nils-herrmann/clean_scirepeval huggingface dataset:
Model predictive control: Theory and practice - A survey

Al~tmd--We refer to Model Predictive Control (MPC) as that family of controllers in which there is a direct use of an explicit and separately identifiable model. Control design methods based on the MPC concept have found wide acceptance in industrial applications and have been studied by academia. The reason for such popularity is the ability of MPC designs to yield high performance control systems capable of operating without expert intervention for long periods of time. In this paper the issues of importance that any control system should address are stated. MPC techniques are then reviewed in the light of these issues in order to point out their advantages in design and implementation. A number of design techniques emanating from MPC, namely Dynamic Matrix Control, Model Algorithmic Control, Inferential Control and Internal Model Control, are put in perspective with respect to each other and the relation to more traditional methods like Linear Quadratic Control is examined. The flexible constraint handling capabilities of MPC are shown to be a significant advantage in the context of the overall operating objectives of the process industries and the 1-, 2-, and oo-norm formulations of the performance objective are discussed. The application of MPC to non-linear systems is examined and it is shown that its main attractions carry over. Finally, it is explained that though MPC is not inherently more or less robust than classical feedback, it can be adjusted more easily for robustness.
Overcoming Language Priors in Visual Question Answering with Adversarial Regularization

Modern Visual Question Answering (VQA) models have been shown to rely heavily on superficial correlations between question and answer words learned during training – e.g. overwhelmingly reporting the type of room as kitchen or the sport being played as tennis, irrespective of the image. Most alarmingly, this shortcoming is often not well reflected during evaluation because the same strong priors exist in test distributions; however, a VQA system that fails to ground questions in image content would likely perform poorly in real-world settings. In this work, we present a novel regularization scheme for VQA that reduces this effect. We introduce a question-only model that takes as input the question encoding from the VQA model and must leverage language biases in order to succeed. We then pose training as an adversarial game between the VQA model and this question-only adversary – discouraging the VQA model from capturing language biases in its question encoding. Further, we leverage this question-only model to estimate the increase in model confidence after considering the image, which we maximize explicitly to encourage visual grounding. Our approach is a model agnostic training procedure and simple to implement. We show empirically that it can improve performance significantly on a bias-sensitive split of the VQA dataset for multiple base models – achieving state-of-the-art on this task. Further, on standard VQA tasks, our approach shows significantly less drop in accuracy compared to existing bias-reducing VQA models.
Removal of fine powders from film surface. I. Effect of electrostatic force on the removal efficiency.

A novel fine particle removal system composed of a corona-discharge neutralizer, a pulse-jet air unit and an image processing system has been developed. First of all, adhesion force between particle and film was directly measured and effect of electrostatic force on the adhesion force was calculated experimentally and theoretically. The electrostatic force was found to be significant, leading to the suggestion that the countermeasure for the electrostatic force was required to effectively remove fine particles. This system was then applied to the removal of fine particles from surface of a gelatin film used for conventional capsule material. The number of particles removed by the system was calculated by an image processing system and number base removal efficiency was computed with and without the elimination of electrostatic charge by the neutralizer. It was found that the difference between the removal efficiency of particles with elimination of electrostatic charge and that of without the elimination showed linear relationship with the electrostatic adhesion force. The data confirmed the necessity of electrostatic charge elimination for the effective removal of fine particles.
Frontiers of high-throughput metabolomics.

Large scale metabolomics studies are increasingly used to investigate genetically different individuals and time-dependent responses to environmental stimuli. New mass spectrometric approaches with at least an order of magnitude more rapid analysis of small molecules within the cell's metabolome are now paving the way towards true high-throughput metabolomics, opening new opportunities in systems biology, functional genomics, drug discovery, and personalized medicine. Here we discuss the impact and advantages of the progress made in profiling large cohorts and dynamic systems with high temporal resolution and automated sampling. In both areas, high-throughput metabolomics is gaining traction because it can generate hypotheses on molecular mechanisms and metabolic regulation. We conclude with the current status of the less mature single cell analyses where high-throughput analytics will be indispensable to resolve metabolic heterogeneity in populations and compartmentalization of metabolites.
Wideband Dual-Polarized Cross-Shaped Vivaldi Antenna

