nils-herrmann/clean_scirepeval huggingface dataset:
All Controllers for the General Control Problem : LMI Existence Conditions and State Space Formulas *

Abs t rad -Th i s paper presents all controllers for the general ~'® control problem (with no assumptions on the plant matrices). Necessary and sufficient conditions for the existence of an ~® controller of any order are given in terms of three Linear Matrix Inequalities (LMIs). Our existence conditions are equivalent to Scherer's results, but with a more elementary derivation. Furthermore, we provide the set of all ~(= controllers explicitly parametrized in the state space using the positive definite solutions to the LMIs. Even under standard assumptions (full rank, etc.), our controller parametrization has an advantage over the Q-parametrization. The freedom Q (a real-rational stable transfer matrix with the ~® norm bounded above by a specified number) is replaced by a constant matrix L of fixed dimension with a norm bound, and the solutions (X, Y) to the LMIs. The inequality formulation converts the existence conditions to a convex feasibility problem, and also the free matrix L and the pair (X, Y) define a finite dimensional design space, as opposed to the infinite dimensional space associated with the Q-parametrization.
A dual-feed circularly-polarized traveling-wave array antenna

A traveling-wave circularly-polarized microstrip array antenna is presented in this paper. It uses a circularly polarized dual-feed radiating element. The element is a rectangular patch with two chamfered corners. It is fed by microstrip lines, making it possible for the radiating element and feed lines to be realized and integrated in a single layer. A four-element array is designed, built and tested. Measured performance of the antenna is presented, where a good agreement between the simulated and measured results is obtained and demonstrated.
A Consistent Regularization Approach for Structured Prediction

We propose and analyze a regularization approach for structured prediction problems. We characterize a large class of loss functions that allows to naturally embed structured outputs in a linear space. We exploit this fact to design learning algorithms using a surrogate loss approach and regularization techniques. We prove universal consistency and finite sample bounds characterizing the generalization properties of the proposed method. Experimental results are provided to demonstrate the practical usefulness of the proposed approach.
Hardness of parameter estimation in graphical models

We consider the problem of learning the canonical parameters specifying an undirected graphical model (Markov random field) from the mean parameters. For graphical models representing a minimal exponential family, the canonical parameters are uniquely determined by the mean parameters, so the problem is feasible in principle. The goal of this paper is to investigate the computational feasibility of this statistical task. Our main result shows that parameter estimation is in general intractable: no algorithm can learn the canonical parameters of a generic pair-wise binary graphical model from the mean parameters in time bounded by a polynomial in the number of variables (unless RP = NP). Indeed, such a result has been believed to be true (see ) but no proof was known. Our proof gives a polynomial time reduction from approximating the partition function of the hard-core model, known to be hard, to learning approximate parameters. Our reduction entails showing that the marginal polytope boundary has an inherent repulsive property, which validates an optimization procedure over the polytope that does not use any knowledge of its structure (as required by the ellipsoid method and others).
Interactively shaping agents via human reinforcement: the TAMER framework

As computational learning agents move into domains that incur real costs (e.g., autonomous driving or financial investment), it will be necessary to learn good policies without numerous high-cost learning trials. One promising approach to reducing sample complexity of learning a task is knowledge transfer from humans to agents. Ideally, methods of transfer should be accessible to anyone with task knowledge, regardless of that person's expertise in programming and AI. This paper focuses on allowing a human trainer to interactively shape an agent's policy via reinforcement signals. Specifically, the paper introduces "Training an Agent Manually via Evaluative Reinforcement," or TAMER, a framework that enables such shaping. Differing from previous approaches to interactive shaping, a TAMER agent models the human's reinforcement and exploits its model by choosing actions expected to be most highly reinforced. Results from two domains demonstrate that lay users can train TAMER agents without defining an environmental reward function (as in an MDP) and indicate that human training within the TAMER framework can reduce sample complexity over autonomous learning algorithms.
Emotional attention for erotic stimuli: Cognitive and brain mechanisms.

It has long been posited that among emotional stimuli, only negative threatening information modulates early shifts of attention. However, in the last few decades there has been an increase in research showing that attention is also involuntarily oriented toward positive rewarding stimuli such as babies, food, and erotic information. Because reproduction-related stimuli have some of the largest effects among positive stimuli on emotional attention, the present work reviews recent literature and proposes that the cognitive and cerebral mechanisms underlying the involuntarily attentional orientation toward threat-related information are also sensitive to erotic information. More specifically, the recent research suggests that both types of information involuntarily orient attention due to their concern relevance and that the amygdala plays an important role in detecting concern-relevant stimuli, thereby enhancing perceptual processing and influencing emotional attentional processes.
A Slope-Based Multipath Estimation technique for mitigating short-delay multipath in GNSS receivers

