Miracle-dz/UniFAR-FTUs huggingface dataset:
Telecommunication Needs Universal Service

： There are various universal service policies in many fields at present, in which telecommunications universal service is not the only one. Other fields which need the universal service include postal service, electricity and natural gas, etc. Regardless of International Telecommunications Union (ITU), other international organizations such as OECD, the developed countries in the world and developing countries all implement universal service policy in the telecommunication trade. The difference between them is only in universal service contents. But with the development at full speed of the telecommunication trade in the last few years, the continual renovation of technology, especially a lot of countries have carried on a series of telecommunication reforms, the introduction of the competition, privatization and deregulation make universal service become the focus disputed. This paper proves the necessity of telecommunication universal service in terms of theory.
Improving the Generalization of Deep Learning Classification Models in Medical Imaging Using Transfer Learning and Generative Adversarial Networks

Data sets for medical images are generally imbalanced and limited in sample size because of high data collection costs, time-consuming annotations, and patient privacy concerns. The training of deep neural network classification models on these data sets to improve the generalization ability does not produce the desired results for classifying the medical condition accurately and often overfit the data on the majority of class samples. To address the issue, we propose a framework for improving the classification performance metrics of deep neural network classification models using transfer learning: pre-trained models, such as Xception, InceptionResNet, DenseNet along with the Generative Adversarial Network (GAN) – based data augmentation. Then, we trained the network by combining traditional data augmentation techniques, such as randomly flipping the image left to right and GAN-based data augmentation, and then fine-tuned the hyper-parameters of the transfer learning models, such as the learning rate, batch size, and the number of epochs. With these configurations, the Xception model outperformed all other pre-trained models achieving a test accuracy of 98.7%, the precision of 99%, recall of 99.3%, f1-score of 99.1%, receiver operating characteristic (ROC) - area under the curve (AUC) of 98.2%.
Handwritten Text Extraction Using HOG Feature and SVM

: Handwritten Text Extraction (HTE) is an emerging technology in image processing. HTE is the wonders of empowering a machine to automatically realize the characters written in a user dialect. Optical character extraction has turned out to be a standout amongst the best candidates of innovation in the field of pattern recognition and artificial intelligence. In this paper we propose a methodology for extracting and recognizing characters or text using Histogram of Gradient (HOG) feature extraction and Support Vector Machines (SVM) as classifier. To evaluate the accuracy of our proposed method we use the ground truth data.
Some Applications to Telephone Traffic Theory Based on Functional Limit Laws for Cumulative Processes

Cumulative processes appear frequently in traffic theory. If regeneration points in time can be chosen so, that the intervals between those points are i.i. d. r.v. 's. and the behaviour of the increments in the process over these intervals also are i.i. d. , then we can call the process cumulative. Let e.g. the beginning of each congestion period be a regeneration point, then the total time congestion in (O,t] can be studied as a cumulative process, wet). Some results can be achieved from functional limit theorems for wet). To apply those theorems some constants have to be known. In simple cases they can be calculated, but in more complicated situations simulation is more useful. To illustrate the special technique and the advantages of having the results in a functional form, we have chosen some simple processes, where the norming constants are known. Functionals, stopping times and especially the random change of time method will be discussed. ' Applications to scanning processes are also given. THEORETICAL BACKGROUND The theoretical background of this article will be some functional limit theorems proved in [5J. These limit theorems concern cumulative processes in the sense of Smith,[S] and [9]. A process wet), t>O is said to be cumulative, if i 0, S. = E ~. are regeneration points and 1 j=l J , Cl. {~i' w(Si) w(Si_l)} are i.i. d. random vectors, P(~.<O) = 0 Jt CZ . i(t) = f Idw(t) I is finite w.p.l for O<t<~ o C3 iCS i ) wCS i _1 ) are i.i. d. r.v. 's. Put w(O) o for simplicity. Define K = E(w(Si) w(Si_l)) = Ew(Sl) Kl = Ei(Sl)' KZ = E(w 2 (Sl))
Assessing Password Protection Effectiveness Using Markov Processes *

Passwords are an integral part of modern digital life, but their effectiveness has been repeatedly challenged. This paper explores the role of password authentication in modern information systems, the changes it has experienced over time, as well as new alternative approaches to authentication and their interactions with classical password-based systems. Technical and organizational recommendations are formulated based on security research and modern trends in information technology, and an application of Markov processes to assessing password quality is presented, as a quantitative measure of password system security
Pathophysiological Roles of Abnormal Axon Initial Segments in Neurodevelopmental Disorders

