Miracle-dz/UniFAR-FTUs huggingface dataset:
A Changing-Reference Parasitic-Matching Sensing Circuit for 3-D Vertical RRAM

The 3-D vertical array architecture is considered to be a promising technology for emerging nonvolatile memories. But in 3-D vertical emerging nonvolatile memories, planar parasitic elements, vertical parasitic elements, and the sneak currents of the half-selected memory cells result in the delay of the read operation and read errors. This paper refers to the case of the 3-D vertical resistive switching random access memory (RRAM) technology. A read scheme, the memory core design, and the read path are proposed or analyzed. The factors that affect the read operation are concluded. A changing-reference parasitic-matching sensing circuit is, therefore, proposed. In the proposed circuit, the reference side and the read side share similar sneak currents and read paths. Simulated in a 40-nm CMOS process, the sensing time of 128-Mb 3-D vertical RRAM is 8.54 ns compared to the conventional 34.26 ns. Monte Carlo simulations show a 130.21-ns worst sensing time compared to the conventional 401.95 ns. The read errors are reduced by 100% and 95.31% under the regular and the worst RRAM resistance, respectively.
High-Efficiency Target Detection Scheme through Relay Nodes in Chemotactic-Based Molecular Communication

Nano-network models based on chemotaxis are com-monly designed for the application of target detection or drug delivery. However, due to the random diffusion of molecules, chemotactic substances can't rapidly reach long-range nanoma-chines, which affects effectiveness of the detection process. In this paper, we propose a nano-network model and a corresponding target detection scheme using relay nodes that can release attractants to expand the impact range of chemotactic substances and improve the chemotactic efﬁciency. We then establish the mathematical model for this process. Finally, simulation results show the proposed scheme for the new model is superior to general schemes.
Potential sources of uncertainties in nuclear reaction modeling

Nowadays, reliance on nuclear models to interpolate or extrapolate between experimental data points is very common, for nuclear data evaluation. It is also well known that the knowledge of nuclear reaction mechanisms is at best approximate, and that their modeling relies on many parameters which do not have a precise physical meaning outside of their specific implementations in nuclear model codes: they carry both specific physical information, and effective information that is related to the deficiencies of the model itself. Therefore, to improve the uncertainties associated with evaluated nuclear data, the models themselves must be refined so that their parameters can be rigorously derived from theory. Examples of such a process will be given for a wide sample of models like: detailed theory of compound nucleus decay through multiple nucleon or gamma emission, or refinements to the width fluctuation factor of the Hauser-Feshbach model. All these examples will illustrate the reduction in the effective components of nuclear model parameters, through the reduced dynamics of parameter adjustment needed to account for experimental data. The significant progress, recently achieved for the non-fission channels, also highlights the difficult path ahead to improve our quantitative understanding of fission in a similar way: by relying on microscopic theory.
Particle Swarm Optimization for Polarization ‐ Independent and Low Loss Grating Couplers

Silicon‐on‐insulator waveguide grating couplers suffers from some key problems like high polarization dependency due to its high index contrast. In order to overcome this problem refractive index engineering has been demonstrated as a suitable solution [1, 2]. However, the design complexity is highly increased interfering with the search of the optimal design. This work presents a particle swarm optimization (PSO) strategy applied to a polarization‐independent grating coupler based on subwavelength structures for the near‐infrared band. Furthermore, the achieved design is suitable to be fabricated with a single‐fully‐etch step and was constrained to have a minimum feature size ≥ 100 nm.
A Frequency Tunable Quadrature Coupler With Wide Tuning Range of Center Frequency and Wide Operating Bandwidth

To solve the problems of existing frequency tunable quadrature couplers with limited tuning range and narrow bandwidth, a frequency tunable 3-dB quadrature coupler with wide tuning range and wide bandwidth is proposed and is based on the vertically installed planar (VIP) structure. Varactor diodes are loaded between vertical patches to achieve frequency tuning. Due to the strong coupling characteristics of the VIP structure, broad bandwidth is maintained during the frequency tuning process. For validation, a frequency tunable coupler is designed, fabricated, and measured. The center frequency of the designed coupler can be varied from 1.3 GHz to 2.7 GHz covering a frequency range of 70%. For each operating mode, more than 40% bandwidth defined by 1 dB amplitude imbalance between output ports, 10 dB return loss and isolation, and phase variation of 5°, can be achieved.
Phase fluctuation phenomena in superconductors

