naturally from a Bayesian perspective. Specifically, it is derived as a test of the hypothesis that the signals in the channels are independent Gaussian processes against the hypothesis that the processes have some arbitrary correlation. This is achieved by introducing suitable non-informative priors for the covariance matrices across the channels under the two hypotheses. Subsequently, reduced likelihoods are obtained by marginalizing the joint distribution of the data and the covariance matrix in each case. The likelihood ratio is then shown to be a monotonic function of the GC detection statistic. This derivation extends to the case of time-correlated signals, allowing comparison with the generalized likelihood ratio test (GLRT) recently proposed by Ramı́rez et al.
Development and Validation of the Instrument Measuring Eating Behavior for Thai People with Metabolic Syndrome

Metabolic syndrome (MetS) or abdominal obesity was found to be a major cause of chronic diseases, such as cancer, hypertension, cardiovascular disease, and diabetes mellitus (Wijesinghe, 2018). It had been continuously increasing by 20-30% in adult population worldwide (Vera et al., 2018), especially by 6070% in adults who had abdominal obesity and diabetes mellitus (Van Namen et al., 2019). It was reported 50-75% among adults in Asian countries (Kim et al., 2019) and 9.5% in Thai people aged 45-59, 30.3% among men and 70% among women (Araya et al., 2018;Blackford et al., 2016).
CNN based accurate fault location method in distribution network

With higher power quality requirement of users, power companies pay more attention to the improvement of distribution network. To decrease outage time, accurate fault location algorithm has been widely used in transmission network, but faces with plenty of difficulties when it comes to distribution network. DC lines and distributed generations, such as wind turbine and photovoltaic generation, make distribution network increasingly complex. This paper introduces a Convolutional Neural Network (CNN) based accurate fault location method in distribution network with Phasor Measurement Unit (PMU) information. Besides, the performance of proposed method when memory or communication failures happen is discussed. The precisely-synchronized curves recorded by the PMUs are fed into CNN. The curves with pre- and post-fault data can offer abundant features which is hard to extract for traditional methods. In our experiments, the result proves that the proposed method has a good performance not only with complete PMU information but also with incomplete PMU information.
Toward Privacy-Preserving Healthcare Monitoring Based on Time-Series Activities Over Cloud

The thriving of the Internet of Things (IoT) has become the enabler of smart eHealthcare, which greatly benefits patients by providing various data-driven healthcare monitoring services. Among those promising services, the time-series activities-based healthcare monitoring service is highly regarded due to its popularity. Meanwhile, with the rapidly growing volume of healthcare data, an emerging trend is to outsource the time-series activities-based healthcare monitoring models and the corresponding services to a cloud, which, however, inevitably entails privacy concerns. Although many existing works have put forth some solutions for privacy-preserving time-series activities-based healthcare monitoring, they are not applicable to the outsourced scenario with a single-server setting. To address the challenge, in this article, we propose an efficient and privacy-preserving forward algorithm (PPFA) and further apply PPFA to construct a remote healthcare monitoring scheme over the cloud. To the best of our knowledge, our PPFA is the first privacy-preserving forward algorithm over cloud while without any accuracy loss. In addition, our remote healthcare monitoring scheme is also the first privacy-preserving hidden Markov model-based healthcare monitoring scheme in the single-server setting. Detailed security analysis shows that our PPFA and healthcare monitoring scheme are indeed privacy preserving. In addition, extensive simulations are conducted, and the results also demonstrate their efficiencies.
Acetylcholine receptors on cultured antennal cells of the moth Manduca sexta

We used the whole-cell patch-clamp technique to test whether 1 mMACh or 0.1 mM muscarine would elicit any currents in the cultured neurons. The pipet solution was (in mM): KCl 150; MgCl2 2; CaCl2 1; EGTA 11; Na+ATP 2; HEPES 5; pH 7.1, adjusted to 330 mOsm. The bath solution consisted of (in mM): NaCl 156; KCl 4; CaCl2 6; Glucose 5; HEPES 10; pH 7.1, adjusted to 380 mOsm. The pipet resistance was 6 to 10 M . The agonists were freshly dissolved in saline and applied directly to the bath solution. We could not see any clear responses to these agonists. Currents triggered by mAChR are usually small (peak current of 0.8 nA at a membrane potential of -50 mV in motoneurons), slow and can be inhibited by Ca2+, Mg2+ or other divalent cations (Trimmer 1994). When both nicotinic and muscarinic receptors are present on the same cell, responses toACh can be up to 40 times greater than to muscarine alone. In isolated neuronal somata of locusts, at a holding potential of -40 mV, the ACh current is about 12 nA, while a similar dose of muscarine elicits a current of approximately 300 pA(Benson 1992). Thus, the absence of large inward currents when the neurons are stimulated with ACh supports the idea that nicotinicAChR are not present on these cells. Manduca ELECTROPHYSIOLOGY INTRODUCTION
The fine-coarse network model for simulating crowd behavior

