nils-herrmann/clean_scirepeval huggingface dataset:
Tracking performance of the RLS algorithm applied to an antenna array in a realistic fading environment

In this paper, frequency domain techniques are used to derive the tracking properties of the recursive least squares (RLS) algorithm applied to an adaptive antenna array in a mobile fading environment, expanding the use of such frequency domain approaches for nonstationary RLS tracking to the interference canceling problem that characterizes the use of antenna arrays in mobile wireless communications. The analysis focuses on the effect of the exponential weighting of the correlation estimation filter and its effect on the estimations of the time variant autocorrelation matrix and cross-correlation vector. Specifically, the case of a flat Rayleigh fading desired signal applied to an array in the presence of static interferers is considered with an AR2 fading process approximating the Jakes’ fading model. The result is a mean square error (MSE) performance metric parameterized by the fading bandwidth and the RLS exponential weighting factor, allowing optimal parameter selection. The analytic results are verified and demonstrated with a simulation example.
Prototype rotary percussive drill for the Mars Sample Return mission

Since 1990s Honeybee Robotics has been developing and testing surface coring drills for future planetary missions. Recently, we focused on developing a rotary-percussive core drill for the 2018 Mars Sample Return mission and in particular for the Mars Astrobiology Explorer-Cacher, MAX-C mission. The goal of the 2018 MAX-C mission is to acquire approximately 20 cores from various rocks and outcrops on the surface of Mars. The acquired cores, 1 cm diameter and 5 cm long, would be cached for return back to Earth either in 2022 or 2024, depending which of the MSR architectures is selected. We built a testbed coring drill that was used to acquire drilling data, such as power, rate of penetration, and Weight on Bit, in various rock formations. Based on these drilling data we designed a prototype Mars Sample Return coring drill. The proposed MSR drill is an arm-mounted, standalone device, requiring no additional arm actuation once positioned and preloaded. A low mass, compact transmission internal to the housing provides all of the actuation of the tool mechanisms. The drill uses a rotary-percussive drilling approach and can acquire a 1 cm diameter and 5 cm long core in Saddleback basalt in less than 30 minutes with only ∼20 N Weight on Bit and less than 100 Watt of power. The prototype MSR drill weighs approximately 5 kg1,2.
Ten Simple Rules for Reproducible Research in Jupyter Notebooks

Reproducibility of computational studies is a hallmark of scientific methodology. It enables researchers to build with confidence on the methods and findings of others, reuse and extend computational pipelines, and thereby drive scientific progress. Since many experimental studies rely on computational analyses, biologists need guidance on how to set up and document reproducible data analyses or simulations. In this paper, we address several questions about reproducibility. For example, what are the technical and non-technical barriers to reproducible computational studies? What opportunities and challenges do computational notebooks offer to overcome some of these barriers? What tools are available and how can they be used effectively? We have developed a set of rules to serve as a guide to scientists with a specific focus on computational notebook systems, such as Jupyter Notebooks, which have become a tool of choice for many applications. Notebooks combine detailed workflows with narrative text and visualization of results. Combined with software repositories and open source licensing, notebooks are powerful tools for transparent, collaborative, reproducible, and reusable data analyses.
Radars for the Detection and Tracking of Cruise Missiles

■ The advent of the modern cruise missile, with reduced radar observables and the capability to fly at low altitudes with accurate navigation, placed an enormous burden on all defense weapon systems. Every element of the engagement process, referred to as the kill chain, from detection to target kill assessment, was affected. While the United States held the low-observabletechnology advantage in the late 1970s, that early lead was quickly challenged by advancements in foreign technology and proliferation of cruise missiles to unfriendly nations. Lincoln Laboratory’s response to the various offense/defense trade-offs has taken the form of two programs, the Air Vehicle Survivability Evaluation program and the Radar Surveillance Technology program. The radar developments produced by these two programs, which became national assets with many notable firsts, is the subject of this article.
A syntaxin 1, Galpha(o), and N-type calcium channel complex at a presynaptic nerve terminal: analysis by quantitative immunocolocalization.

