nils-herrmann/clean_scirepeval huggingface dataset:
NVNA versus LSNA: enemies or friends?

The NVNA and the LSNA are powerful instruments which allow to measure the full nonlinear behavior of microwave devices. They are super-oscilloscopes which are able to measure absolute time domain waveforms. Both instruments are based on a different measurement principle: the mixer-based architecture of the NVNA and the sampler based architecture of the LSNA. They both have their advantages and disadvantages. By using these nonlinear measurement instruments, one is able to gain a good insight into the nonlinear behavior of a system. Combining good measurements with good insight, results in good nonlinear modeling.
Understanding patterns and processes in models of trophic cascades

Climate fluctuations and human exploitation are causing global changes in nutrient enrichment of terrestrial and aquatic ecosystems and declining abundances of apex predators. The resulting trophic cascades have had profound effects on food webs, leading to significant economic and societal consequences. However, the strength of cascades-that is the extent to which a disturbance is diminished as it propagates through a food web-varies widely between ecosystems, and there is no formal theory as to why this should be so. Some food chain models reproduce cascade effects seen in nature, but to what extent is this dependent on their formulation? We show that inclusion of processes represented mathematically as density-dependent regulation of either consumer uptake or mortality rates is necessary for the generation of realistic 'top-down' cascades in simple food chain models. Realistically modelled 'bottom-up' cascades, caused by changing nutrient input, are also dependent on the inclusion of density dependence, but especially on mortality regulation as a caricature of, e.g. disease and parasite dynamics or intraguild predation. We show that our conclusions, based on simple food chains, transfer to a more complex marine food web model in which cascades are induced by varying river nutrient inputs or fish harvesting rates.
Facial thermal variations: A new marker of emotional arousal

Functional infrared thermal imaging (fITI) is considered a promising method to measure emotional autonomic responses through facial cutaneous thermal variations. However, the facial thermal response to emotions still needs to be investigated within the framework of the dimensional approach to emotions. The main aim of this study was to assess how the facial thermal variations index the emotional arousal and valence dimensions of visual stimuli. Twenty-four participants were presented with three groups of standardized emotional pictures (unpleasant, neutral and pleasant) from the International Affective Picture System. Facial temperature was recorded at the nose tip, an important region of interest for facial thermal variations, and compared to electrodermal responses, a robust index of emotional arousal. Both types of responses were also compared to subjective ratings of pictures. An emotional arousal effect was found on the amplitude and latency of thermal responses and on the amplitude and frequency of electrodermal responses. The participants showed greater thermal and dermal responses to emotional than to neutral pictures with no difference between pleasant and unpleasant ones. Thermal responses correlated and the dermal ones tended to correlate with subjective ratings. Finally, in the emotional conditions compared to the neutral one, the frequency of simultaneous thermal and dermal responses increased while both thermal or dermal isolated responses decreased. Overall, this study brings convergent arguments to consider fITI as a promising method reflecting the arousal dimension of emotional stimulation and, consequently, as a credible alternative to the classical recording of electrodermal activity. The present research provides an original way to unveil autonomic implication in emotional processes and opens new perspectives to measure them in touchless conditions.
Damage Characterization of the IASC-ASCE Structural Health Monitoring Benchmark Structure by Transfer Function Pole Migration

In this paper, a novel approach to the characterization of structural damage in civil structures is presented. Structural damage often results in subtle changes to structural stiffness and damping properties that are manifested by changes in the location of transfer function characteristic equation roots (poles) upon the complex plane. Using structural response time-history data collected from an instrumented structure, transfer function poles can be estimated using traditional system identification methods. Comparing the location of poles corresponding to the structure in an unknown structural state to those of the undamaged structure, damage can be accurately identified. The IASC-ASCE structural health monitoring benchmark structure is used in this study to illustrate the merits of the transfer function pole migration approach to damage detection in civil structures.
Dyslexia: the Role of Vision and Visual Attention

Dyslexia is more than just difficulty with translating letters into sounds. Many dyslexics have problems with clearly seeing letters and their order. These difficulties may be caused by abnormal development of their visual "magnocellular" (M) nerve cells; these mediate the ability to rapidly identify letters and their order because they control visual guidance of attention and of eye fixations. Evidence for M cell impairment has been demonstrated at all levels of the visual system: in the retina, in the lateral geniculate nucleus, in the primary visual cortex and throughout the dorsal visuomotor "where" pathway forward from the visual cortex to the posterior parietal and prefrontal cortices. This abnormality destabilises visual perception; hence, its severity in individuals correlates with their reading deficit. Treatments that facilitate M function, such as viewing text through yellow or blue filters, can greatly increase reading progress in children with visual reading problems. M weakness may be caused by genetic vulnerability, which can disturb orderly migration of cortical neurones during development or possibly reduce uptake of omega-3 fatty acids, which are usually obtained from fish oils in the diet. For example, M cell membranes require replenishment of the omega-3 docosahexaenoic acid to maintain their rapid responses. Hence, supplementing some dyslexics' diets with DHA can greatly improve their M function and their reading.
Conscious and unconscious emotional learning in the human amygdala