The paper covers topics concerning the creation of models and simulators of crowd behavior and their usage in decision support systems. Models and crowd behavior simulators were originally created to understand the phenomena occurring in a real crowd. Due to the complexity of observed processes constructed models only chosen aspects are usually described, while others are being generalized or skipped. Creating a model requires making a choice between descriptions which are constant or discret, deterministic or random, and microor macroscopic. The paper introduces a developed model of crowd movement and behavior allowing a simulation of the movement of large masses of crowd in a city agglomeration environment. In model the representation of environment uses a combination of two approaches: coarse network model and fine network model simultaneously. Using a self-made simulator the author conducted experiments showing advantages and effectiveness of implemented model.
Nanostructured Conducting Polymers for Applications in Water Treatment and Aerospace Coatings

Author(s): Farrell, Thomas Potapenko | Advisor(s): Kaner, Richard B | Abstract: Nanostructured conducting polymers have been synthesized by chemical oxidative polymerization, where the nucleation conditions have been intentionally modified to produce materials with high processability. These materials have been blended with conventional polymers and incorporated into polymer membranes via nonsolvent-induced phase separation. The membranes produced show tunable hydrophilicity, pore structure, and separation properties. Pure polyaniline membranes are super hydrophilic and have unusually high permeability.The pore structure and resultant separation properties of polyaniline ultrafiltration membranes have been modified by the use of 4-methylpiperidine (4MP) as a gel-inhibiting agent. Bovine serum albumin (BSA) rejection for polyaniline membranes is improved from 0% to over 90% when 4MP is included in membrane casting solutions. Membranes cast with 4MP are hydrophobic until an appropriate post-treatment method is applied. Treatment with camphorsulfonic acid recovers the hydrophilicity in these membranes through induction of the expanded coil conformation of polyaniline, where hydrophobic 4MP molecules can now diffuse out of the polymer matrix.Polypyrrole nanospheres have been produced by rapidly initiated chemical oxidative polymerization and used to produce nanocomposite membranes with polysulfone. The nanocomposite membranes have high BSA rejection (g80%) and have permeabilities up to 5 times greater than pure polysulfone. Fouling experiments were performed on these membranes by monitoring the flux upon introduction of BSA and subsequent washing steps. Membranes containing as little as 2% PPy nanospheres were resistant to both reversible and irreversible fouling by BSA proteins.Copolymer nanofibers of poly(aniline-co-ethylaniline) have been produced by rapidly-initiated chemical oxidative polymerization using the initiators p-phenylenediamine and N-phenyl-1,4-phenylenediamine. Copolymer nanofibers are formed in all stoichiometries as determined by electron microscopy. Resulting copolymer compositions are enriched in ethylaniline due to the higher reactivity ratio of ethylaniline in comparison to aniline. All copolymers possess lower conductivity than the parent polyaniline nanofibers. Conductivity is highest in copolymer samples where p-phenylenediamine is used as an initiator.Carbon nanotube/polyurethane composites have been produced as static dissipative coating materials by using polyaniline/dodecylbenzenesulfonate (DBSA) as a matrix and dispersant. While chloroform and DBSA were unable to disperse non-functionalized carbon nanotubes, the introduction of polyaniline produced homogeneous dispersions with carbon nanotubes. These materials were combined with solvent-based polyurethane to produce composite coatings. With as little as 20% loading of these polyaniline-based conducting materials, the composites fall within the static dissipative regime and maintain a high transparency.
The Lexicon of Profit and Commerce in Ovid's Ars Amatoria and Other Works