We revisit the scaling properties of the resistivity and the current-voltage characteristics at and below the Berezinskii-Kosterlitz-Thouless transition, both in zero and nonzero magnetic field. The scaling properties are derived by integrating the renormalization group flow equations up to a scale where they can be reliably matched to simple analytic expressions. The vortex fugacity turns out to be dangerously irrelevant for these quantities below $T_c$, thereby altering the scaling behavior. We derive the possible crossover effects as the current, magnetic field or system size is varied, and find a strong multiplicative logarithmic correction near $T_c$, all which is necessary to account for when interpreting experiments and simulation data. Our analysis clarifies a longstanding discrepancy between the finite size dependence found in many simulations and the current-voltage characteristics of experiments. We further show that the logarithmic correction can be avoided by approaching the transition in a magnetic field, thereby simplifying the scaling analysis. We confirm our results by large scale numerical simulations, and calculate the dynamic critical exponent $z$, for relaxational Langevin dynamics and for resistively and capacitively shunted Josephson junction dynamics.
An improved embedding matching model for Chinese word segmentation

To date, various deep neural network based models have been extensively applied in Chinese word segmentation (CWS) task, however, some of these models either consume so much time to train, or perform weakly without additional dictionary or training corpus. In this paper, we proposed an improved embedding matching model for CWS, which achieved 0.4% and 0.5% improvement of F1-Measure respectively on PKU and MSR dataset compared to the original model. Also, our proposed model achieved 0.77% improvement of F1-Measure and 2.59% improvement of weighted F1-Measure on the NLPCC dataset. The improved model outperforms most of previous models on the F1 measure of segmentation performance, and consumes less time to train and test. After improvement, the model can be a better choice in practical use for its exceedingly fast convergence and excellent performance.
High Performance Vertical GaN-on-GaN p-n Power Diodes With Hydrogen-Plasma-Based Edge Termination

This letter reports the first implementation of a hydrogen-plasma-based edge termination technique (HPET) in vertical GaN p-n power diodes grown on bulk GaN substrates using metalorganic chemical vapor deposition. The device with a 9-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula>-thick drift layer exhibited a high breakdown voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{\textsf {bd}}$ </tex-math></inline-formula>) of 1.57 kV, a low ON-resistance (<inline-formula> <tex-math notation="LaTeX">${R}_{\mathrm{ ON}}$ </tex-math></inline-formula>) of 0.45 <inline-formula> <tex-math notation="LaTeX">$\text{m}\Omega ~ \cdot $ </tex-math></inline-formula> cm<sup>2</sup> (or 0.70 <inline-formula> <tex-math notation="LaTeX">$\text{m}\Omega ~\cdot $ </tex-math></inline-formula> cm<sup>2</sup> with current spreading considered) and a high Baliga’s figure-of-merit (<inline-formula> <tex-math notation="LaTeX">${V}^{\textsf {2}}_{\textsf {bd}}/{R}_{\mathrm{ ON}}$ </tex-math></inline-formula>) of 5.5 GW/cm<sup>2</sup> (or 3.6 GW/cm<sup>2</sup>) without passivation or field plate, which are close to the theoretical limit of GaN. This technique enabled a significant reduction in leakage current (~10<sup>6</sup> times at −300 V) and a huge enhancement in <inline-formula> <tex-math notation="LaTeX">${V}_{\textsf {bd}}$ </tex-math></inline-formula> (from ~300 V to 1.57 kV). Furthermore, the device showed good forward characteristics with a turn-ON voltage of 3.5 V, an ON-current of ~2 kA/cm<sup>2</sup> (or 1.3 kA/cm<sup>2</sup>), an ON/OFF ratio of ~10<sup>9</sup>, and an ideality factor of 1.4. This work shows the HPET can serve as an effective, low cost, and easy-to-implement edge termination technique for high voltage and high power GaN p-n power diodes.
The impact of the program structure at Hannover Medical School on academic success in medical studies