The 20–60 μm axon initial segment (AIS) is proximally located at the interface between the axon and cell body. AIS has characteristic molecular and structural properties regulated by the crucial protein, ankyrin-G. The AIS contains a high density of Na+ channels relative to the cell body, which allows low thresholds for the initiation of action potential (AP). Molecular and physiological studies have shown that the AIS is also a key domain for the control of neuronal excitability by homeostatic mechanisms. The AIS has high plasticity in normal developmental processes and pathological activities, such as injury, neurodegeneration, and neurodevelopmental disorders (NDDs). In the first half of this review, we provide an overview of the molecular, structural, and ion-channel characteristics of AIS, AIS regulation through axo-axonic synapses, and axo−glial interactions. In the second half, to understand the relationship between NDDs and AIS, we discuss the activity-dependent plasticity of AIS, the human mutation of AIS regulatory genes, and the pathophysiological role of an abnormal AIS in NDD model animals and patients. We propose that the AIS may provide a potentially valuable structural biomarker in response to abnormal network activity in vivo as well as a new treatment concept at the neural circuit level.
A REVIEW ON NANO CATALYST

The researchers are particularly interested in the utilisation of waste water treatment using nano-catalysts, particularly inorganic materials, such as semiconductors and metal oxides. Diverse kinds of nano-catalysts, such as photo-catalysts, elektrocatalysts, and Fenton-based catalysts are utilised to treat wastewater. The current study will attempts to investigate and analysze the nano catalysts.
Advances in oxygen-17 NMR for biological structure determination

The determination of the structures of biological systems, such as, fibril-forming peptides and proteins, membrane proteins, and viruses, by solid-state nuclear magnetic resonance (NMR) spectroscopy has materialized as an important field due to the high-resolution details that the technique provides. While solid-state NMR studies have focused on the three prevalent spin I= 1/2 nuclei present in biomolecules, oxygen has largely been ignored as a probe of the structure of such systems. Described in this thesis are the results of studies to advance the use of solid-state 170 NMR spectroscopy for structure determination of biological molecules by utilizing the sensitivity of the electric field gradient (EFG) and chemical shift tensors of 17. We demonstrated a distinct chemical shift range for bound water in crystalline amino acids and dipeptides and identified discordance between experimental and calculated EFG parameters. This difference was further studied via variable temperature 17 NMR experiments on barium chlorate monohydrate that demonstrated the effect of the librational motions of the bound water on the NMR measurable 170 quadrupolar coupling constant. A well resolved splitting in the 170 magic-angle spinning NMR spectrum was shown at the'rigid lattice limit of the bound water that was determined to be caused by the interaction between the 'H-1H dipole coupling and the 'H170 dipole couplings. Further study of bound water environments is presented exhibiting the ability to resolve multiple unique bound water environments in a single system by highresolution 170 NMR spectroscopy. We demonstrate the ability to utilize dynamic nuclear polarization (DNP) NMR to polarize multiple nuclei via an endogenous radical dopant that does not disrupt the native crystal structure. Efficient "7 labeling of a dipeptide is explored to enable the study of the dipeptide via 70 NMR. Utilizing oneand two-dimensional double resonance correlation spectroscopy, the interatomic correlations between oxygen and carbon, nitrogen, and hydrogen atoms were explored, exhibiting the ability of 170 NMR to determine structural characteristics of biomolecular solids. A study on the use of DNP NMR to study biosilica entrapped proteins is also presented as part of this thesis. Thesis Supervisor: Robert G. Griffin Title: Professor of Chemistry Director of the Francis Bitter Magnet Laboratory
Photothermoelastic Investigation of Semiconductor Material Due to Distributed Loads

A dynamic mathematical model of photothermoelastic (semiconductor) medium is developed to analyze the deformation due to inclined loads. The governing equations for photothermoelastic with dual phase lag model are framed for two dimensional case and are further simplified by using potential function. Appropriate transforms w.r. t time (Laplace) and w.r. t space variables (Fourier) are employed on the resulting equations which convert the system of equations into differential equation. The problem is examined by deploying suitable mechanical boundary conditions. Specific types of distributed loads as uniformly distributed force and Linearly distributed force are taken to examine the utility of the model. The analytic expressions like displacements, stresses, temperature distribution and carrier density are obtained in the new domain (transformed). To recover the quantities in the physical domain, numerical inversion technique is employed. Numerical computed results with different angle of inclination vs distance are analyzed with and without dual phase lag theories of thermoelasticity in the form of visual representations. It is seen that physical field quantities are sensitive towards photothermoelastic and phase lag parameters.
Voice Reconstruction from Silent Speech with a Sequence-to-Sequence Model

