nils-herrmann/clean_scirepeval huggingface dataset:
23.8 A 34V charge pump in 65nm bulk CMOS technology

Recent advances in MEMS-based oscillators have resulted in their proliferation in timing applications that were once exclusive to quartz-based devices . For applications requiring low phase noise - e.g., cellular, GPS and high-speed serial links - one possible approach is to bias the MEMS resonator at a higher DC voltage to reduce its motional impedance and increase signal energy [2]. Realizing high-voltage charge pumps in bulk CMOS technology is limited by the breakdown voltage of the well/substrate diodes shown in Fig. 23.8.1(a) and Fig. 23.8.1(b). This breakdown limit is even lower with technology scaling and is <;10V in a 22nm CMOS node. Systems with high-voltage requirements often resort to older, high-voltage-tolerant nodes or exotic technologies that limit MEMS integration into SoCs. This work demonstrates a charge pump design in 65nm technology with a three-fold increase in the output voltage range. Highvoltage tolerance is enabled by the proposed well-biasing arrangement and oxide isolation. The pump achieves 34V output by using three different charge pump cells that tradeoff achievable voltage range and power efficiency to achieve a peak efficiency of 38%. Additionally, finger capacitors are optimized to ensure reliability while maintaining efficiency.
UWB Printed Slot Antenna With Bluetooth and Dual Notch Bands

A novel technique to add an extra Bluetooth band and dual notch bands simultaneously to a compact ultrawideband (UWB) printed slot antenna is proposed. The UWB slot antenna, covering 3.1-11 GHz, is in the shape of an octagon and is fed by a rectangular patch with a beveled bottom edge. To create an extra band outside the UWB frequency range, centered at the 2.4-GHz Bluetooth band, a quarter-wavelength stub is attached to the high concentrated current area in the ground plane. Two notch bands, centered at 3.5-GHz WiMAX and 5.8-GHz WLAN, are also created by placing two stubs similar to that of the extra band. The proposed antenna has a compact size (23 × 28 mm 2), almost stable radiation pattern, and reflection coefficient of lower than -10 dB across the entire passband. A prototype of the proposed antenna is fabricated, and the measured results are shown to be in good agreement with the simulated results.
Malicious PDF detection using metadata and structural features

Owed to their versatile functionality and widespread adoption, PDF documents have become a popular avenue for user exploitation ranging from large-scale phishing attacks to targeted attacks. In this paper, we present a framework for robust detection of malicious documents through machine learning. Our approach is based on features extracted from document metadata and structure. Using real-world datasets, we demonstrate the the adequacy of these document properties for malware detection and the durability of these features across new malware variants. Our analysis shows that the Random Forests classification method, an ensemble classifier that randomly selects features for each individual classification tree, yields the best detection rates, even on previously unseen malware.
 Indeed, using multiple datasets containing an aggregate of over 5,000 unique malicious documents and over 100,000 benign ones, our classification rates remain well above 99% while maintaining low false positives of 0.2% or less for different classification parameters and experimental scenarios. Moreover, the classifier has the ability to detect documents crafted for targeted attacks and separate them from broadly distributed malicious PDF documents. Remarkably, we also discovered that by artificially reducing the influence of the top features in the classifier, we can still achieve a high rate of detection in an adversarial setting where the attacker is aware of both the top features utilized in the classifier and our normality model. Thus, the classifier is resilient against mimicry attacks even with knowledge of the document features, classification method, and training set.
Capturing Handwritten Ink Strokes with a Fast Video Camera

We present a system for capturing ink strokes written with ordinary pen and paper using a fast camera with a frame rate comparable to a stylus digitizer. From the video frames, ink strokes are extracted and used as input to an online handwriting recognition engine. A key component in our system is a pen up/down detection model for detecting the contact of the pen-tip with the paper in the video frames. The proposed model consists of feature representation with convolutional neural networks and classification with a recurrent neural network. We also use a high speed tracker with kernelized correlation filters to track the pen-tip. For training and evaluation, we collected labeled video data of users writing English and Japanese phrases from public datasets, and we report on character accuracy scores for different frame rates in the two languages.
Design of Multiplate Back-Reflector for a Wideband Slot Antenna

A novel multiplate back-reflector wide-bandwidth unidirectional slot antenna is presented in this letter. The proposed structure not only maintains the wideband nature of slot structure, but also exhibits stable antenna gain and better front-to-back (F/B) ratio over the frequency band of interest. A slot antenna consisting of a pair of matching disc patches and a microstrip feeding line is employed to demonstrate the advantages of the new reflector structure. Four smaller metallic plates in different distances are used. Initial parameter measurement of the new slot antenna is conducted. The result shows an impedance bandwidth can exceed the original slot antenna bandwidth for $S_{11}\leq-10~$dB in the range 1.56–3.14 GHz, stable gain more than 8.5 dB, and F/B ratio higher than 17 dB, with unidirectional radiation patterns and low cross polarization over the whole operating band. Those performances are exceeding the performance that a conventional back-reflector slot antenna can obtain.
Childhood maltreatment and intimate partner violence in dissociative disorder patients