Aim: The classical course structure for medicine in Germany is separated by three sections of the medical state examination. This structure is generally regarded as sensible and unchangeable. Because the special program structure at Hannover Medical School (MHH) has one integrated, rather than two separate study blocks, it is possible to examine the influence of structural modifications on the study success of different admission groups. Methods: The data was obtained from students admitted to the MHH between 2006 and 2008 in different admission quotas. Study success was defined as the successful completion of the entire program, but completion of the first section of the state examination was also analysed. Results: More students from the best “Abitur” (school leaving examinations) quota successfully completed their studies than those accepted via the selection process of the universities. The latter were more successful than students from the waiting list quota. However the successful graduates of this last group completed their studies more often within the prescribed period of study, although they needed more time for completing all parts of the first section of the state examination. Conclusion: The data shows that an integrated course structure can offer, in particular, students from the waiting list quota, the opportunity to compensate for delays in the first years of study. However, they do not provide any evidence which applicants are best suited to meet the social and professional requirements of trained doctors. Due to the complex structure of such longitudinal studies, our results allow more than one plausible interpretation.
A hybrid deep learning model for consumer credit scoring

Consumer credit scoring is an essential part of credit risk management in the fast-growing consumer finance industry and various data mining techniques have been proposed and used on it. Recently, deep learning techniques have gained significant popularity and shown excellent performance in many fields such as image recognition, computer vision and so on. In this paper, we try to take the advantage of deep learning and introduce it into consumer credit scoring. We propose a hybrid model that combines the well-known convolutional neural network with the feature selection algorithm Relief. Experiments are carried on a real-world dataset from a Chinese consumer finance company, and the results show that the proposed model gets superior performance in comparison with other benchmark models such as logistic regression and random forest.
A Novel 0.8-V 79-nW CMOS-Only Voltage Reference With −55-dB PSRR @ 100 Hz

A novel 79-nW CMOS-only subthreshold voltage reference with 328-mV output voltage, which works down to 0.8-V supply voltage, is presented in this brief. In order to reduce the area and power consumption, the proposed circuit avoids the use of resistors and bipolar transistors. A current subtracting technique is proposed to realize the temperature compensation of reference current. Instead of the op-amp in traditional circuits, some cascode current mirrors are used to enhance the power supply rejection ratio (PSRR). A novel structure consisting of a 1.8-V MOS transistor and a 3.3-V MOS transistor is utilized to accomplish the voltage temperature compensation and to further reduce the power consumption of V<sub>REF</sub> generator. The proposed voltage reference was fabricated in 0.18-<inline-formula> <tex-math notation="LaTeX">${\mu }\text{m}$ </tex-math></inline-formula> CMOS process. The measured results show that the temperature coefficient of the output voltage is 33.8 ppm/°C in the range from 10 °C to 100 °C, the PSRR is −55 dB @ 100 Hz at 1.2-V power supply, and the line regulation is 0.21%/V in a supply voltage range of 0.8 to 3.4 V. What is more, the power dissipation is 79 nW, and the chip area is 0.01 mm<sup>2</sup>. It is suitable for low-power applications.
$L_p$- and $S_{p,q}^rB$-discrepancy of (order $2$) digital nets

Dick proved that all order $2$ digital nets satisfy optimal upper bounds of the $L_2$-discrepancy. We give an alternative proof for this fact using Haar bases. Furthermore, we prove that all digital nets satisfy optimal upper bounds of the $S_{p,q}^r B$-discrepancy for a certain parameter range and enlarge that range for order $2$ digitals nets. $L_p$-, $S_{p,q}^r F$- and $S_p^r H$-discrepancy is considered as well.
Investigation of the relationship between TSH and serum lipids in patints with subclinical hypothyroidism and euthroid subjects Subklinik hipotiroidi hastalarında ve ötroid bireylerde TSH düzeyi ile serum lipid düzeyleri arasındaki ilişkinin araştırılması