The everlasting public interest on location and positioning services has originated a demand for a high performance Global Navigation Satellite System (GNSS), such as the Global Positioning System or the future European satellite navigation system, Galileo. The performance of GNSS is subject to several errors, such as ionosphere delay, troposphere delay, receiver noise and multipath. Among all these errors, multipath is the main limiting factor in precision-oriented GNSS applications. In order to mitigate the multipath influence on navigation receivers, the multipath problem has been approached from several directions, including the development of novel signal processing techniques. Many of these techniques rely on modifying the tracking loop discriminator in order to make it resistant to multipath. These techniques have proved very efficient against multipath having a medium or large delay with respect to the Line-Of-Sight (LOS) signal. In general, the multipath errors are largely reduced for multipath delays greater than around 0.1 chips (which is about 29.3 meters for Galileo E1 Open Service (OS) signal). Theoretically, this constitutes a remarkable improvement as compared to simpler techniques such as narrow Early-Minus-Late (nEML) tracking loop. However, in practice, most of the multipath signals enter the receiver with short-delay with respect to LOS signal, making most of these mitigation techniques partially ineffective. In this paper, we propose a new multipath estimation technique, namely the Slope-Based Multipath Estimation (SBME), which is capable of mitigating the short-delay multipath (i.e., multipath delays less than 0.35 chips) quite well compared with other state-of-the-art mitigation techniques, such as the nEML and the High Resolution Correlators (HRC). The proposed SBME first derives a multipath estimation equation by utilizing the correlation shape of the ideal normalized correlation function of a Binary Phase Shift Keying (BPSK)- or Multiplexed Binary Offset Carrier (MBOC)-modulated signal, which is then used to compensate for the multipath bias of a nEML tracking loop. It is worth to mention here that the SBME requires an additional correlator at the late side of the correlation function, and it is used in-conjunction with a nEML tracking loop. The multipath performance of the above-mentioned mitigation techniques is presented for Galileo E1 OS and GPS L1 C/A signals from theoretical as well as simulation perspective.
Three level buck converter with control and soft startup

The three level buck converter can offer high efficiency and high power density in VR and POL applications. The gains are made possible by adding a flying capacitor that reduces the MOSFET voltage stress by half allowing for the use of low voltage devices, doubles the effective switching frequency, and decreases the inductor size by reducing the volt-second across the inductor. To achieve high efficiency and power density the flying capacitor must be balanced at half of the input voltage and the circuit must be started up without the MOSFETs seeing the full input voltage for protection purposes. This paper provides a new novel control method to balance the flying capacitor with the use of current control and offers a simple startup solution to protect the MOSFETs during start up. Experimental verification shows the efficiency gains and inductance reduction.
Indoor Motion Classification Using Passive RF Sensing Incorporating Deep Learning

This paper proposes a method of recognizing and classifying the basic activities such as forward and backward motions by applying a deep learning framework on passive radio frequency (RF) signals. The echoes from the moving body possess unique pattern which can be used to recognize and classify the activity. A passive RF sensing test- bed is set up with two channels where the first one is the reference channel providing the un- altered echoes of the transmitter signals and the other one is the surveillance channel providing the echoes of the transmitter signals reflecting from the moving body in the area of interest. The echoes of the transmitter signals are eliminated from the surveillance signals by performing adaptive filtering. The resultant time series signal is classified into different motions as predicted by proposed novel method of convolutional neural network (CNN). Extensive amount of training data has been collected to train the model, which serves as a reference benchmark for the later studies in this field.
NONCONVEX OPTIMIZATION APPROACH TO ROBUST STABILIZATION BY STATIC OUTPUT FEEDBACK AND LOW-ORDER CONTROLLERS

Stabilization by static output feedback (SOF) is a long-standing open problem in control: given an n by n matrix A and rectangular matrices B and C, find a p by q matrix K such that A + BKC is stable. Low-order controller design is a practically important problem that can be cast in the same framework, with (p+k)(q+k) design parameters instead of pq, where k is the order of the controller, and k << n. Robust stabilization further demands stability in the presence of perturbation and satisfactory transient as well as asymptotic system response. We formulate two related nonsmooth, nonconvex optimization problems over K, respectively with the following objectives: minimization of the -pseudospectral abscissa of A+BKC, for a fixed ≥ 0, and maximization of the complex stability radius of A + BKC. Finding global optimizers of these functions is hard, so we use a recently developed gradient sampling method that approximates local optimizers. For modest-sized systems, local optimization can be carried out from a large number of starting points with no difficulty. The best local optimizers may then be investigated as candidate solutions to the static output feedback or low-order controller design problem. We show results for two problems published in the control literature. The first is a turbo-generator example that allows us to show how different choices of the optimization objective lead to stabilization with qualitatively different properties, conveniently visualized by pseudospectral plots. The second is a well known model of a Boeing 767 aircraft at a flutter condition. For this problem, we are not aware of any SOF stabilizing K published in the literature. Our method was not only able to find an SOF stabilizing K, but also to locally optimize the complex stability radius of A + BKC. We also found locally optimizing order–1 and order–2 controllers for this problem. All optimizers are visualized using pseudospectral plots.
Primipaternity and duration of exposure to sperm antigens as risk factors for pre-eclampsia.