This communication presents a wideband, dual-polarized Vivaldi antenna or tapered slot antenna with over a decade (10.7:1) of bandwidth. The dual-polarized antenna structure is achieved by inserting two orthogonal Vivaldi antennas in a cross-shaped form without a galvanic contact. The measured -10 dB impedance bandwidth (S11) is approximately from 0.7 up to 7.30 GHz, corresponding to a 166% relative frequency bandwidth. The isolation (S21) between the antenna ports is better than 30 dB, and the measured maximum gain is 3.8-11.2 dB at the aforementioned frequency bandwidth. Orthogonal polarizations have the same maximum gain within the 0.7-3.6 GHz band, and a slight variation up from 3.6 GHz. The cross-polarization discrimination (XPD) is better than 19 dB across the measured 0.7-6.0 GHz frequency bandwidth, and better than 25 dB up to 4.5 GHz. The measured results are compared with the numerical ones in terms of S-parameters, maximum gain, and XPD.
Characterization and Comparison of High Blocking Voltage IGBTs and IEGTs Under Hard- and Soft-Switching Conditions

The market of converters connected to transmission lines continues to require insulated gate bipolar transistors (IGBTs) with higher blocking voltages to reduce the number of IGBTs connected in series in high-voltage converters. To cope with these demands, semiconductor manufactures have developed several technologies. Nowadays, IGBTs up to 6.5-kV blocking voltage and IEGTs up to 4.5-kV blocking voltage are on the market. However, these IGBTs and injection-enhanced gate transistors (IEGTs) still have very high switching losses compared to low-voltage devices, leading to a realistic switching frequency of up to 1 kHz. To reduce switching losses in high-power applications, the auxiliary resonant commutated pole inverter (ARCPI) is a possible alternative. In this paper, switching losses and on-state voltages of NPT-IGBT (3.3 kV-1200 A), FS-IGBT (6.5 kV-600 A), SPT-IGBT (2.5 kV-1200 A, 3.3 kV-1200 A and 6.5 kV-600 A) and IEGT (3.3 kV-1200 A) are measured under hard-switching and zero-voltage switching (ZVS) conditions. The aim of this selection is to evaluate the impact of ZVS on various devices of the same voltage ranges. In addition, the difference in ZVS effects among the devices with various blocking voltage levels is evaluated.
The obesity epidemic in the United States.

We describe the epidemic of obesity in the United States: escalating rates of obesity in both adults and children, and why these qualify as an epidemic; disparities in overweight and obesity by race/ethnicity and sex, and the staggering health and economic consequences of obesity. Physical activity contributes to the epidemic as explained by new patterns of physical activity in adults and children. Changing patterns of food consumption, such as rising carbohydrate intake--particularly in the form of soda and other foods containing high fructose corn syrup--also contribute to obesity. We present as a central concept, the food environment--the contexts within which food choices are made--and its contribution to food consumption: the abundance and ubiquity of certain types of foods over others; limited food choices available in certain settings, such as schools; the market economy of the United States that exposes individuals to many marketing/advertising strategies. Advertising tailored to children plays an important role.
Cross-Sensor Fingerprint Matching Method Based on Orientation, Gradient, and Gabor-HoG Descriptors With Score Level Fusion

The fingerprint is one of the oldest and most widely used biometric modality for person identification. Existing automatic fingerprint matching systems perform well when the same sensor is used for both enrollment and verification (regular matching). However, their performance significantly deteriorates when different sensors are used (cross-matching, fingerprint sensor interoperability problem). We propose an automatic fingerprint verification method to solve this problem. It was observed that the discriminative characteristics among fingerprints captured with sensors of different technology and interaction types are ridge orientations, minutiae, and local multi-scale ridge structures around minutiae. To encode this information, we propose two minutiae-based descriptors: histograms of gradients obtained using a bank of Gabor filters and binary gradient pattern descriptors, which encode multi-scale local ridge patterns around minutiae. In addition, an orientation descriptor is proposed, which compensates for the spurious and missing minutiae problem. The scores from the three descriptors are fused using a weighted sum rule, which scales each score according to its verification performance. Extensive experiments were conducted using two public domain benchmark databases (FingerPass and Multi-Sensor Optical and Latent Fingerprint) to show the effectiveness of the proposed system. The results showed that the proposed system significantly outperforms the state-of-the-art methods based on minutia cylinder-code (MCC), MCC with scale, VeriFinger—a commercial SDK, and a thin-plate spline model.
"How to do things with words" in health professions education.

This paper reports on a qualitative study of journal entries written by students in six health professions participating in the Interprofessional Health Mentors program at the University of British Columbia, Canada. The study examined (1) what health professions students learn about professional language and communication when given the opportunity, in an interprofessional group with a patient or client, to explore the uses, meanings, and effects of common health care terms, and (2) how health professional students write about their experience of discussing common health care terms, and what this reveals about how students see their development of professional discourse and participation in a professional discourse community. Using qualitative thematic analysis to address the first question, the study found that discussion of these health care terms provoked learning and reflection on how words commonly used in one health profession can be understood quite differently in other health professions, as well as on how health professionals' language choices may be perceived by patients and clients. Using discourse analysis to address the second question, the study further found that many of the students emphasized accuracy and certainty in language through clear definitions and intersubjective agreement. However, when prompted by the discussion they were willing to consider other functions and effects of language.
Factors associated with the appearance of gingival papillae.