Abstract:This paper studies the vocabulary of advantage, exchange, and loss in Ovid's Ars Amatoria, which stands apart from the rest of the Ovidian corpus (and from the rest of Roman erotic elegy) in its special, systematic handling of these words. Philological analysis of utor and related terms shows that the praeceptor amoris of Ars, importing language of commerce and profit into his teaching, contradicts his supposed intent of creating durable romantic relationships. To establish the unique treatment of this lexicon in Ars Amatoria, I analyze the employment of these terms across the genre of Latin elegy and across the remainder of Ovid's poetic output. Through systematic usage of terms such as usus and damnum, profit and loss, Ars Amatoria strips the elegiac relationship down to its material, commercial basis: the praeceptor amoris is teaching not the art of love, but the business of getting free sex from sex-laborers. The lexicon becomes a means of destabilizing the speaker's surface-level occupation with enduring romantic liaisons—of demystifying the elegiac relation and demonstrating that it is, at heart, an exchange of money for sex. The poet highlights the commercial exchange between meretrix and amator, ultimately giving the lie to the traditional emotional/affective façade of elegiac relationships. This lexical study additionally offers new insights into a number of Ars-like moments in Ovid's poetry after Ars.
Development of a Scale to Measure Educators’ Practice in Teaching Self-Determination

The purpose of this study was to develop a scale for assessing teachers’ self-determination instruction and to test the validity and reliability of this tool. The subjects included 315 teachers recruited from elementary and junior high schools nationwide in Taiwan. The Teaching Self-Determination Scale (TSDS) developed in this study aimed at assessing the extent to which educators teach students knowledge and skills related to self-determination. The 24-item TSDS is comprised of four subscales including SelfRealization, Psychological Empowerment, Self-Regulation, and Autonomy. Data collected were analyzed using descriptive statistics, correlation analyses, t tests, and factor analyses. Findings showed that the TSDS has satisfactory psychometric properties. The internal consistency reliability coefficients (Cronbach’s α) ranged from . 76 to . 93, while the test-retest coefficients ranged from . 71 to . 87. Findings of the exploratory factor analysis showed that the four TSDS subscale factors can be reasonably extracted, which can explain 59.7% of the total item variance. The confirmatory factor analysis results further indicated a good fit between the measurement model and the sample data (GFI = .96, AGFI = .91, RMSEA = .08, NFI = .97, RFI = .93, IFI = .98, TLI = .95, CFI = .98). Suggestions are provided for future research
Employment Responses to Income Effect: Evidence from Pension Reform

For the design of the pension system, it is crucial to disentangle the employment responses related to the substitution effect and the income effect. In this paper, we provide causal evidence regarding the importance of the income effect, which is generally assumed to be small or non-existent. We exploit a pension reform in Germany that raised pension bene- fits related to children. For the identification, we exploit the discontinuity induced by the reform: only mothers with children born before 1.1. 1992 were affected by the pension reform. Children born after this cut-off date did not change pension income. We use a difference-in-differences estimator based on administrative data from the German pension insurance that includes complete individual employment histories. We find that income effects are significant and economically important. We show that the policy led to a reduction in the employment of affected females. Further, we are able to show effect heterogeneity on different dimensions: by treatment intensity, age of the mother, and pre-reform pension wealth.
RUSSIAN-LANGUAGE SPEECH RECOGNITION SYSTEM BASED ON DEEPSPEECH

The paper examines the practical issues in developing a speech-to-text system using deep neural networks. The development of a Russian-language speech recognition system based on DeepSpeech architecture is described. The Mozilla company’s open source implementation of DeepSpeech for the English language was used as a starting point. The system was trained in a containerized environment using the Docker technology. It allowed to describe the entire process of component assembly from the source code, including a number of optimization techniques for CPU and GPU. Docker also allows to easily reproduce computation optimization tests on alternative infrastructures. We examined the use of TensorFlow XLA technology that optimizes linear algebra computations in the course of neural network training. The number of nodes in the internal layers of neural network was optimized based on the word error rate (WER) obtained on a test data set, having regard to GPU memory limitations. We studied the use of probabilistic language models with various maximum lengths of word sequences and selected the model that shows the best WER. Our study resulted in a Russian-language acoustic model having been trained based on a data set comprising audio and subtitles from YouTube video clips. The language model was built based on the texts of subtitles and publicly available Russian-language corpus of Wikipedia’s popular articles. The resulting system was tested on a data set consisting of audio recordings of Russian literature available on voxforge.com—the best WER demonstrated by the system was 18%.