To establish whether primipaternity and duration of unprotected sexual cohabitation is associated with an increased risk of pre-eclampsia.



At a tertiary referral center, the study had a case and control group of 60 multigravid women each, as well as a case and control group of 50 primigravid women each. Information was compiled by means of a confidential questionnaire.



After multiple logistic regression analysis using age, smoking, hypertension in previous pregnancies, change of paternity and duration of unprotected sexual cohabitation as predictors, the regression coefficients for change of paternity and sexual cohabitation of longer than 6 months in multigravid women were -0.4 (P = 0.15) and -1.4 (P = 0.03), respectively.



Multigravid women with a period of unprotected sexual cohabitation of longer than 6 months had a decreased risk of pre-eclampsia. Primipaternity was not a significant risk factor for pre-eclampsia.
Don't sweat the small stuff: formal verification of C code without the pain

We present an approach for automatically generating provably correct abstractions from C source code that are useful for practical implementation verification. The abstractions are easier for a human verification engineer to reason about than the implementation and increase the productivity of interactive code proof. We guarantee soundness by automatically generating proofs that the abstractions are correct.
 In particular, we show two key abstractions that are critical for verifying systems-level C code: automatically turning potentially overflowing machine-word arithmetic into ideal integers, and transforming low-level C pointer reasoning into separate abstract heaps. Previous work carrying out such transformations has either done so using unverified translations, or required significant proof engineering effort.
 We implement these abstractions in an existing proof-producing specification transformation framework named AutoCorres, developed in Isabelle/HOL, and demonstrate its effectiveness in a number of case studies. We show scalability on multiple OS microkernels, and we show how our changes to AutoCorres improve productivity for total correctness by porting an existing high-level verification of the Schorr-Waite algorithm to a low-level C implementation with minimal effort.
Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification

We propose a new approach, called cooperative neural networks (CoNN), which uses a set of cooperatively trained neural networks to capture latent representations that exploit prior given independence structure. The model is more flexible than traditional graphical models based on exponential family distributions, but incorporates more domain specific prior structure than traditional deep networks or variational autoencoders. The framework is very general and can be used to exploit the independence structure of any graphical model. We illustrate the technique by showing that we can transfer the independence structure of the popular Latent Dirichlet Allocation (LDA) model to a cooperative neural network, CoNNsLDA. Empirical evaluation of CoNN-sLDA on supervised text classification tasks demonstrates that the theoretical advantages of prior independence structure can be realized in practice we demonstrate a 23% reduction in error on the challenging MultiSent data set compared to state-of-the-art.
Non-rigid Reconstruction with a Single Moving RGB-D Camera

We present a novel non-rigid reconstruction method using a moving RGB-D camera. Current approaches use only non-rigid part of the scene and completely ignore the rigid background. Non-rigid parts often lack sufficient geometric and photometric information for tracking large frame-to-frame motion. Our approach uses camera pose estimated from the rigid background for foreground tracking. This enables robust foreground tracking in situations where large frame-to-frame motion occurs. Moreover, we are proposing a multi-scale deformation graph which improves non-rigid tracking without compromising the quality of the reconstruction. We are also contributing a synthetic dataset which is made publically available for evaluating non-rigid reconstruction methods. The dataset provides frame-by-frame ground truth geometry of the scene, the camera trajectory, and masks for background foreground. Experimental results show that our approach is more robust in handling larger frame-to-frame motions and provides better reconstruction compared to state-of-the-art approaches.
Uncertainty-Aware Deep Learning for Image

Sectra is a high-tech company that develops products in the areas of medical IT and secure communication. The company has customers in over 60 countries and more than 600 employees, with the headquarters located in Linköping, Sweden. On the medical side they have a strong interest in automated diagnostic systems for radiology and pathology. Pathology is the medical specialty concerned with diagnostics based on microscopy images of tissue and cell smears. Pathology is a crucial component in a vast number of diagnostic processes, in particular those concerning cancer. However, diagnosis based on pathological images, which are often extremely high-resolution and need to be analyzed at di erent scales, is typically very time consuming. This has lead to an increased interest in developing automated methods for classi cation and decision support in pathology. However, for such systems to be widely adopted a reliable quality assurance procedure is of utmost importance. Speci cally, the algorithm needs to be able to warn the user of uncertain classi cations and anomalies in the data. This is made particularly challenging by the large variations in the characteristics of pathological imagery. In addition to providing quality assurance, uncertainty quanti cation also enables a dynamic work ow: when the algorithm nds itself in uncharted territory a human expert can be alerted and manually adapt the algorithm parameters to the new scenario.
Effects of alcohol reduction on blood pressure: a meta-analysis of randomized controlled trials.

