dataesr/scientific-paragraphs-categorization huggingface dataset:
Implementation We have created a proof-of-concept implementation of the grab 'n' drop toolglass as a Scotty  [10]  plugin for Mac OS X Cocoa applications. Eagan et al. 's Scotty uses runtime toolkit overloading to enable the modification of third-party applications without access to their source code  1. Scotty plugins run inside the host program and thus have access to its internal classes and objects, including documents, views, and their controllers. While this proof-of-concept implementation is effectively a Mac OS X hack using Scotty, neither the concept of the grab 'n' drop toolglass nor the overall implementation approach is dependent on the Mac. Rather, this approach enabled us to effectively extend the Cocoa toolkit to provide a set of generally reusable components that could be provided by other toolkits such as Java Swing or Windows.
In order to pass events to the underlying window, the toolglass clones the currently handled event and injects it into the system event queue (using a Cocoa event tap). That event can then be either consumed or propagated to the standard event stream. It is essential that the state machine for the toolglass preserve standard event symmetry invariants, such as press/release pairing and that drag events always occur between a press and a release. If these invariants are not respected, the program may enter into an undefined state. Widget design patterns: (controllers, target-action) Cocoa applications are typically developed using a model-view-controller (MVC) architecture  [24] , where event handlers are typically implemented in a controller object associated with the interface. Widgets, in turn, use a target-action pattern to designate their controller (the target) and the message 2  to send to the target (action).
Other toolkits typically use a similar mechanism to handle callbacks, signals, or event handlers: when a widget is triggered, the system executes some bit of code with an event describing what happened and what was triggered. In Cocoa, when a widget is triggered, such as by clicking a button or pressing return in a text field, the widget sends its action message to its target with itself as the sole parameter: the sender. Grab: extracting a widget's action Grabbing a widget's "callback" thus entails simply storing a reference to its target and action, and storing a reference to the widget itself. We also create a copy of its backing image in order to store its on-screen representation in the toolglass bucket. Invoking a previously grabbed command whenever it is dropped through the toolglass is then as simple as invoking the action on the target with the source widget as the sender (e.
g. the redeye tool), or after the terminating mouse release, as in the case of an operation on a selection (e. g. the styles chooser). As shown above, actually triggering the callback associated with the source widget is straight-forward and simply involves sending the action message to the target with either the source widget or its proxy as the sender parameter. Serializing the Grab 'n' Drop Toolglass Initially identifying a widget, its target, and its action during a grab operation is easy: they come from the clicked widget. But in order to serialize toolglasses across invocations of the application, we need a way to find these same objects in a new application instance, without a user's click to identify the widget. Furthermore, when loading a previously serialized toolglass, the program may be in a different state.
In order to avoid unforeseen side effects, we look only at instance variable when searching for serialization paths. Comparing the values of two variables is safe. We ignore methods, since they may have unanticipated effects. For example, in some applications, calling the accessor for the current print job could potentially trigger a print job if one does not exist. When loading in a new application instance, however, those attributes might not yet have been initialized. Thus, when deserializing, we prefer to use those very same accessors that we avoided during serialization. In Java and other languages, accessors typically start with get. In Cocoa, methods and attributes are in different namespaces. We therefor look for a method with the same name, or with any leading underscores or a single m removed and re-camel-cased.
The model is effectively opaque and may even be implemented in another programming language, but it is linked to the interface through the controller, which maps widget events to operations on the model. Different programs follow moreor less-clean implementations of this separation, and frequently the PAC model  [5]  under the guise of MVC. Regardless of the particular implementation, analyzing the controller provides a foothold into the underlying functionality of the program, and the links into the operational interface that controls it. Callback-bindings are the linking mechanism between a widget and the controller. In Cocoa, these are implemented using the target-action model where a widget event, such as a button press, triggers an action on its associated target. The target is typically the associated controller, and the action is the associated method.
End-user programming The grab 'n' drop toolglass aims to provide a programming-free means of letting a user adapt an existing user interface to her own needs. Tools such as Sikuli  [28] , Prefab [dixon10], and Scotty  [10] , among many others, provide tools for programmers to alter software interfaces or functionality. Between these two ends of the spectrum lie more advanced end-user programming techniques, aiming to increase raise the ceiling of expressivity while maintaining a low threshold to entry. For example, by using a model of intentionality based on some combination of the user's context and the widgets he or she grabs into the toolglass, the toolglass could potentially provide more seamless interaction without explicit programming. For richer control, EUD environments such as Sikuli could enable the user to go beyond simply extracting existing widgets to including new interactions and functionalities.