If subjects are shown an angry face as a target visual stimulus for less than forty milliseconds and are then immediately shown an expressionless mask, these subjects report seeing the mask but not the target. However, an aversively conditioned masked target can elicit an emotional response from subjects without being consciously perceived,. Here we study the mechanism of this unconsciously mediated emotional learning. We measured neural activity in volunteer subjects who were presented with two angry faces, one of which, through previous classical conditioning, was associated with a burst of white noise. In half of the trials, the subjects' awareness of the angry faces was prevented by backward masking with a neutral face. A significant neural response was elicited in the right, but not left, amygdala to masked presentations of the conditioned angry face. Unmasked presentations of the same face produced enhanced neural activity in the left, but not right, amygdala. Our results indicate that, first, the human amygdala can discriminate between stimuli solely on the basis of their acquired behavioural significance, and second, this response is lateralized according to the subjects' level of awareness of the stimuli.
Local Anchor Schemes for Seamless and Low-Cost Handover in Coordinated Small Cells

Cellular systems are rapidly evolving from a homogeneous macrocell deployment to a heterogeneous deployment of macrocells overlaid with many small cells. The striking features of small cells include small coverage area, ad-hoc deployment, and flexible backhaul connectivity. These features call for a profound rethinking of traditional cellular concepts including mobility management and interference management among others. Owing to the unique features, coordinated small cells or commonly referred to as network of small cells promises several benefits including efficient mobility management in a rapid and scalable fashion. The problem of handover in a high-density small cell deployment is studied in this work. A novel local anchor-based architecture for a static cluster of small cells is proposed using which new handover schemes are presented. Such clusters are prevalent in the evolving cellular systems involving high-density small cell deployments in urban, residential, and enterprise environments. A mathematical framework is developed using discrete-time-Markov-models to evaluate the proposed schemes. Using this, closed-form expressions for key handover metrics including handover cost and interruption time are derived. Extensive numerical and simulation studies indicate significant savings of over 50 percent in the handover costs and, more importantly, up to 80 percent in the handover interruption time compared to the existing 3GPP scheme for coordinated small cells.
A framework for interpreting social media interactions in the public sector

Social media applications are extending the information and communication technology landscape in the public sector and are used to increase government transparency, participation and collaboration in the U.S. federal government. The success, impact and performance of these new forms of bi directional and networked interactions can provide insights to understand compliance with the mandate of the Open Government Initiative. Many government agencies are experimenting with the use of social media, however very few actively measure the impact of their digital interactions. This article builds on insights from social media directors in the U.S. federal government highlighting their current lack of measurement practices for social media interactions. Based on their articulated needs for measurement, existing rules regulating the extent of measurement practices and technological features of the main social media platforms, a framework is presented that traces online interactions to mission support and the resulting social media tactics. Implications for both researchers and practitioners are discussed.
A Real-Time Bus Positioning System Based on LoRa Technology

With the development of the Internet of Things technology, smart transportation has been continuously studied in recent years. In order to help people identify the real-time location and expected arrival time of buses, this paper proposes a real-time bus positioning system based on Long Range (LoRa) technology. The system can ease the anxiety of people waiting for buses and make the travel smarter and more convenient. The system is composed of terminal devices, data concentrators, cloud servers, and user interface. The terminal device installed on the bus broadcasts its position data to the data concentrators. Then the data concentrators upload data to the cloud server and present it to users. Compared with the traditional real-time bus positioning system, our system operates in the unlicensed frequency band and has a long transmission distance, which does not require any communication costs and repeaters. Experimental results show that our system has the advantages of low power consumption, low packet loss rate and short time delay.
Language Runtime and Optimizations in IBM Streams

Stream processing is important for continuously transforming and analyzing the deluge of data that has revolutionized our world. Given the diversity of application domains, streaming applications must be both easy to write and performant. Both goals can be accomplished by high-level programming languages. Dedicated language syntax helps express stream programs clearly and concisely, whereas the compiler and runtime system of the language help optimize runtime performance. This paper describes the language runtime for the IBM Streams Processing Language (SPL) used to program the distributed IBM Streams platform. It gives a system overview and explains several language-based optimizations implemented in the SPL runtime: fusion, thread placement, fission, and transport optimizations.
MINIATURE X-RAY TUBES UTILIZING CARBON NANOTUBE-BASED COLD CATHODES