Halef Okan Dogan, Fatma Meriç Yılmaz, Canan Topcuoğlu Dilek Berker, Yüksel Koca Cumhuriyet University, Faculty of Medicine, Department of Biochemistry, 58140, Sıvas,Turkey Department of Clinical Biochemistry, Yildirim Beyazit University, Faculty of Medicine, 06030, Ankara, Turkey Ankara Numune Training and Research Hospital, Laboratory of Clinical Biochemistry, 06030, Ankara, Turkey Ankara Numune Training and Research Hospital, Department of Endocrinology, 06030, Ankara, Turkey Ankara Public Health Management, Public Health Laboratory, 06790, Ankara, Turkey Corresponding author: Halef Okan Doğan, Cumhuriyet University, Faculty of Medicine, Department of Biochemistry, 58140, Sıvas,Turkey E-mail: halefokan@gmail.com Received/Accepted: March 30, 2016 / May 17, 2016 Conflict of interest: There is not a conflict of interest.
SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates

The lack of reliable methods for identifying descriptors---the sets of parameters capturing the underlying mechanisms of a material's property---is one of the key factors hindering efficient materials development. Here, we propose a systematic approach for discovering descriptors for materials' properties, within the framework of compressed-sensing-based dimensionality reduction. The sure independence screening and sparsifying operator (SISSO) tackles immense and correlated features spaces, and converges to the optimal solution from a combination of features relevant to the materials' property of interest. In addition, SISSO gives stable results also with small training sets. The methodology is benchmarked with the quantitative prediction of the ground-state enthalpies of octet binary materials (using ab initio data) and applied to the showcase example of predicting the metal/insulator classification of binaries (with experimental data). Accurate, predictive models are found in both cases. For the metal-insulator classification model, the predictive capability is tested beyond the training data: It rediscovers the available pressure-induced insulator-to-metal transitions and it allows for the prediction of yet unknown transition candidates, ripe for experimental validation. As a step forward with respect to previous model-identification methods, SISSO can become an effective tool for automatic materials development.
Stability Analysis and Delay Control for Switched Positive Linear Systems

In this paper, the problems of stability analysis and delay control for switched positive linear systems (SPLSs) are studied by using average dwell time (ADT) switching. By developing a new class of multiple piecewise-continuous linear copositive Lyapunov function (MPLCLF) approach, an improved stability condition for SPLSs is obtained, which can obtain a tighter bound on the ADT guaranteeing the stability. The obtained stability condition covers the existing results on stability analysis for SPLSs, since the multiple linear copositive Lyapunov function used in the literature can be viewed as a special case of our proposed MPLCLF, and our result can obtain a tighter bound of the ADT. Then, a more efficient controller design method over the existing ones is also established for SPLSs with switching delay to guarantee the underlying system to be exponentially stable. To illustrate the advantages of our proposed results, two numerical examples are finally given.
Passive radio system for real-time drone detection and DoA estimation

The principal objective behind the development of passive radio system is to detect and localize mini remotely piloted aircraft systems (RPAS) and their operators in three dimensions (3D). This paper describes the system architecture, detection procedure and intermediate test results. Goodness-of-Fit (GoF) based spectrum sensing is used to detect the frequency of the transmitted signal of mini-RPAS and its controller. The direction of arrival (DoA) is estimated with the MUSIC algorithm. The implementation of GoF-based wideband spectrum sensing and one dimensional DoA estimation including spatial smoothing and automatic source number detection are detailed in this paper. Several test results are presented to validate the real time operation of the passive radio system.
Beyond Big Data of Human Behaviors: Modeling Human Behaviors and Deep Emotions

