Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel tight coupling and high directivity λ /4 partially-narrowed and slotted coupled line and its application

A new λ/4 microstrip coupled line (MSCL) partially-narrowed and slotted the other part to obtain both tight coupling beyond machining limitations and high directivity is proposed. And a multi-element broadband coupler using the proposed MSCL as the tightest coupling element is presented. A multi-element 10 dB coupler is designed, and the performances, which are −10.3±0.9dB coupling and greater than 20dB isolation over 162% of the bandwidth.
Glioma Grade Prediction Using Wavelet Scattering-Based Radiomics

Glioma grading before surgery is very critical for the prognosis prediction and treatment plan making. We present a novel wavelet scattering-based radiomic method to predict noninvasively and accurately the glioma grades. The method consists of wavelet scattering feature extraction, dimensionality reduction, and glioma grade prediction. The dimensionality reduction was achieved using partial least squares (PLS) regression and the glioma grade prediction using support vector machine (SVM), logistic regression (LR) and random forest (RF). The prediction obtained on multimodal magnetic resonance images of 285 patients with well-labeled intratumoral and peritumoral regions showed that the area under the receiver operating characteristic curve (AUC) of glioma grade prediction was increased up to 0.99 when considering both intratumoral and peritumoral features in multimodal images, which represents an increase of about 13% compared to traditional radiomics. In addition, the features extracted from peritumoral regions further increase the accuracy of glioma grading.
Trajectory Tracking Control for Flexible-Joint Robot Based on Extended Kalman Filter and PD Control

The robot arm with flexible joint has good environmental adaptability and human robot interaction ability. However, the controller for such robot mostly relies on data acquisition of multiple sensors, which is greatly disturbed by external factors, resulting in a decrease in control precision. Aiming at the control problem of the robot arm with flexible joint under the condition of incomplete state feedback, this paper proposes a control method based on closed-loop PD (Proportional-Derivative) controller and EKF (Extended Kalman Filter) state observer. Firstly, the state equation of the control system is established according to the non-linear dynamic model of the robot system. Then, a state prediction observer based on EKF is designed. The state of the motor is used to estimate the output state, and this method reduces the number of sensors and external interference. The Lyapunov method is used to analyze the stability of the system. Finally, the proposed control algorithm is applied to the trajectory control of the flexible robot according to the stability conditions, and compared with the PD control algorithm based on sensor data acquisition under the same experimental conditions, and the PD controller based on sensor data acquisition under the same test conditions. The experimental data of comparison experiments show that the proposed control algorithm is effective and has excellent trajectory tracking performance.
High resolution nearly-ML estimation of sinusoids in noise using a fast frequency domain approach

Estimating the frequencies, amplitudes and phases of sinusoids in noise is a problem which arises in many-applications. The aim of the methods in this paper is to achieve computational efficiency and near-ML performance (i.e. low bias, variance and threshold SNR), in problems such as vibration or audio analysis where the number of tones may be large (e.g. > 20). An approach has recently been published for resolved tones [4]. This paper extends that frequency domain approach to the high-resolution problem.
microRNAs expression in endometriosis and their relation to angiogenic factors.

BACKGROUND Endometriosis is a common, multifactorial disease in which angiogenesis may be involved in the growth of endometrium outside the uterus. microRNAs (miRNAs) are 21-22 nucleotide non-coding RNAs that regulate gene expression and play fundamental roles in biological processes. The objective of this study was to analyze several miRNAs related to angiogenesis and the angiogenic factors, vascular endothelial growth factor-A (VEGF-A) and thrombospondin-1 (TSP-1), in endometriotic lesions (ovarian endometrioma, peritoneal lesion and rectovaginal nodule) and eutopic endometrium from women with endometriosis. METHODS TaqMan real-time PCR was used to assess the expression of the miRNAs (miR-15b, -16, -17-5p, -20a, -21, -125a, -221 and -222), while VEGF-A and TSP-1 mRNA were assessed by real-time PCR, with SYBR Green I and VEGF-A and TSP-1 protein levels were quantified by ELISA. Included in the study were 58 women with endometriosis and 38 control women. RESULTS In paired samples, ovarian endometrioma showed significantly lower VEGF-A mRNA (P = 0.02) and protein (P = 0.002) expression than eutopic endometrium and higher expression of miR-125a (P = 0.003) and miR-222 (P <0.001). However, ovarian endometrioma had significantly higher expression of the angiogenic inhibitor TSP-1 and lower expression of miR-17-5p than eutopic endometrium (P < 0.001). Moreover, a significant inverse correlations between miR-222 and VEGF-A protein levels (-0.267, P = 0.018) and between miR-17-5p and TSP-1 protein levels (-0.260, P=0.022) were observed. Peritoneal lesions showed a significant increase in VEGF-A in comparison with ovarian endometrioma (P < 0.01). CONCLUSIONS Expression levels of miRNAs related to angiogenesis were different in eutopic endometrium from that observed in ovarian endometrioma. This could influence the expression of angiogenic factors and play a role in the pathogenesis of endometriosis.
A vanishing result for higher smooth duals