The electron field-emission properties of carbon nanotubes enable the fabrication of cold cathodes for a variety of vacuum device applications. The utilization of these cathodes is an attractive alternative for the replacement of thermionic or hot cathodes for generating x-rays.[1] Miniature X-ray tubes [2,3,4] have been fabricated using a triode-style carbon nanotube-based cathodes.[5] In this paper, we report the results of characterization studies, such as beam current dependence on the control gate voltage. Also, results on focal spot measurements and electron-beam modeling will be presented. It is shown that the new x-ray tubes exhibit superior lifetime, stable focus spot, and are capable of pulsed operation. The miniature tube has also been integrated with a high-voltage power supply for operation in a constant anode-current mode, providing a stable platform for x-ray analysis applications.  “Generation of Continuous and Pulse Diagnostic Imaging X-Ray Radiation Using a Carbon-Nanotube-Based Field Emission Cathode”, G. Z. Yue, Q. Qiu, Bo Gao, Y. Cheng, J. Zhang, H. Shimoda, S. Chang, J. P. Lu, and O. Zhou,  (2002).  “Characterization Techniques for Miniature Low-Power X-Ray Tubes”, A. ReyesMena, Melany Moras, Charles Jensen, Steven D. Liddiard, and D. Clark Turner, Advance In X-ray Analysis 47, 2003.  “Improvements in Low-Power, End-Window, Transmission-Target X-Ray Tubes”, Charles Jensen, Stephen M. Elliott, Steven D. Liddiard, A. Reyes-Mena, Melany Moras, and D. Clark Turner, Advance In X-ray Analysis 47, 2003.  “Mobile Miniature X-Ray Source”, D. Clark Turner, Arturo Reyes, Hans K. Pew, Mark W. Lund, Michael Lines, Paul Moody, and Sergei Voronov, US Patent 6661876.  “X-Ray Generating Mechanism Using Electron Field Emission Cathode”, Otto Zhou and J. Lu, US Patent 6,553,096.
The role of the right inferior frontal gyrus: inhibition and attentional control

There is growing interest regarding the role of the right inferior frontal gyrus (RIFG) during a particular form of executive control referred to as response inhibition. However, tasks used to examine neural activity at the point of response inhibition have rarely controlled for the potentially confounding effects of attentional demand. In particular, it is unclear whether the RIFG is specifically involved in inhibitory control, or is involved more generally in the detection of salient or task relevant cues. The current fMRI study sought to clarify the role of the RIFG in executive control by holding the stimulus conditions of one of the most popular response inhibition tasks-the Stop Signal Task-constant, whilst varying the response that was required on reception of the stop signal cue. Our results reveal that the RIFG is recruited when important cues are detected, regardless of whether that detection is followed by the inhibition of a motor response, the generation of a motor response, or no external response at all.
Reproducibility of X-rays and CT arthrography in SLAC, SNAC, SCAC wrists.

The purpose of this study was to assess the inter-observer and intra-observer reproducibility of the interpretation of CT arthrography and plain X-rays for scapholunate advanced collapse (SLAC), scaphoid non-union advanced collapse (SNAC) and scaphoid chondrocalcinosis advanced collapse (SCAC) wrist conditions, as well as the clinical relevance of these imaging modalities. The CT and X-rays images were reviewed twice in a blinded and randomized manner by two experienced orthopedic surgeons specialized in hand surgery, two orthopedic surgery residents and two experienced radiologists specialized in bone and joint imaging. Cohen's kappa and Fleiss' kappa coefficients were used to analyze the reproducibility of interpretation of the radiological examinations. With CT arthrography, the overall diagnosis was often a problem, in terms of both inter- or intra-observer reproducibility. The assessment of the joint line appeared to be fairly reproducible for each observer but was poorly reproducible between different observers. Plain X-rays are not sufficient to assess cartilage quality in degenerative wrist disease. CT arthrography is a reliable examination, but its interpretation is not always standardized. Diagnostic arthroscopy may be justified in doubtful cases.
A Framework for Automatic Personalised Ontology Learning

Understanding or acquiring a user's information needs from their local information repository (e.g. a set of example-documents that are relevant to user information needs) is important in many applications. However, acquiring the user's information needs from the local information repository is very challenging. Personalised ontology is emerging as a powerful tool to acquire the information needs of users. However, its manual or semi-automatic construction is expensive and time-consuming. To address this problem, this paper proposes a model to automatically learn personalised ontology by labelling topic models with concepts, where the topic models are discovered from a user's local information repository. The proposed model is evaluated by comparing against ten baseline models on the standard dataset RCV1 and a large ontology LCSH. The results show that the model is effective and its performance is significantly improved.
Maintenance of a Minimum Spanning Forest in a Dynamic Planar Graph