The purpose of this study was to evaluate factors associated with the fill of inter-dental spaces by gingival papillae.



Ninety-six adult subjects were evaluated. Papilla score (PS), tooth form/shape, interproximal contact length and gingival thickness were recorded for 672 maxillary anterior and first pre-molar interproximal sites. Statistical analyses included a non-parametric chi(2) test, anova, the Mixed Procedure for SAS and Pearson's correlation coefficient (r).



Papilla deficiency was more frequent in older subjects (p<0.05), as papilla height decreased 0.012 mm with each year of increasing age (p<0.05). Competent papillae (complete fill inter-dentally) were associated with: (1) crown width: length >or=0.87; (2) proximal contact length >or=2.8 mm; (3) bone crest-contact point <or=5 mm; and (4) interproximal gingival tissue thickness >or=1.5 mm. Gingival thickness correlated negatively with PS (r=-0.37 to -0.54) and positively with tissue height (r=0.23-0.43). Tooth form (i.e. crown width to length ratio) correlated negatively with PS (r=-0.37 to -0.61). Other parameters failed to show any significant effects.



Gingival papilla appearance was associated significantly with subject age, tooth form/shape, proximal contact length, crestal bone height and interproximal gingival thickness.
Better accuracy with quantified privacy: representations learned via reconstructive adversarial network

The remarkable success of machine learning, especially deep learning, has produced a variety of cloud-based services for mobile users. Such services require an end user to send data to the service provider, which presents a serious challenge to end-user privacy. To address this concern, prior works either add noise to the data or send features extracted from the raw data. They struggle to balance between the utility and privacy because added noise reduces utility and raw data can be reconstructed from extracted features. This work represents a methodical departure from prior works: we balance between a measure of privacy and another of utility by leveraging adversarial learning to find a sweeter tradeoff. We design an encoder that optimizes against the reconstruction error (a measure of privacy), adversarially by a Decoder, and the inference accuracy (a measure of utility) by a Classifier. The result is RAN, a novel deep model with a new training algorithm that automatically extracts features for classification that are both private and useful. It turns out that adversarially forcing the extracted features to only conveys the intended information required by classification leads to an implicit regularization leading to better classification accuracy than the original model which completely ignores privacy. Thus, we achieve better privacy with better utility, a surprising possibility in machine learning! We conducted extensive experiments on five popular datasets over four training schemes, and demonstrate the superiority of RAN compared with existing alternatives.
Analysis of tissues following mesenchymal stromal cell therapy in humans indicates limited long-term engraftment and no ectopic tissue formation.

Mesenchymal stromal cells (MSCs) are explored as a novel treatment for a variety of medical conditions. Their fate after infusion is unclear, and long-term safety regarding malignant transformation and ectopic tissue formation has not been addressed in patients. We examined autopsy material from 18 patients who had received human leukocyte antigen (HLA)-mismatched MSCs, and 108 tissue samples from 15 patients were examined by PCR. No signs of ectopic tissue formation or malignant tumors of MSC-donor origin were found on macroscopic or histological examination. MSC donor DNA was detected in one or several tissues including lungs, lymph nodes, and intestine in eight patients at levels from 1/100 to <1/1,000. Detection of MSC donor DNA was negatively correlated with time from infusion to sample collection, as DNA was detected from nine of 13 MSC infusions given within 50 days before sampling but from only two of eight infusions given earlier. There was no correlation between MSC engraftment and treatment response. We conclude that MSCs appear to mediate their function through a "hit and run" mechanism. The lack of sustained engraftment limits the long-term risks of MSC therapy.
Detecting Advanced Persistent Threats using Fractal Dimension based Machine Learning Classification

Advanced Persistent Threats (APTs) are a new breed of internet based smart threats, which can go undetected with the existing state of-the-art internet traffic monitoring and protection systems. With the evolution of internet and cloud computing, a new generation of smart APT attacks has also evolved and signature based threat detection systems are proving to be futile and insufficient. One of the essential strategies in detecting APTs is to continuously monitor and analyze various features of a TCP/IP connection, such as the number of transferred packets, the total count of the bytes exchanged, the duration of the TCP/IP connections, and details of the number of packet flows. The current threat detection approaches make extensive use of machine learning algorithms that utilize statistical and behavioral knowledge of the traffic. However, the performance of these algorithms is far from satisfactory in terms of reducing false negatives and false positives simultaneously. Mostly, current algorithms focus on reducing false positives, only. This paper presents a fractal based anomaly classification mechanism, with the goal of reducing both false positives and false negatives, simultaneously. A comparison of the proposed fractal based method with a traditional Euclidean based machine learning algorithm (k-NN) shows that the proposed method significantly outperforms the traditional approach by reducing false positive and false negative rates, simultaneously, while improving the overall classification rates.
Explicit Grammar and Implicit Grammar Teaching for English Major Students in University