In this paper we prove a general vanishing result for Kohlhaase's higher smooth duality functors $S^i$. If $G$ is any unramified connected reductive $p$-adic group, $K$ is a hyperspecial subgroup, and $V$ is a Serre weight, we show that $S^i(\ind_K^G V)=0$ for $i>\dim(G/B)$ where $B$ is a Borel subgroup. (Here and throughout the paper $\dim$ refers to the dimension over $\Q_p$.) This is due to Kohlhaase for $\GL_2(\Q_p)$ in which case it has applications to the calculation of $S^i$ for supersingular representations. Our proof avoids explicit matrix computations by making use of Lazard theory, and we deduce our result from an analogous statement for graded algebras via a spectral sequence argument. The graded case essentially follows from Koszul duality between symmetric and exterior algebras.
Zoology of Solid \& Fluid Holography

We provide a comprehensive classification of isotropic solid and fluid holographic models with broken translational invariance. We describe in detail the collective modes in both the transverse and longitudinal sectors. First, we discuss holographic fluid models, i.e. systems invariant under internal volume preserving diffeomorphisms. We consider the explicit (EXB) and the spontaneous (SSB) breaking of translations and we emphasize the differences with respect to their solid counterpart. Then, we present a study of the longitudinal collective modes in simple holographic solid and fluid models exhibiting the interplay between SSB and EXB. We confirm the presence of light pseudo-phonons obeying the Gell-Mann-Oakes-Renner relation. Our results suggest that the crystal diffusion mode does not acquire a simple damping term because of the novel relaxation scale proportional to the EXB. The dynamics is more complex and it involves the interplay of three modes: the crystal diffusion and two more arising from the splitting of the original sound mode. In this sense, the novel relaxation scale, which comes from the explicit breaking of the global internal shift symmetry of the Stückelberg fields, is different from the one induced by elastic defects, and depending solely on the SSB scale. https://members.ift. uam-csic.es/matteo.baggioli ar X iv :1 90 5. 09 48 8v 1 [ he pth ] 2 3 M ay 2 01 9
Studies of the retention and properties of S35 labeled antigen in livers of immunized rabbits.

The rate of loss of S35 from livers of rabbits, which had received nine injections of tagged antigen, diminished with time and became fairly constant at four weeks after the last injection. The percentage of antigen retained at six weeks in the liver, as based upon total amount injected, was less for rabbits which received repeated injections than for those which received only a single injection. Antigen in the soluble fraction of liver was present in two forms as shown by electrophoresis, salt-precipitation and serological activity. Three days after the last of nine injections, both precipitating antigen and non-precipitating antigen (by specific inhibition) were present. Five to six weeks later only non-precipitating antigen was present.
Escaping a Neighborhood Along a Prescribed Sequence in Lie Groups and Banach Algebras

Abstract It is shown that Jamison sequences, introduced in 2007 by Badea and Grivaux, arise naturally in the study of topological groups with no small subgroups, of Banach or normed algebra elements whose powers are close to identity along subsequences, and in characterizations of (self-adjoint) positive operators by the accretiveness of some of their powers. The common core of these results is a description of those sequences for which non-identity elements in Lie groups or normed algebras escape an arbitrary small neighborhood of the identity in a number of steps belonging to the given sequence. Several spectral characterizations of Jamison sequences are given, and other related results are proved.
Active shunt current shaping scheme for multiple paralleled DC–DC converters

A new shunt current shaping scheme for multiple paralleled dc–dc converters is proposed in this paper. The current command for the shunt current shaper is indirectly obtained by forcing the source current to follow the demanded sinusoidal signal. The amplitude of the demanded sinusoidal source current that is in‐phase with the source voltage can be determined from the sensed load currents of the post‐stage dc–dc converters. Neither high‐order filters nor time‐consuming computations are required. The shunt current shaper supplies all the harmonics and the out‐of‐phase fundamental of the distorted input current and the power source only supplies the in‐phase fundamental component. Experimental results on a prototype system verify the feasibility of the presented scheme. The implemented shunt current shaper demonstrates an efficiency of 92% and a nearly unity power factor at the utility side. Copyright © 2011 John Wiley & Sons, Ltd.
The Role of Asymmetries in Thermal Nonequilibrium