We give efficient algorithms for maintaining a minimum spanning forest of a planar graph subject to on-line modifications. The modifications supported include changes in the edge weights, and insertion and deletion of edges and vertices. To implement the algorithms, we develop a data structure called an edge-or&reck dynumic tree, which is a variant of the dynamic tree data structure of Sleator and Tarjan. Using this data structure, our algorithms run in O(logn) time per operation and O(n) space. The algorithms can be used to maintain the connected components of a dynamic planar graph in O(logn) time per operation. Computer Science Laboratory, Xerox PARC, 3333 Coyote Hill Rd., Palo Alto, CA 94304. This work was done while the author was at the Department of Computer Science, Columbia University, New York, NY 10027. Department of Computer Science, Columbia University, New York, NY 10027 and Dipartmento di Informatica e Sistemistica, Universitb di Roma, Rome, Italy. ***Department of Computer Science, Brown University, Box 1910, Providence, RI 02912-1910.  This work was done while the author was at Department of Computer Science, Princeton University, Princeton, NJ 08544.
Overlapping correlation clustering

We introduce a new approach for finding overlapping clusters given pairwise similarities of objects. In particular, we relax the problem of correlation clustering by allowing an object to be assigned to more than one cluster. At the core of our approach is an optimization problem in which each data point is mapped to a small set of labels, representing membership in different clusters. The objective is to find a mapping so that the given similarities between objects agree as much as possible with similarities taken over their label sets. The number of labels can vary across objects. To define a similarity between label sets, we consider two measures: (i) a 0–1 function indicating whether the two label sets have non-zero intersection and (ii) the Jaccard coefficient between the two label sets. The algorithm we propose is an iterative local-search method. The definitions of label set similarity give rise to two non-trivial optimization problems, which, for the measures of set-intersection and Jaccard, we solve using a greedy strategy and non-negative least squares, respectively. We also develop a distributed version of our algorithm based on the BSP model and implement it using a Pregel framework. Our algorithm uses as input pairwise similarities of objects and can thus be applied when clustering structured objects for which feature vectors are not available. As a proof of concept, we apply our algorithms on three different and complex application domains: trajectories, amino-acid sequences, and textual documents.
Aqueous enzyme assisted oil extraction from oilseeds and emulsion de-emulsifying methods: a review

14 Regulatory, safety, and environmental issues have prompted the development of aqueous 15 enzymatic extraction (AEE) for extracting components from oil-bearing materials. The 16 emulsion resulting from AEE requires de-emulsification to separate the oil; when enzymes 17 are used for this purpose, the method is known as aqueous enzymatic emulsion de18 emulsification (AEED). In general, enzyme assisted oil extraction is known to yield oil 19 having highly favourable characteristics. This review covers technological aspects of 20 enzyme assisted oil extraction, and explores the quality characteristics of the oils obtained, 21 focusing particularly on recent efforts undertaken to improve process economics by 22 recovering and reusing enzymes.
Bilateral LSTM: A Two-Dimensional Long Short-Term Memory Model With Multiply Memory Units for Short-Term Cycle Time Forecasting in Re-entrant Manufacturing Systems

Forecasting short-term cycle time (CT) of wafer lots is crucial for production planning and control in the wafer manufacturing. A novel recurrent neural network called “bilateral long short-term memory (bilateral LSTM)” is proposed to model a short-term cycle time forecasting (CTF) of each re-entrant period of a wafer lot. First, a two-dimensional (2-D) architecture is designed to transmit the wafer and layer correlations by using wafer and layer connections. Subsequently, aiming to store various error signals caused by the diverse CT data, a multiply memory structure is presented to extend the capacity of constant error carousel (CEC) in the LSTM model. The experiment results indicate that the proposed model outperforms conventional models in the accuracy and stability for the short-term CTF. Further comparative experiments reveal that the 2-D architecture can enhance the prediction accuracy and the multi-CEC structure can improve the forecasting stability for the short-term CTF of wafer lots.
Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy

Astrophysics and cosmology are rich with data. The advent of wide-area digital cameras on large aperture telescopes has led to ever more ambitious surveys of the sky. Data volumes of entire surveys a decade ago can now be acquired in a single night, and real-time analysis is often desired. Thus, modern astronomy requires big data know-how, in particular, highly efficient machine learning and image analysis algorithms. But scalability isn't the only challenge: astronomy applications touch several current machine learning research questions, such as learning from biased data and dealing with label and measurement noise. The authors argue that this makes astronomy a great domain for computer science research, as it pushes the boundaries of data analysis. They focus here on exemplary results, discuss main challenges, and highlight some recent methodological advancements in machine learning and image analysis triggered by astronomical applications.