Presynaptic Ca(V)2.2 (N-type) calcium channels are subject to modulation by interaction with syntaxin 1 and by a syntaxin 1-sensitive Galpha(O) G-protein pathway. We used biochemical analysis of neuronal tissue lysates and a new quantitative test of colocalization by intensity correlation analysis at the giant calyx-type presynaptic terminal of the chick ciliary ganglion to explore the association of Ca(V)2.2 with syntaxin 1 and Galpha(O). Ca(V)2.2 could be localized by immunocytochemistry (antibody Ab571) in puncta on the release site aspect of the presynaptic terminal and close to synaptic vesicle clouds. Syntaxin 1 coimmunoprecipitated with Ca(V)2.2 from chick brain and chick ciliary ganglia and was widely distributed on the presynaptic terminal membrane. A fraction of the total syntaxin 1 colocalized with the Ca(V)2.2 puncta, whereas the bulk colocalized with MUNC18-1. Galpha(O,) whether in its trimeric or monomeric state, did not coimmunoprecipitate with Ca(V)2.2, MUNC18-1, or syntaxin 1. However, the G-protein exhibited a punctate staining on the calyx membrane with an intensity that varied in synchrony with that for both Ca channels and syntaxin 1 but only weakly with MUNC18-1. Thus, syntaxin 1 appears to be a component of two separate complexes at the presynaptic terminal, a minor one at the transmitter release site with Ca(V)2.2 and Galpha(O), as well as in large clusters remote from the release site with MUNC18-1. These syntaxin 1 protein complexes may play distinct roles in presynaptic biology.
Mix and Match Networks: Encoder-Decoder Alignment for Zero-Pair Image Translation

We address the problem of image translation between domains or modalities for which no direct paired data is available (i.e. zero-pair translation). We propose mix and match networks, based on multiple encoders and decoders aligned in such a way that other encoder-decoder pairs can be composed at test time to perform unseen image translation tasks between domains or modalities for which explicit paired samples were not seen during training. We study the impact of autoencoders, side information and losses in improving the alignment and transferability of trained pairwise translation models to unseen translations. We show our approach is scalable and can perform colorization and style transfer between unseen combinations of domains. We evaluate our system in a challenging cross-modal setting where semantic segmentation is estimated from depth images, without explicit access to any depth-semantic segmentation training pairs. Our model outperforms baselines based on pix2pix and CycleGAN models.
Empirical evaluation of abstract argumentation: Supporting the need for bipolar and probabilistic approaches

In dialogical argumentation, it is often assumed that the involved parties will always correctly identify the intended statements posited by each other and realize all of the associated relations, conform to the three acceptability states (accepted, rejected, undecided), adjust their views whenever new and correct information comes in, and that a framework handling only attack relations is sufficient to represent their opinions. Although it is natural to make these assumptions as a starting point for further research, dropping some of them has become quite challenging. Probabilistic argumentation is one of the approaches that can be harnessed for more accurate user modelling. The epistemic approach allows us to represent how much a given argument is believed or disbelieved by a given person, offering us the possibility to express more than just three agreement states. It comes equipped with a wide range of postulates, including those that do not make any restrictions concerning how initial arguments should be viewed. Thus, this approach is potentially more suitable for handling beliefs of the people that have not fully disclosed their opinions or counterarguments with respect to standard Dung’s semantics. The constellation approach can be used to represent the views of different people concerning the structure of the framework we are dealing with, including situations in which not all relations are acknowledged or when they are seen differently than intended. Finally, bipolar argumentation frameworks can be used to express both positive and negative relations between arguments. In this paper we will describe the results of an experiment in which participants were asked to judge dialogues in terms of agreement and structure. We will compare our findings with the aforementioned assumptions as well as with the constellation and epistemic approaches to probabilistic argumentation and bipolar argumentation.  Dialogical argumentation, probabilistic argumentation, abstract argumentation ∗This research is funded by EPSRC Project EP/N008294/1 “Framework for Computational Persuasion”.We thank the reviewers for their valuable comments that helped us to improve this paper.
Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods

Partial least squares-based structural equation modeling (PLS-SEM) is extensively used in the field of information systems, as well as in many other fields where multivariate statistical methods are employed. One of the most fundamental issues in PLS-SEM is that of minimum sample size estimation. The “10-times rule” has been a favorite due to its simplicity of application, even though it tends to yield imprecise estimates. We propose two related methods, based on mathematical equations, as alternatives for minimum sample size estimation in PLSSEM: the inverse square root method, and the gamma-exponential method. Based on three Monte Carlo experiments, we demonstrate that both methods are fairly accurate. The inverse square root method is particularly attractive in terms of its simplicity of application.
Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection

Due to the ability of encoding and mapping semantic information into a highdimensional latent feature space, neural networks have been successfully used for detecting events to a certain extent. However, such a feature space can be easily contaminated by spurious features inherent in event detection. In this paper, we propose a self-regulated learning approach by utilizing a generative adversarial network to generate spurious features. On the basis, we employ a recurrent network to eliminate the fakes. Detailed experiments on the ACE 2005 and TAC-KBP 2015 corpora show that our proposed method is highly effective and adaptable.
Teaching DSP First with LabVIEW

The proponents of graphical programming (that is using graphics to program a computer, not programming a computer to do graphics) claim graphical programming is better than text-based programming; however text-based programmers far out number graphics-based programmers. This paper describes the preliminary developments of comparing the use of LabVIEW (a graphical programming language) to MATLAB (a text-based language) in teaching discrete-time signal processing (DSP). This paper presents the results of using both methods in a junior-level introduction to DSP class. The students who enter this class have had a course in continuous-time signals and systems but no DSP theory background. Several quarters of concept inventory data have been collected on the MATLAB version of the class. The same inventory was used with the LabVIEW version of the class and the results compared
Silicone-oil-based subvisible particles: their detection, interactions, and regulation in prefilled container closure systems for biopharmaceuticals.

Recent increased regulatory scrutiny concerning subvisible particulates (SbVPs) in parenteral formulations of biologics has led to the publication of numerous articles about the sources, characteristics, implications, and approaches to monitoring and detecting SbVPs. Despite varying opinions on the level of associated risks and method of regulation, nearly all industry scientists and regulators agree on the need for monitoring and reporting visible and subvisible particles. As prefillable drug delivery systems have become a prominent packaging option, silicone oil, a common primary packaging lubricant, may play a role in the appearance of particles. The goal of this article is to complement the current SbVP knowledge base with new insights into the evolution of silicone-oil-related particulates and their interactions with components in prefillable systems. We propose a "toolbox" for improved silicone-oil-related particulate detection and enumeration, and discuss the benefits and limitations of approaches for lowering and controlling silicone oil release in parenterals. Finally, we present surface cross-linking of silicone as the recommended solution for achieving significant SbVP reduction without negatively affecting functional performance.
Partial Recharge Strategies for the Electric Vehicle Routing Problem with Time Windows

The Electric Vehicle Routing Problem with Time Windows (EVRPTW) is an extension to the well-known Vehicle Routing Problem with Time Windows (VRPTW) where the fleet consists of electric vehicles (EVs). Since EVs have limited driving range due to their battery capacities they may need to visit recharging stations while servicing the customers along their route. The recharging may take place at any battery level and after the recharging the battery is assumed to be full. In this paper, we relax the full recharge restriction and allow partial recharging (EVRPTW-PR) which is more practical in the real world due to shorter recharging duration. We formulate this problem as 0-1 mixed integer linear program and develop an Adaptive Large Neighborhood Search (ALNS) algorithm to solve it efficiently. We apply several removal and insertion mechanisms by selecting them dynamically and adaptively based on their past performances, including new mechanisms specifically designed for EVRPTW and EVRPTWPR. We test the performance of ALNS by using benchmark instances from the recent literature. The computational results show that the proposed method is effective in finding high quality solutions and the partial recharging option may significantly improve the routing decisions.
Learning Personalized Pronunciations for Contact Name Recognition

Automatic speech recognition that involves people’s names is difficult because names follow a long-tail distribution and they have no commonly accepted spelling or pronunciation. This poses significant challenges to contact dialing by voice. We propose using personalized pronunciation learning: people can use their own pronunciations for their contact names. We achieve this by implicitly learning from users’ corrections and within minutes making that pronunciation available for the next voice dialing. We show that personalized pronunciations significantly reduce word error for difficult contact names by 15% relatively.
Factors Influencing Employee Knowledge Sharing Capabilities in Electronic Government Agencies in Malaysia

Effective managing and sharing of knowledge has the power to improve individual’s lives and society. However, research has shown that people are reluctant to share. Knowledge sharing (KS) involve not only our knowledge, but a process of giving and receiving of knowledge with others. Knowledge sharing capabilities (KSC) is an individual’s capability to share experience, expertise and know-how with other employees in the organization. Previous studies identified many factors affecting KSC either in public or private sectors. Upon a critical review on factors affecting KS and factors affecting KSC, this paper attempts to examine the factors that have been cited as significant in influencing employees KSC within Electronic Government (EG) agencies in Malaysia. Two capable factors that are considered in this study are technical factor and non-technical factor. This paper proposes an integrated conceptual framework of employees KSC which can be used for research enhancement.
Design and control of a variable footprint mechanism for holonomic omnidirectional vehicles and its application to wheelchairs