Childhood maltreatment (CM) is a risk factor for subsequent intimate partner violence (IPV) in adulthood, with high rates of retrospectively reported CM among IPV victims and perpetrators. A theorized mechanism of the link between CM and IPV is dissociation. Dissociation may allow perpetrators of violence to remain emotionally distant from their behavior and minimize empathy toward those they victimize, enabling them to commit acts of violence similar to their own experiences. Indeed, elevated rates of dissociation and dissociative disorders (DD) have been found among IPV survivors and perpetrators. In addition, in pilot studies, DD clinicians have reported high levels of violent behavior among DD patients.



The present study investigates IPV among DD patients with Dissociative Identity Disorder and Dissociative Disorder Not Otherwise Specified, a group with CM rates of 80-95% and severe dissociative symptoms.



DD clinicians reported on rates of CM and IPV among 275 DD patients in outpatient treatment. DD patients also completed a self-report measure of dissociation. Analyses assessed the associations between CM typologies and IPV, as well as trait dissociation and IPV.



Physical and emotional child abuse were associated with physical IPV, and childhood witnessing of domestic violence (DV) and childhood neglect were associated with emotional IPV.



The present study is the first to provide empirical support for a possible CM to adult IPV developmental trajectory among DD patients. Future research is needed to better understand the link between CM and IPV among those with trauma and DD.
On the design and implementation of linear differential microphone arrays.

Differential microphone array (DMA), a particular kind of sensor array that is responsive to the differential sound pressure field, has a broad range of applications in sound recording, noise reduction, signal separation, dereverberation, etc. Traditionally, an Nth-order DMA is formed by combining, in a linear manner, the outputs of a number of DMAs up to (including) the order of N - 1. This method, though simple and easy to implement, suffers from a number of drawbacks and practical limitations. This paper presents an approach to the design of linear DMAs. The proposed technique first transforms the microphone array signals into the short-time Fourier transform (STFT) domain and then converts the DMA beamforming design to simple linear systems to solve. It is shown that this approach is much more flexible as compared to the traditional methods in the design of different directivity patterns. Methods are also presented to deal with the white noise amplification problem that is considered to be the biggest hurdle for DMAs, particularly higher-order implementations.
Visual Categorization with Bags of Keypoints

We present a novel method for generic visual categorization: the problem of identifying the object content of natural images while generalizing across variations inherent to the object class. This bag of keypoints method is based on vector quantization of affine invariant descriptors of image patches. We propose and compare two alternative implementations using different classifiers: Naïve Bayes and SVM. The main advantages of the method are that it is simple, computationally efficient and intrinsically invariant. We present results for simultaneously classifying seven semantic visual categories. These results clearly demonstrate that the method is robust to background clutter and produces good categorization accuracy even without exploiting geometric information.
Preventing Student Dropout in Distance Learning Using Machine Learning Techniques

Student dropout occurs quite often in universities providing distance education. The scope of this research is to study whether the usage of machine learning techniques can be useful in dealing with this problem. Subsequently, an attempt was made to identifying the most appropriate learning algorithm for the prediction of students' dropout. A number of experiments have taken place with data provided by the ‘informatics' course of the Hellenic Open University and a quite interesting conclusion is that the Naive Bayes algorithm can be successfully used. A prototype web based support tool, which can automatically recognize students with high probability of dropout, has been constructed by implementing this algorithm.
A Broadband Dual Circularly Polarized Patch Antenna With Wide Beamwidth

A broadband dual circularly polarized patch antenna with wide beamwidth is presented. The patch is excited by four cross slots via a microstrip line with multiple matching segments underneath the ground plane. The four cross slots and the multiple matching segments are optimized simultaneously to obtain the best performance. Measurements show that the antenna has 10-dB return-loss bandwidth of 24%, 10-dB isolation bandwidth of 19%, 3-dB axial-ratio bandwidth of 16%, and beamwidth of 110 °. Moreover, the single feed makes it a good candidate for array design.
Representing Meaning with a Combination of Logical Form and Vectors