Thermal nonequilibrium (TNE) is a fascinating situation that occurs in coronal magnetic flux tubes (loops) for which no solution to the steady-state fluid equations exists. The plasma is constantly evolving even though the heating that produces the hot temperatures does not. This is a promising explanation for isolated phenomena such as prominences, coronal rain, and long-period pulsating loops, but it may also have much broader relevance. As known for some time, TNE requires that the heating be both (quasi-)steady and concentrated at low coronal altitudes. Recent studies indicate that asymmetries are also important, with large enough asymmetries in the heating and/or cross-sectional area resulting in steady flow rather than TNE. Using reasonable approximations, we have derived two formulae for quantifying the conditions necessary for TNE. As a rough rule of thumb, the ratio of apex to footpoint heating rates must be less than about 0.1, and asymmetries must be less than about a factor of 3. The precise values are case-dependent. We have tested our formulae with 1D hydrodynamic loop simulations and find a very acceptable agreement. These results are important for developing physical insight about TNE and assessing how widespread it may be on the Sun.
Statistical Assertions for Validating Patterns and Finding Bugs in Quantum Programs

In support of the growing interest in quantum computing experimentation, programmers need new tools to write quantum algorithms as program code. Compared to debugging classical programs, debugging quantum programs is difficult because programmers have limited ability to probe the internal states of quantum programs; those states are difficult to interpret even when observations exist; and programmers do not yet have guidelines for what to check for when building quantum programs. In this work, we present quantum program assertions based on statistical tests on classical observations. These allow programmers to decide if a quantum program state matches its expected value in one of classical, superposition, or entangled types of states. We extend an existing quantum programming language with the ability to specify quantum assertions, which our tool then checks in a quantum program simulator. We use these assertions to debug three benchmark quantum programs in factoring, search, and chemistry. We share what types of bugs are possible, and lay out a strategy for using quantum programming patterns to place assertions and prevent bugs.
Mathematical-Physics of propagating modes in planar waveguides

In this paper we present a detailed physical analysis of the formation of the propagation transverse modes in planar dielectric waveguides using a mathematical-physics approach. We demonstrate physically that, at the wavelength scale, the pure stationary mode inside planar waveguide is described by the cosine function. Meanwhile, the sine function yields a quasi-stationary periodic mode.
The Journey is the Reward: Unsupervised Learning of Influential Trajectories

Unsupervised exploration and representation learning become increasingly important when learning in diverse and sparse environments. The information-theoretic principle of empowerment formalizes an unsupervised exploration objective through an agent trying to maximize its influence on the future states of its environment. Previous approaches carry certain limitations in that they either do not employ closed-loop feedback or do not have an internal state. As a consequence, a privileged final state is taken as an influence measure, rather than the full trajectory. We provide a model-free method which takes into account the whole trajectory while still offering the benefits of option-based approaches. We successfully apply our approach to settings with large action spaces, where discovery of meaningful action sequences is particularly difficult.
The tradeoff between the utility and risk of location data and implications for public good

High-resolution individual geolocation data passively collected from mobile phones is increasingly sold in private markets and shared with researchers. This data poses significant security, privacy, and ethical risks: it's been shown that users can be re-identified in such datasets, and its collection rarely involves their full consent or knowledge. This data is valuable to private firms (e.g. targeted marketing) but also presents clear value as a public good. Recent public interest research has demonstrated that high-resolution location data can more accurately measure segregation in cities and provide inexpensive transit modeling. But as data is aggregated to mitigate its re-identifiability risk, its value as a good diminishes. How do we rectify the clear security and safety risks of this data, its high market value, and its potential as a resource for public good? We extend the recently proposed concept of a tradeoff curve that illustrates the relationship between dataset utility and privacy. We then hypothesize how this tradeoff differs between private market use and its potential use for public good. We further provide real-world examples of how high resolution location data, aggregated to varying degrees of privacy protection, can be used in the public sphere and how it is currently used by private firms.
CASI: A Convolutional Neural Network Approach for Shell Identification

We utilize techniques from deep learning to identify signatures of stellar feedback in simulated molecular clouds. Specifically, we implement a deep neural network with an architecture similar to U-Net and apply it to the problem of identifying wind-driven shells and bubbles using data from magnetohydrodynamic simulations of turbulent molecular clouds with embedded stellar sources. The network is applied to two tasks, dense regression and segmentation, on two varieties of data, simulated density and synthetic 12CO observations. Our Convolutional Approach for Shell Identification (casi) is able to obtain a true-positive rate greater than 90%, while maintaining a false-positive rate of 1%, on two segmentation tasks and also performs well on related regression tasks. The source code for casi is available on GitLab.
Self-organization of action hierarchy and compositionality by reinforcement learning with recurrent networks.