This type of interaction is common in physical interactions with real-world tools, where a master craftsman might typically use standard off-the-shelf tools to practice her trade. But when confronted with a particular need, she might create an adapter, or modify the tool in some way to better satisfy her special case. Should that adapter or modification be particularly useful, she might share it with others, possibly even becoming a new member of a standard toolbox, as we have seen with a wide variety of now standard tools: needle-nose pliers, 90-degree screwdrivers, etc. This type of interaction offers an exciting design opportunity to create a broad class of tools. One need not necessarily look as far as the real world to identify these kinds of practices. Unix users have a long and well-documented history of customizing their software with Unix dotfiles  [19] , whether for functional reasons, to work around compatibility issues, or even for self-expression.
Furthermore, sharing cultures  [12, 17]  arising around such customizations have even led to entire communities, with customizations being produced and shared by expert programmers and tinkerers alike  [21]. Furthermore, when confronted with a relevant problem within one's domain, with sufficient motivation, even regular end-users may program extensive customizations for their own particular needs. For example, spreadsheet software has led to vast collections of complex logic programmed by secretaries and other non-computing professionals  [22] , while artists have formed communities around the development and exchange of Photoshop plugins and customizations  [9].
In our use of the grab 'n' drop toolglass, we have not observed data corruption. The worst we have seen has been unexpected program termination (i. e. a crash) induced by a bug (since corrected) in the implementation of the toolglass prototype that injected an extraneous mouse press event or suppressed a mouse release event, breaking the system's underlying assumptions about the symmetry of press/release events. The most common sort of non-bug-related unexpected behavior that we have observed in our testing relates to uncaptured state leading to hidden behavior not exposed by the standard callback model. As we have seen earlier, in the standard target-action design pattern, a widget invokes its target's action method with itself as the sole parameter, the sender, whenever a widget event is triggered (e.
Although our current implementation has worked in our usage, there is a risk that a subclass may only re-use other aspects of the parent class' behavior and may re-implement or bypass the captured behavior of the proxy. In these cases, the resulting behavior is difficult to predict. We have also created a SNDTracerProxy that logs all method calls to the console before forwarding them to the proxied object. This proxy is useful for a programmer to probe any hidden interactions that may involve the proxied object but is beyond the scope of normal user interaction with the toolglass. It may be possible to resolve much of this fragility by extending the target-action design pattern to more fully express some of these relationships. Such extensions, however, would require that programmers change the way that they develop software.
Conclusions & future work The grab 'n' drop toolglass provides a relatively simple interaction technique through which end-users can modify the underlying interaction of existing applications without access to their source code. In many cases, it lets user create new, customized toolglasses suited to their own particular needs without programming and without special support by the underlying application. We have demonstrated a proof-of-concept implementation for Mac OS X Cocoa applications, but variations of this technique should be compatible with other graphical environments. We view the grab 'n' drop toolglass as part of a larger class of user-programmable interactions, wherein the underlying software functionality and interaction is malleable. We plan to continue our work in this area, investigating a combination of novel programming models and interactions techniques to increase the flexibility and control over the software without requiring extensive programming on the part of the user.
Grunts and copulation calls are typically short-distance communications while the wahoos, barks, and yaks carry over longer distances  [27]. From our recordings we finally selected a total of 1335 spontaneous vocalizations for analysis, and after splitting the wahoos into their wa-and-hoo phases, the vocalizations we recorded contained a total of 1404 of what we term "vowel like segments" (VLSs): any continuous section within a vocalization containing a consistent and detectable formant structure. These 1404 VLSs served as the corpus for acoustical analyses. All individual segments were isolated, extracted from silence and extraneous noise, and labelled. The upper part of Table  1  reports quantitative data on these segments, including their frequency of occurrence in the database as well as the number of baboons who produced them.
Analysis was done with successive frames operating as a sliding window overlapping by half each step, and our results and subsequent processing are based on the frame outputs from this LPC processing. The frame database was then filtered to further control for detection errors, and all the frames missing F1 or with F1 or F2 values greater than 3 standard deviations from the means of their VLS categories were eliminated from the dataset (see below). Also, F0 was measured in the same frames using autocorrelation and peak-picking. There is no theoretically definitive method for setting the number of poles in LPC formant detection, so they must be set empirically  [32] , considering sampling rate, frequency range analyzed, and especially fundamental frequency of the signal. As indicated in the bottom part of Table  1 , we chose settings of 30 poles for high F0 VLS (male "wa-" and female "bark"), and settings of 60 poles for the low F0 VLSs (all other VLSs).
5 to 2 seconds long, so that each frame encompassed several utterances. Analysis was done with successive frames operating as a sliding window overlapping by half each step, and our results and subsequent processing are based on the frame outputs from this LPC processing. The frame database was then filtered to further control for detection errors, and all the frames missing F1 or with F1 or F2 values greater than 3 standard deviations from the means of their VLS categories were eliminated from the dataset (see below). Also, F0 was measured in the same frames using autocorrelation and peak-picking. There is no theoretically definitive method for setting the number of poles in LPC formant detection, so they must be set empirically [32] , considering sampling rate, frequency range analyzed, and especially fundamental frequency of the signal.