NLP tasks differ in the semantic information they require, and at this time no single semantic representation fulfills all requirements. Logic-based representations characterize sentence structure, but do not capture the graded aspect of meaning. Distributional models give graded similarity ratings for words and phrases, but do not adequately capture overall sentence structure. So it has been argued that the two are complementary. In this paper, we adopt a hybrid approach that combines logic-based and distributional semantics through probabilistic logic inference in Markov Logic Networks (MLNs). We focus on textual entailment (RTE), a task that can utilize the strengths of both representations. Our system is three components, 1) parsing and task representation, where input RTE problems are represented in probabilistic logic. This is quite different from representing them in standard first-order logic. 2) knowledge base construction in the form of weighted inference rules from different sources like WordNet, paraphrase collections, and lexical and phrasal distributional rules generated on the fly. We use a variant of Robinson resolution to determine the necessary inference rules. More sources can easily be added by mapping them to logical rules; our system learns a resource-specific weight that counteract scaling differences between resources. 3) inference, where we show how to solve the inference problems efficiently. In this paper we focus on the SICK dataset, and we achieve a state-of-the-art result. Our system handles overall sentence structure and phenomena like negation in the logic, then uses our Robinson resolution variant to query distributional systems about words and short phrases. Therefor, we use our system to evaluate distributional lexical entailment approaches. We also publish the set of rules queried from the SICK dataset, which can be a good resource to evaluate them.
A case of accidental decapitation in a farm worker.

Every year in France, about 75,000 occupational accidents are reported among farm workers, of which 150 are fatal [1, 2]. One third of these deaths involve machines and usually result from crushing injuries. However, decapitations resulting from occupational accidents in the agricultural setting appear to be rare events. We report the unusual case of a farmer who suffered decapitation after his scarf was accidentally caught by the drive shaft of a trailer.
Social Learning Theory and Developmental Psychology : The Legacies of

Social learning theory began as an attempt by Robert Sears and others to meld psychoanalytic and stimulus-response learning theory into a comprehensive explanation of human behavior, drawing on the clinical richness of the former and the rigor of the latter. Albert Bandura abandoned the psychoanalytic and drive features of the approach, emphasizing instead cognitive and informationprocessing capacities that mediate social behavior. Both theories were intended as a general framework for the understanding of human behavior, and their developmental aspects remain to be worked out in detail. Nevertheless, Bandura has provided a strong theoretical beginning: The theory appears to be capable of accounting well for existing developmental data as well as guiding new investigation.
Ionospheric Time-Delay Algorithm for Single-Frequency GPS Users

The goal in designing an ionospheric time-delay correctionalgorithm for the single-frequency global positioning system userwas to include the main features of the complex behavior of theionosphere, yet require a minimum of coefficients and usercomputational time, while still yielding an rms correction of at least50 percent. The algorithm designed for this purpose, andimplemented in the GPS satellites, requires only eight coefficientssent as part of the satellite message, contains numerousapproximations designed to reduce user computationalrequirements, yet preserves the essential elements required to obtaingroup delay values along multiple satellite viewing directions.
DivQ: diversification for keyword search over structured databases

Keyword queries over structured databases are notoriously ambiguous. No single interpretation of a keyword query can satisfy all users, and multiple interpretations may yield overlapping results. This paper proposes a scheme to balance the relevance and novelty of keyword search results over structured databases. Firstly, we present a probabilistic model which effectively ranks the possible interpretations of a keyword query over structured data. Then, we introduce a scheme to diversify the search results by re-ranking query interpretations, taking into account redundancy of query results. Finally, we propose α-nDCG-W and WS-recall, an adaptation of α-nDCG and S-recall metrics, taking into account graded relevance of subtopics. Our evaluation on two real-world datasets demonstrates that search results obtained using the proposed diversification algorithms better characterize possible answers available in the database than the results of the initial relevance ranking.
Game mechanics telling stories? An experiment

We investigate the question of how game mechanics influence or produce narrative by way of empirical experiment. The experimental setup involves a game with reconfigurable game mechanics and minimal representational elements. 69 players played games with randomized game mechanics, and 16 players formed a control group playing with a fixed set of mechanics. All players were asked to describe the story of the game with minimal prompting, and these free-text answers were tagged. Associations between tags and mechanics were analyzed using correlation analysis and frequent pattern mining. It was found that the choice of mechanics significantly impact what stories players tell about the game, but that most of the associations are relatively simple and straightforward.
The evolution of Crew Resource Management training in commercial aviation.