A reconfigurable mechanism for varying the footprint of a four-wheeled omnidirectional vehicle is developed and applied to wheelchairs. The variable footprint mechanism consists of a pair of beams intersecting at a pivotal point in the middle. Two pairs of ball wheels at the diagonal positions of the vehicle chassis are mounted, respectively, on the two beams intersecting in the middle. The angle between the two beams varies actively so that the ratio of the wheel base to the tread may change. Four independent servo motors driving the four ball wheels allow the vehicle to move in an arbitrary direction from an arbitrary configuration as well as to change the angle between the two beams and thereby change the footprint. The objective of controlling the beam angle is threefold. One is to augment static stability by varying the footprint so that the mass centroid of the vehicle may be kept within the footprint at all times. The second is to reduce the width of the vehicle when going through a narrow doorway. The third is to apparently change the gear ratio relating the vehicle speed to individual actuator speeds. First the concept of the varying footprint mechanism is described, and its kinematic behavior is analyzed, followed by the three control algorithms for varying the footprint. A prototype vehicle for an application as a wheelchair platform is designed, built, and tested.
Rescuers at risk: a systematic review and meta-regression analysis of the worldwide current prevalence and correlates of PTSD in rescue workers

We sought to estimate the pooled current prevalence of posttraumatic stress disorder (PTSD) among rescue workers and to determine the variables implicated in the heterogeneity observed among the prevalences of individual studies. A systematic review covering studies reporting on the PTSD prevalence in rescue teams was conducted following four sequential steps: (1) research in specialized online databases, (2) review of abstracts and selection of studies, (3) review of reference list, and (4) contact with authors and experts. Prevalence data from all studies were pooled using random effects model. Multivariate meta-regression models were fitted to identify variables related to the prevalences heterogeneity. A total of 28 studies, reporting on 40 samples with 20,424 rescuers, were selected. The worldwide pooled current prevalence was 10%. Meta-regression modeling in studies carried out in the Asian continent had, on average, higher estimated prevalences than those from Europe, but not higher than the North American estimates. Studies of ambulance personnel also showed higher estimated PTSD prevalence than studies with firefighters and police officers. Rescue workers in general have a pooled current prevalence of PTSD that is much higher than that of the general population. Ambulance personnel and rescuers from Asia may be more susceptible to PTSD. These results indicate the need for improving pre-employment strategies to select the most resilient individuals for rescue work, to implement continuous preventive measures for personnel, and to promote educational campaigns about PTSD and its therapeutic possibilities.
HMM-based Indic handwritten word recognition using zone segmentation

This paper presents a novel approach towards Indic handwritten word recognition using zone-wise information. Because of complex nature due to compound characters, modifiers, overlapping and touching, etc., character segmentation and recognition is a tedious job in Indic scripts (e.g. Devanagari, Bangla, Gurumukhi, and other similar scripts). To avoid character segmentation in such scripts, HMMbased sequence modeling has been used earlier in holistic way. This paper proposes an efficient word recognition framework by segmenting the handwritten word images horizontally into three zones (upper, middle and lower) and recognize the corresponding zones. The main aim of this zone segmentation approach is to reduce the number of distinct component classes compared to the total number of classes in Indic scripts. As a result, use of this zone segmentation approach enhances the recognition performance of the system. The components in middle zone where characters are mostly touching are recognized using HMM. After the recognition of middle zone, HMM based Viterbi forced alignment is applied to mark the left and right boundaries of the characters. Next, the residue components, if any, in upper and lower zones in their respective boundary are combined to achieve the final word level recognition. Water reservoir feature has been integrated in this framework to improve the zone segmentation and character alignment defects while segmentation. A novel sliding window-based feature, called Pyramid Histogram of Oriented Gradient (PHOG) is proposed for middle zone recognition. PHOG features has been compared with other existing features and found robust in Indic script recognition. An exhaustive experiment is performed on two Indic scripts namely, Bangla and Devanagari for the performance evaluation. From the experiment, it has been noted that proposed zone-wise recognition improves accuracy with respect to the traditional way of Indic word recognition.