Acknowledgments This research was conducted at the  Rousset-sur-Arc primate center  ( CNRS-UPS 846 ) and at  GIPSA-lab  ( UMR 5216 CNRS ), France. The authors thank  Romain Lacoste  and the staff of the primate center for technical supports, and  Sophie Jacopin  for providing Figs 1A and5A. Research was supported by grants  ANR-11-LABX-0036  ( BLRI ) and  ANR-11-IDEX-0001-02  (A Ã MIDEX). CK was supported by a  BLRI research post-doctoral grant.
Color images captured by digital devices may contain some non-uniform illuminations. Many enhancement methods produce undesirable results in the aspect of contrast improvement or naturalness preservation. A global and local contrast enhancement method is proposed for adaptively enhancing the non-uniform illumination images. Firstly, a novel global contrast adaptive enhancement algorithm obtains the global enhancement image. Secondly, a huepreserving local contrast adaptive enhancement algorithm produces the local enhancement image. Finally, a contrastbrightness-based fusion algorithm obtains the final result, which represents a trade-off between global contrast and local contrast. This method improves the visual quality and preserves the image naturalness. Experiments are conducted on a dataset including different kinds of non-uniform illumination images.
strates CLAHE and the proposed method outperforms other algorithms in terms of contrast improvement (higher e and r) and pixel value range preservation (lower Σ). The local processing of CLAHE and the fusion processing of the proposed method can generate more visible edges. Since more visible edges may result in unnatural looking images, a comparison of the image naturalness is also considered in this quantitative analysis. Yeganeh et al. [34]  proposed a Statistical Naturalness Measure (SNM) model for evaluating the image quality. They consider the global intensity distributions of a large number of images and build a model to assess the image naturalness. The range of SNM is [0, 1]. The higher value of SNM means the better naturalness of the considered image. We use this metric to evaluate our method and several compared algorithms on the test dataset.
Computation Cost The proposed method contains the global adaptive contrast enhancement with optimization processing, the hue preservation contrast-limited adaptive histogram equalization, and the contrast-brightness-based image fusion framework. We implemented the function of the proposed method in Matlab without improving the code performance. The dataset includes 25 color images. Each color image contains around 200 thousands pixels. The total running time is 20. 25 seconds and the average processing time for one image is 0. 81 seconds. The code performance will be improved with C programming and parallel computing.
We ran the proposed method and several state-of-the-art image enhancement methods on a dataset including 25 nonuniform illumination color images. These test images are widely used in the literature. The comparison algorithms include Rivera et al. 's content-aware enhancement via channel division (Rivera'12) [29] , Lee et al. 's layered difference representation contrast enhancement (Lee'13) [16] , Petro et al. 's improved multiscale retinex contrast enhancement (Petro'14) [24] , Jmal et al. 's enhancement method by saturation feedback and homomorphic filter (Jmal'17) [10], Arici et al. 's histogram-modification-based global contrast enhancement (Arici'09) [3], and the Contrast-Limited Adaptive Histogram Equalization method (CLAHE) [26]. The source codes of Rivera'12, Lee'13, Jmal'17 are available on the authors' homepages.
This resulted in different ranking of the newly introduced species relative to the core species (Fig. 5 ). This ranking could be correctly predicted as a function of its connection to the core: In Networks A and C, new species CU and GA were expressed at similar levels as GU and AA, all being dominated by UC; in Network B, species AG clearly ranked below all those of the core; in Network D, GC fraction was similar to the UC fraction, dominating other species; in Network E, CG fraction was clearly above the core. To help experimental design and interpretation, we developed an application with Matlab to predict network behavior based on the model above. This application is made available as a free standing Windows executable (available on request from the authors), in which three columns of input data are required.
Single-pot network kinetics Reaction mixtures containing WXY (0. 5 µM) of each genotype, equimolar Z (1. 5 µM in three-membered networks and 2. 0 µM in four-membered networks), and 32 P-labeled WXY (≤0. 003 µM) of each genotype, were heated to 80°C for 2 min then cooled to 48°C. Reactions were initiated with the addition of reaction buffer (100 mM MgCl 2 and 30 mM EPPS, pH 7. 5). For time points samples were drawn and rapidly quenched (125 mM EDTA and 2× loading dye containing formamide and bromophenol blue) over 0-5 min and used for rate comparison experiments. WXY and WXYZ RNA bands were separated on 8% polyacrylamide/8M urea gels. Visualization and quantification was done with a Typhoon Trio+ variable mode phosphorimager and its accompanying ImageQuant software (GE Healthcare). A product ratio was calculated by comparing the RNA in the product WXYZ band to the unreacted WXY band (fraction reacted = [reacted/(reacted + unreacted)]).