In this study, we describe changes in the nature of Crew Resource Management (CRM) training in commercial aviation, including its shift from cockpit to crew resource management. Validation of the impact of CRM is discussed. Limitations of CRM, including lack of cross-cultural generality are considered. An overarching framework that stresses error management to increase acceptance of CRM concepts is presented. The error management approach defines behavioral strategies taught in CRM as error countermeasures that are employed to avoid error, to trap errors committed, and to mitigate the consequences of error.
Privacy-Preserving Online Task Assignment in Spatial Crowdsourcing with Untrusted Server

With spatial crowdsourcing (SC), requesters outsource their spatiotemporal tasks (tasks associated with location and time) to a set of workers, who will perform the tasks by physically traveling to the tasks' locations. However, current solutions require the locations of the workers and/or the tasks to be disclosed to untrusted parties (SC server) for effective assignments of tasks to workers. In this paper we propose a framework for assigning tasks to workers in an online manner without compromising the location privacy of workers and tasks. We perturb the locations of both tasks and workers based on geo-indistinguishability and then devise techniques to quantify the probability of reachability between a task and a worker, given their perturbed locations. We investigate both analytical and empirical models for quantifying the worker-task pair reachability and propose task assignment strategies that strike a balance among various metrics such as the number of completed tasks, worker travel distance and system overhead. Extensive experiments on real-world datasets show that our proposed techniques result in minimal disclosure of task locations and no disclosure of worker locations without significantly sacrificing the total number of assigned tasks.
Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing

The primary sensory system requires the integrated function of multiple cell types, although its full complexity remains unclear. We used comprehensive transcriptome analysis of 622 single mouse neurons to classify them in an unbiased manner, independent of any a priori knowledge of sensory subtypes. Our results reveal eleven types: three distinct low-threshold mechanoreceptive neurons, two proprioceptive, and six principal types of thermosensitive, itch sensitive, type C low-threshold mechanosensitive and nociceptive neurons with markedly different molecular and operational properties. Confirming previously anticipated major neuronal types, our results also classify and provide markers for new, functionally distinct subtypes. For example, our results suggest that itching during inflammatory skin diseases such as atopic dermatitis is linked to a distinct itch-generating type. We demonstrate single-cell RNA-seq as an effective strategy for dissecting sensory responsive cells into distinct neuronal types. The resulting catalog illustrates the diversity of sensory types and the cellular complexity underlying somatic sensation.
Medical Diagnosis for Liver Cancer using Classification Techniques

The important and successful applications of data mining are in fields like business intelligence, finance, digital libraries, in other industries and sectors. One of the applications of data mining is medical diagnosis which is mostly used in research area. Medical diagnosis is the field where many researchers are concentrating. To reduce the diagnosis time and improve the diagnosis accuracy, it has become an important issue. In medical, Liver Cancer is one of the most prevalent and deadly cancers in human beings. Liver cancer is difficult to be diagnosed at an early stage due to the risk factors. Therefore, new metrologies for early Liver Cancer are needed to determine the condition of the Liver Cancer. This paper encapsulates various review and research articles on liver cancer. The main goal of this review paper is to study the related works on cancer especially liver cancer. In this paper we present an overview of the current research being carried out using the data mining techniques. Various Data classification techniques or algorithms are used to solve this issue. Some classification techniques or algorithms are Decision tree, C4.5, Association rule, Bayesian networks, Support vector Machine, K-NN, Neural networks etc.
A near-threshold 7T SRAM cell with high write and read margins and low write time for sub-20 nm FinFET technologies

In this paper, a 7T SRAM cell with differential write and single ended read operations working in the near-threshold region is proposed. The structure is based on modifying a recently proposed 5T cell which uses high and low VTH transistors to improve the read and write stability. To enhance the read static noise margin (RSNM) while keeping the high write margin and low write time, an extra access transistor is used and the threshold voltages of the SRAM transistors are appropriately set. In addition, to maintain the low leakage power of the cell and increase the Ion/Ioff ratio of its access transistors, a high VTH transistor is used in the pull down path of the cell. To assess the efficacy of the proposed cell, its characteristics are compared with those of 5T, 6T, 8T, and 9T SRAM cells. The characteristics are obtained from HSPICE simulations using 20 nm, 16 nm, 14 nm, 10 nm, and 7 nm FinFET technologies assuming a supply voltage of 500 mV. The results reveal high write and read margins, the highest Ion/Ioff ratio, a fast write, and ultra-low leakage power in the hold “0” state for the cell. Therefore, the suggested 7T cell may be considered as one of the better design choices for both high performance and low power applications. Also, the changes of cell parameters when the temperature rises from 40 1C to 100 1C are investigated. Finally, the write margin as well as the read and hold SNMs of the cell in the presence of the process variations are studied at two supply voltages of 400 mV and 500 mV. The study shows that the proposed cell meets the required cell sigma value (6σ) under all conditions. & 2015 Elsevier B.V. All rights reserved.