The status of English grammar teaching in English teaching has weakened and even once disappeared in part English class; until the late 1980s, foreign English teachers had a consistent view of the importance of grammar teaching. In recent years, more and more domestic scholars begin to think about the situation of China and explore the grammar teaching method. This article will review the explicit grammar instruction and implicit grammar teaching research, collect and analyze the integration of explicit grammar instruction and implicit grammar teaching strategy and its advantages in the grammar teaching.
How to Make an Image More Memorable?: A Deep Style Transfer Approach

Recent works have shown that it is possible to automatically predict intrinsic image properties like memorability. In this paper, we take a step forward addressing the question: "Can we make an image more memorable?". Methods for automatically increasing image memorability would have an impact in many application fields like education, gaming or advertising. Our work is inspired by the popular editing-by-applying-filters paradigm adopted in photo editing applications, like Instagram and Prisma. In this context, the problem of increasing image memorability maps to that of retrieving ``memorabilizing'' filters or style ``seeds''. Still, users generally have to go through most of the available filters before finding the desired solution, thus turning the editing process into a resource and time consuming task. In this work, we show that it is possible to automatically retrieve the best style seeds for a given image, thus remarkably reducing the number of human attempts needed to find a good match. Our approach leverages from recent advances in the field of image synthesis and adopts a deep architecture for generating a memorable picture from a given input image and a style seed. Importantly, to automatically select the best style a novel learning-based solution, also relying on deep models, is proposed. Our experimental evaluation, conducted on publicly available benchmarks, demonstrates the effectiveness of the proposed approach for generating memorable images through automatic style seed selection.
RF CMOS low-phase-noise LC oscillator through memory reduction tail transistor

Based on the understanding of flicker noise generation in “silicon metal-oxide semiconductor field-effect transistors” (MOSFETs), a novel method for improving the phase noise performance of a CMOS LC oscillator is presented. Zhou et al. and Hoogee have suggested that the 1 noise can be reduced through a switched gate, and the flicker noise generated is inversely proportional to the gate switching frequency. The novel tail transistor topology is compared to the two popular tail transistor topologies, namely, the fixed biasing tail transistor and without tail transistor. Through this technique, a figure of merit of 193 dB is achieved using a fully integrated CMOS oscillator with a tank quality factor of about 9.
A 3D printed monolithic soft gripper with adjustable stiffness

Soft robotics has recently gained a significant momentum as a newly emerging field in robotics that focuses on biomimicry, compliancy and conformability with safety in near-human environments. Beside conventional fabrication methods, additive manufacturing is a primary technique to employ to fabricate soft robotic devices. We developed a monolithic soft gripper, with variable stiffness fingers, that was fabricated as a one-piece device. Negative pressure was used for the actuation of the gripper while positive pressure was used to vary the stiffness of the fingers of the gripper. Finger bending and gripping capabilities of the monolithic soft gripper were experimentally tested. Finite element simulation and experimental results demonstrate that the proposed monolithic soft gripper is fully compliant, low cost and requires an actuation pressure below −100 kPa.
COMPUTER AIDED CRIME SCENE

The use of Computer Aided Design (CAD) in forensic science is not new. However CAD did not become a (quality) standard for crime scene sketching. If the crime scene sketch is an effective way to present measurements, it must respond to accuracy criteria to supplement the documentary work by note taking and crime scene photography. The forensic photography unit of the Zürich Police changed already some years ago from hand drawn crime scene sketches to CAD sketches. Meanwhile the technique is used regularly for all major crime scene work. Using the Rolleimetric MR-2 single-camera measuring system combined with commercial CAD-software, crime scene sketches of a high quality standard are obtained.
EMG-driven model-based knee torque estimation on a variable impedance actuator orthosis

Development of movement assistance devices used in rehabilitation must take into account the variable human-machine interaction. To this end, electromyography (EMG) sensors detecting the muscle activation are regarded as a feasible method. In this paper, we focus on torque estimation of the human knee joint. The joint motion analysis was captured through the developed EMG sensor, and subsequently torque estimation by using an EMG-driven model. For calibrating the EMG-driven model, the experimental torque was computed by employing the inverse dynamics that have been ensured within a well-established leg-orthosis system. Here, an impedance-controlled variable stiffness actuator has been implemented. To realize a wide range of calibration tasks, we compared the knee torque by the EMG-driven model and the inverse dynamics through setting different impedance controller gains. The proposed calibration results will provide a support towards the applications in the bio-feedback based impedance control.