—Silent Speech Decoding (SSD) based on Surface electromyography (sEMG) has become a prevalent task in recent years. Though revolutions have been proposed to decode sEMG to audio successfully, some problems still remain. In this paper, we propose an optimized sequence-to-sequence (Seq2Seq) approach to synthesize voice from subvocal sEMG. Both subvocal and vocal sEMG are collected and preprocessed to provide data information. Then, we extract durations from the alignment between subvocal and vocal signals to regulate the subvocal sEMG following audio length. Besides, we use phoneme classiﬁ-cation and vocal sEMG reconstruction modules to improve the model performance. Finally, experiments on a Mandarin speaker dataset, which consists of 6.49 hours of data, demonstrate that the proposed model improves the mapping accuracy and voice quality of reconstructed voice.
Implementation of an advanced dressing protocol for global qubit control in silicon

Quantum computing based on solid state spins allows for densely packed arrays of quantum bits. However, the operation of large-scale quantum processors requires a shift in paradigm toward global control solutions. Here, we report a proof-of-principle demonstration of the SMART (sinusoidally modulated, always rotating, and tailored) qubit protocol. We resonantly drive a two-level system and add a tailored modulation to the dressing field to increase robustness to frequency detuning noise and microwave amplitude fluctuations. We measure a coherence time of 2 ms, corresponding to two orders of magnitude improvement compared to a bare spin, and an average Clifford gate fidelity exceeding 99%, despite the relatively long qubit gate times. We stress that the potential of this work lies in the scalability of the protocol and the relaxation of the engineering constraints for a large-scale quantum processor. This work shows that future scalable spin qubit arrays could be operated using global microwave control and local gate addressability, while increasing robustness to relevant experimental inhomogeneities.
A Dynamic 3D Spontaneous Micro-expression Database: Establishment and Evaluation

—Micro-expressions are spontaneous, unconscious facial movements that show people's true inner emotions and have great potential in related fields of psychological testing. Since the face is a 3D deformation object, the occurrence of an expression can arouse spatial deformation of the face, but limited by the available databases are 2D videos, lacking the description of 3D spatial information of micro-expressions. In this paper, we proposed a new database that contains 373 micro-expression samples, each consisting of 2D video sequence and corresponding 3D point cloud sequence. These samples were classified using objective method based on facial action coding system, as well as non-objective emotion classification method combining video contents and participants’ self-reports. We extracted 2D and 3D features using the local binary patterns on three orthogonal planes (LBP-TOP) and curvature algorithms, respectively, and evaluated the classification accuracies of these two features and their fusion results with leave-one-subject-out (LOSO) and 10-fold cross-validation. Further, we used various advanced neural network algorithms for database evaluation, the results show that classification accuracies are improved by fusing 3D features than using only 2D features. The database offers original and cropped micro-expression samples, which will facilitate the exploration and research on 3D spatio-temporal features of micro-expression.
Regulation Enforcement

This paper compares the effectiveness of two mechanisms of regulation enforcement: (1) the frequency of inspections and (2) penalties for violations. Threat effects of increased penalties and inspection rates, rather than corrective effects upon receiving an inspection or penalty, are the focus of analysis. Mining industry data from 2004–2009 are used to analyze the responses of mines to separate increases in inspections and citation penalties regarding regulations of safety standards. Mines did not improve safety in response to increased penalties at the ex-ante inspecting rates; however, mines significantly reduced accidents under increased inspections when implemented at those higher penalty rates. The identification strategy results in a local average treatment effect that implies increasing inspection rates from current levels would likely increase social welfare. Results are shown to be robust to bandwidth changes and model specification. The interpretation of the estimated local effect in the context of selection is analyzed. Robustness checks regarding selection exploit staffing changes and restrict to similar samples of treated and non-treated mines, justifying that results are representative.
On Neural Associative Memory Structures: Storage and Retrieval of Sequences in a Chain of Tournaments

Abstract Associative memories enjoy many interesting properties in terms of error correction capabilities, robustness to noise, storage capacity, and retrieval performance, and their usage spans over a large set of applications. In this letter, we investigate and extend tournament-based neural networks, originally proposed by Jiang, Gripon, Berrou, and Rabbat (2016), a novel sequence storage associative memory architecture with high memory efficiency and accurate sequence retrieval. We propose a more general method for learning the sequences, which we call feedback tournament-based neural networks. The retrieval process is also extended to both directions: forward and backward—in other words, any large-enough segment of a sequence can produce the whole sequence. Furthermore, two retrieval algorithms, cache-winner and explore-winner, are introduced to increase the retrieval performance. Through simulation results, we shed light on the strengths and weaknesses of each algorithm.
Learning to Penalize Other Learning Agents