ACKNOWLEDGMENTS We thank  S. I. Walker ,  C. Mathis , and  W. Hordijk  for advice and useful discussions. This project/publication was made possible through the support of a grant from the  John Templeton Foundation  (grant no. 54466 ). The opinions expressed in this publication are those of the author(s) and do not necessarily reflect the views of the  John Templeton Foundation. February 12, 2017; accepted March 31, 2017.
Random Instances. In this subsection, random instances are generated to further test the performance of the proposed algorithm. We compare our algorithm with the QEA with shifting decoding scheme to demonstrate the effectiveness of our decoding scheme. We also compare it with commercial software CPLEX and Tabu search (TS) algorithm  [10]. The random instances are generated as follows. We set  which belongs to {10, 15, 18, 20, 22}, and let (1, 2) be a uniform distribution between parameters c1 and c2. The random tests were set as two different groups. One (called Group 1) was defined as the following way: the time windows were   =   (30, 120)  and   =   + (10, 750), 1 ≤  ≤ ; the empty moves and the loaded moves were, respectively, defined as  ,+1 =   (3, 6) ,  , = ∑ -1 =  ,+1 , 0 ≤ ,  ≤  + 1, and   = 20 +  ,+1 , 0 ≤  ≤.
Firstly, we compare our algorithm with the QEA with shifting decoding scheme (i. e. , SQEA). Table  3  presents the comparison results between our decoding scheme and shifting decoding scheme on Group 1 and Group 2. We can see that our decoding scheme generally outperforms the shifting decoding scheme in terms of solution quality and CPU times for all random instances. In particular, the deviations (i. e. , AD) of our algorithm from that with shifting decoding generally decrease with the problem size. Besides, our algorithm spent less time than that with shifting decoding for all random instances. Secondly, Tables  4  and 5  report the comparison results for randomly generated instances using our algorithm, Yan's algorithm, and commercial optimization software CPLEX. Columns AD 1 and AD 2 , respectively, represent the standard Note.
Conclusions This paper proposed a hybrid QEA with improved decoding scheme to solve a single robot one-degree cyclic scheduling problem with flexible processing times in a robotic flow shop. In particular, three different decoding procedures were proposed to convert Q-bit individual into robot move sequences. A repairing procedure was designed to repair the infeasible sequence. Both Q-gate and adaptive genetic operators as variant operators were applied to evolve the population. The effectiveness of the proposed algorithm was demonstrated by solving benchmark instances and randomly generated instances compared with commercial software CPLEX and Yan's algorithm. Experimental results indicate that our decoding scheme outperforms the shifting decoding scheme, and the proposed algorithm can provide high-quality solutions within a reasonable time.
The data also enabled other turbulence parameters such as the temperature structure function parameter C 2 T and refractive index structure function parameter C 2 n to be measured by sensors on the UAV, along with radar-inferred refractive index structure function parameter C 2 n;radar. The comprehensive dataset collected during the campaign (from the radar, the UAV, the boundary layer lidar, the ceilometer, and radiosondes) is expected to help obtain a better understanding of turbulent atmospheric structures, as well as arrive at a better interpretation of the radar data.
As an aside of the main objectives of the campaign, a dataset from a specially designed UAV flight around the radar was used to validate the Capon method for retrieving HR images of echo power and hence reinforce the capability of the radar to resolve fine-scale structures in the atmosphere, as shown in the section "Detection of UAVs by MU Radar. " The Shigaraki MU Observatory also houses a UHF wind profiler, a boundary layer lidar, a Rayleigh Raman lidar, and a ceilometer. The facility also enables launch of radiosondes and ozonesondes at the site. Since its establishment in 1984, the MU radar has contributed significantly to our knowledge of turbulence, and gravity waves and their role in middle atmosphere dynamics (e. g. ,  Fukao and Hamazu 2014 , see also reviews by  Kato 2005; Tsuda 2014 ).
See Table  2  for sensor characteristics. The DataHawk UAV carries a custom sensor module that provides high-resolution temperatures via a 5 μm diameter platinum resistance temperature detector (RTD) wire. The wire is excited at constant current as a "coldwire," i. e. , at a very low overheat ratio, so that its voltage drop is proportional to resistance, and therefore insensitive to the flow velocity, resulting in a temperature change less than 0. 002 °C/ms -1. These coldwire temperature measurements have a range of -60 to +40 °C, quantized at 16 bit resolution, for a digital temperature resolution of 0. 0015 °C. The coldwire itself has a thermal time constant of 0. 6 ms at nominal 14 ms -1 airspeed. The voltage data is anti-aliased at 130 Hz and sampled at 700 Hz, and subsequently subsampled at 100 Hz to remain with in the passband of the anti-alias filter.