Humans possess a variety of long term or short term behaviors such as gesture, posture, and movement and so on. These readable behaviors usually convey significant emotional information, which can facilitate human-machine interactions in intelligent cognitive systems. However, there is a lack of studies on modeling such complex relationship between human behavior and emotion in a time series context. This paper attempts to pioneer such an exploration. First, huge amounts of human behaviors are suggested to be captured by various sensors. Then behaviors and emotions are modeled by deep structure of bidirectional LSTM, which can represent interactions and correlations. To avoid training difficulties, bidirectional LSTM are only located in the bottom layer, and the other layers are uni-bidirectional, while the adjacent layers use residual connections. This deep bidirectional LSTM has the advantage that it can be scaled up to larger varieties of human behaviors captured by multiple sensors. The experimental results show that our proposed deep structure for modeling human behaviors and emotions is able to achieve a high degree of accuracy than shallow representation or models.
Image inpainting algorithm based on self-adaptive structural group sparse representation

Introducing Self-adaptive into groups sparse representation for image inpainting, in order to make the reconstructed image better structure continuity and better texture effect. Different from the traditional method which uses a fixed number of image blocks to construct group. The algorithm proposed in this paper self-adaptive selects different number of similar image blocks according to the different structure structural information to construct self-adaptive group. Using such groups as the repair unit for dictionary learning and sparse representation. Finally, introducing Split Bregman Iteration for updating parameters and getting better restored image. Experiments show that the PSNR value of the image after inpaiting with the algorithm proposed in this paper is higher. Meanwhile, the algorithm proposed in this paper is faster than the algorithm of Group-based Sparse Representation.
Radar-based maritime collision avoidance using dynamic window

Collision avoidance systems are a key ingredient in developing autonomous surface vehicles (ASVs). Such systems require real-time information about the environment, which can be obtained from transponder-based systems or exteroceptive sensors located on the ASV. In this paper, we present a closed-loop collision avoidance (COLAV) system using a maritime radar for detecting target ships, implemented on a 26 foot high-speed ASV. The system was validated in full-scale experiments in Trondheimsfjorden, Norway, in May 2017. The probabilistic data association filter (PDAF) is used for tracking target vessels. The output from the PDAF is processed through a least-squares retrodiction procedure in order to provide the COLAV system with sufficiently accurate course estimates. A tracking interface provides estimates of target states to the COLAV system, which is based on the dynamic window (DW) algorithm. DW is a reactive COLAV algorithm originally designed for ground vehicles, and we therefore make a number of modifications to adapt it for use with high-speed ASVs. The closed-loop experiments demonstrated successful COLAV with this system, but also disclosed several challenges arising from both the DW algorithm and the tracking system, motivating for further work.
Rewind : A Transcription Method and Website

Simple digital audio formats such as mp3s and various others lack the symbolic information that musicians and other organizations needed to retrieve the important details of a given piece. However, there have been recent advances for converting from a digital audio format to a symbolic format a problem called Music Transcription. Rewind is an Automatic Music Transcription (AMT) system that boasts a new deep learning method for generating transcriptions at the frame level and web application. The web app was built as a front end interface to visualize and hear generated transcriptions. Rewind’s new deep learning method utilizes an encoderdecoder network where the decoder consists of a gated recurrent unit (GRU) or two GRUs in parallel and a linear layer. The encoder layer is a single layer autoencoder that captures the temporal dependencies of a song and consists of a GRU followed by a linear layer. It was found that Rewind’s deep learning method is comparable to other existing deep learning methods using existing AMT datasets and a custom dataset. In other words, Rewind is a web app that utilizes a deep learning method that allows users to transcribe, listen to, and see their music.
Forecasting model of a movie's profitability

This study intends to estimate the profit of a movie through the construction of a predictive model that uses several Data Mining techniques, namely neural networks, regression and decision trees. The model will allow obtaining the prediction of box office revenue. Three different dependent variable approaches were used (interval, categorical and binary) aiming to study the difference and predictive influence that each one has on the results. Two metrics were used to determine the most accurate predictions: the misclassification error for the categorical models and the average squared error for the continuous one. In this study, the best predictive results were obtained through the use of multi-layer perceptron. Regarding the representative distinction between the dependent variable, the multiclass model presents a much higher error rate comparing to the remaining ones, which is explained with the increase of the number of classes to predict.