Recurrent neural networks (RNNs) for reinforcement learning (RL) have shown distinct advantages, e.g., solving memory-dependent tasks and meta-learning. However, little effort has been spent on improving RNN architectures and on understanding the underlying neural mechanisms for performance gain. In this paper, we propose a novel, multiple-timescale, stochastic RNN for RL. Empirical results show that the network can autonomously learn to abstract sub-goals and can self-develop an action hierarchy using internal dynamics in a challenging continuous control task. Furthermore, we show that the self-developed compositionality of the network enhances faster re-learning when adapting to a new task that is a re-composition of previously learned sub-goals, than when starting from scratch. We also found that improved performance can be achieved when neural activities are subject to stochastic rather than deterministic dynamics.
Reachable Space Characterization of Markov Decision Processes with Time Variability

We propose a solution to a time-varying variant of Markov Decision Processes which can be used to address decision-theoretic planning problems for autonomous systems operating in unstructured outdoor environments. We explore the time variability property of the planning stochasticity and investigate the state reachability, based on which we then develop an efficient iterative method that offers a good trade-off between solution optimality and time complexity. The reachability space is constructed by analyzing the means and variances of states' reaching time in the future. We validate our algorithm through extensive simulations using ocean data, and the results show that our method achieves a great performance in terms of both solution quality and computing time.
Towards Generation and Evaluation of Comprehensive Mapping Robot Datasets

This paper presents a fully hardware synchronized mapping robot with support for a hardware synchronized external tracking system, for super-precise timing and localization. We also employ a professional, static 3D scanner for ground truth map collection. Three datasets are generated to evaluate the performance of mapping algorithms within a room and between rooms. Based on these datasets we generate maps and trajectory data, which is then fed into evaluation algorithms. The mapping and evaluation procedures are made in a very easily reproducible manner for maximum comparability. In the end we can draw a couple of conclusions about the tested SLAM algorithms.
Detecting Malicious PowerShell Scripts Using Contextual Embeddings

PowerShell is a command line shell, that is widely used in organizations for configuration management and task automation. Unfortunately, PowerShell is also increasingly used by cybercriminals for launching cyber attacks against organizations, mainly because it is pre-installed on Windows machines and it exposes strong functionality that may be leveraged by attackers. This makes the problem of detecting malicious PowerShell scripts both urgent and challenging. We address this important problem by presenting several novel deep learning based detectors of malicious PowerShell scripts. Our best model obtains a true positive rate of nearly 90% while maintaining a low false positive rate of less than 0.1%, indicating that it can be of practical value. Our models employ pre-trained contextual embeddings of words from the PowerShell "language". A contextual word embedding is able to project semantically similar words to proximate vectors in the embedding space. A known problem in the cybersecurity domain is that labeled data is relatively scarce in comparison with unlabeled data, making it difficult to devise effective supervised detection of malicious activity of many types. This is also the case with PowerShell scripts. Our work shows that this problem can be largely mitigated by learning a pre-trained contextual embedding based on unlabeled data. We trained our models' embedding layer using a scripts dataset that was enriched by a large corpus of unlabeled PowerShell scripts collected from public repositories. As established by our performance analysis, the use of unlabeled data for the embedding significantly improved the performance of our detectors. We estimate that the usage of pre-trained contextual embeddings based on unlabeled data for improved classification accuracy will find additional applications in the cybersecurity domain.
Element Abundances: A New Diagnostic for the Solar Wind

We examine the different element abundances exhibited by the closed loop solar corona and the slow speed solar wind. Both are subject to the first ionization potential (FIP) effect, the enhancement in coronal abundance of elements with FIP below 10 eV (e.g., Mg, Si, Fe) with respect to high-FIP elements (e.g., O, Ne, Ar), but with subtle differences. Intermediate elements, S, P, and C, with FIP just above 10 eV, behave as high-FIP elements in closed loops, but are fractionated more like low-FIP elements in the solar wind. On the basis of FIP fractionation by the ponderomotive force in the chromosphere, we discuss fractionation scenarios where this difference might originate. Fractionation low in the chromosphere where hydrogen is neutral enhances the S, P, and C abundances. This arises with nonresonant waves, which are ubiquitous in open field regions, and is also stronger with torsional Alfvén waves, as opposed to shear (i.e., planar) waves. We discuss the bearing these findings have on models of interchange reconnection as the source of the slow speed solar wind. The outflowing solar wind must ultimately be a mixture of the plasma in the originally open and closed fields, and the proportions and degree of mixing should depend on details of the reconnection process. We also describe novel diagnostics in ultraviolet and extreme ultraviolet spectroscopy now available with these new insights, with the prospect of investigating slow speed solar wind origins and the contribution of interchange reconnection by remote sensing.