A key challenge in AI is the development of algorithms that are capable of cooperative behavior in interactions involving multiple independent machines or individuals. Of particular interest are social dilemmas, which are situations that raise tension between an individual’s best choice and the desirable outcome in terms of the group. Although such scenarios have been studied increasingly within the AI community recently, there are still many open questions on which aspects drive co-operative behavior in a particular situation. Based on the insights from behavioral experiments that have suggested positive effects of penalty mechanisms towards cooperation, in this work we adopt the notion of penalties by enabling independent and adaptive agents to penalize others. To that end, we extend agents’ action spaces with penalty actions and deﬁne a negative real-valued punishment value. We utilize reinforcement learning to simulate a process of repeated interaction between independent agents, learning by means of trial-and-error. Our evaluation considers different two player social dilemmas, and the N-player Prisoner’s Dilemma with up to 128 independent agents, where we demonstrate that the proposed mechanism combined with decentralized learning signiﬁcantly increases cooperation within all experiments.
Clonal Origin Investigation in Pre-Leukemia Condition

Whatever the mechanisms governing chromosomal changes in utero, it is evident that for most cases of childhood leukemia, the crucial etiologic bottleneck is the postnatal ‘promotional’ events or exposures that precipitate the secondary genetic changes, including TEL deletion. Uncovering the nature of these exposures is the objective of major case/control epidemiologic studies in some countries. Although no consensus view has yet emerged, there is now a body of evidence implicating infections in childhood Acute Lymphoblastic Leukemia (ALL). In other words, the infection can be leading at immune response deregulation in a pre-leukemic child minimally. After secondary mutation(s), the children go to changing into ALL which leads to clonal expansion ultimately.
Increased Risk of Recurrence of Non-Muscle Invasive Bladder Cancer Associated With Psychological Distress: A Prospective Cohort Study

Objective The primary aim was to evaluate the influence of depressive and anxiety symptoms on the 1-year recurrence rate of non-muscle invasive bladder cancer (NMIBC) patients. The secondary aim was to examine the risk factors leading to psychological distress. Methods A total of 104 NMIBC patients were enrolled for interviews, and the Hospital Anxiety and Depression Scale (HADS) questionnaire survey was administered 1 month after their operation. Their cystoscopy results were followed up. The risk factors affecting their 1-year recurrence rate were evaluated through univariate analysis, Cox regression and Kaplan-Meier analysis. The risk factors causing depressive and anxiety symptoms were evaluated through univariate analysis and logistic regression. Results In addition to American Urological Association risk stratification, depressive symptoms were another independent risk factor for recurrence in NMIBC patients (HR: 2.493, 95% CI: 1.048–5.930, p=0.039), and the increase in the recurrence rate was highly significant in intermediate-risk patients (HR: 8.496, 95% CI: 2.178–33.138, p=0.019). Anxiety symptoms were not an independent risk factor for recurrence (HR: 1.655, 95% CI: 0.714–3.837, p=0.240). We also observed that the burden of medical expenses of NMIBC on the family was an independent risk factor for depressive symptoms (p=0.029) and anxiety symptoms (p=0.048); chronic pain was an independent risk factor for anxiety symptoms (OR: 3.447, 95% CI: 1.182–10.052, p=0.023). Conclusion Depression symptoms are an independent risk factor for recurrence in NMIBC patients. Moreover, the burden of medical expenses on the family is an independent risk factor for depressive and anxiety symptoms in NMIBC patients. Additionally, chronic pain is a risk factor for anxiety symptoms in NMIBC patients. This study provided a theoretical foundation for clinical oncologists to pay more attention to the mental health of NMIBC patients.
Utilization of LSSVM algorithm for estimating synthetic natural gas density

ABSTRACT In the gas engineering the accurate calculation for pipeline and gas reservoirs requires great accuracy in estimation of gas properties. The gas density is one of major properties which are dependent to pressure, temperature and composition of gas. In this work, the Least squares support vector machine (LSSVM) algorithm was utilized as novel predictive tool to predict natural gas density as function of temperature, pressure and molecular weight of gas. A total number of 1240 experimental densities were gathered from the literature for training and validation of LSSVM algorithm. The statistical indexes, Root mean square error (RMSE), coefficient of determination (R2) and average absolute relative deviation (AARD) were determined for total dataset as 0.033466, 1 and 0.0025686 respectively. The graphical comparisons and calculated indexes showed that LSSVM can be considered as a powerful and accurate tool for prediction of gas density.