Alcohol drinking has been associated with increased blood pressure in epidemiological studies. We conducted a meta-analysis of randomized controlled trials to assess the effects of alcohol reduction on blood pressure. We included 15 randomized control trials (total of 2234 participants) published before June 1999 in which alcohol reduction was the only intervention difference between active and control treatment groups. Using a standard protocol, information on sample size, participant characteristics, study design, intervention methods, duration, and treatment results was abstracted independently by 3 investigators. By means of a fixed-effects model, findings from individual trials were pooled after results for each trial were weighted by the inverse of its variance. Overall, alcohol reduction was associated with a significant reduction in mean (95% confidence interval) systolic and diastolic blood pressures of -3.31 mm Hg (-2.52 to -4.10 mm Hg) and -2.04 mm Hg (-1.49 to -2.58 mm Hg), respectively. A dose-response relationship was observed between mean percentage of alcohol reduction and mean blood pressure reduction. Effects of intervention were enhanced in those with higher baseline blood pressure. Our study suggests that alcohol reduction should be recommended as an important component of lifestyle modification for the prevention and treatment of hypertension among heavy drinkers.
EARNING TO SMILE ( S )

This paper shows how one can directly apply natural language processing (NLP) methods to classification problems in cheminformatics. Connection between these seemingly separate fields is shown by considering standard textual representation of compound, SMILES. The problem of activity prediction against a target protein is considered, which is a crucial part of computer aided drug design process. Conducted experiments show that this way one can not only outrank state of the art results of hand crafted representations but also gets direct structural insights into the way decisions are made.
A CONCEPTUAL FRAMEWORK FOR APPLYING LEAN MANAGEMENT METHODOLOGY IN HIGHER EDUCATION INSTITUTIONS

Over the last two decades, the organizational environment of Higher Education Institutions (HEI) in many countries, has fundamentally changed. Student numbers have continuously increased since the 1980s and transformed Higher Education (HE) from an exclusive offering for a small elite to a mass product. Consequently, universities had to increasingly deal with operations management issues such as capacity planning and efficiency. In order to enable this expansion and as means to facilitate competition, the funding structure of HEI's has changed. Greater reliance on tuition fees and industryfunded research exposed universities to the forces of the market. All in all, growth, commercialization and competition have transformed HEI's from publicly funded cosy elite institutions to large professional service operations with more demanding customers. Consequently, they increasingly look at private sector management practices to deal with the rising performance pressure. During the last two decades, Lean Management has received the reputation as a reliable method for achieving performance improvements by delivering higher quality at lower costs. From its origins in manufacturing, Lean has spread first to the service sector and is now successfully adopted by an increasing number of public sector organizations. Paradoxically, the enthusiasm for Lean in HE has so far been limited. A conceptual framework for applying Lean Management methodology in HEI's is presented in this paper.
Design and Analysis of Turbo Codes on Rayleigh Fading Channels

The performance and design of turbo codes using coherent BPSK signaling on the Rayleigh fading channel is considered. In low signal-to-noise regions, performance analysis uses simulations of typical turbo coding systems. For higher signalto-noise regions beyond simulation capabilities, an average upper bound is used in which the average is over all possible interleaving schemes. Fully interleaved and exponentially correlated Rayleigh channels are explored. Furthermore, the design issues relevant to turbo codes are examined for the correlated fading channel. Turbo interleaver design criteria are developed and architectural modifications are proposed for improved performance.
MECHANISM FOR CELL SEPARATION BASED ON SIZE AND DEFORMABILITY USING MICROFLUIDIC RATCHETS

We present a mechanism for separating cells based on size and deformability using microfluidic ratchets created using micrometer-scale funnel constrictions. The force required to deform individual cells through such constrictions is directionally asymmetric, enabling rectified transport from oscillatory flow of the bulk fluid. Combining ratcheting with simple filtration enables cell separation based on size and deformability. Based on this concept, we developed a microfluidic device using a 2D matrix of funnel constrictions. We demonstrate highly selective separation of two cell types while retaining viability, study the effect of oscillation flow pressure, and confirm the irreversible nature of the ratcheting process.