dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements Joschka Roffe  was supported by a  Durham Doctoral Studentship (Faculty of Science). Nicholas Chancellor ,  Dominic Horsman  and  Viv Kendon  were supported by  EPSRC  (grant ref:grant ref:  EP/L022303/1 ). We acknowledge use of the  IBM Quantum Experience  for this work. The views expressed are those of the authors and do not reflect the official policy or position of IBM or the  IBM Quantum Experience team. The quantum circuits in this paper were drawn using the QPIC package by  Thomas Draper  and  Samuel Kutin  [51].
The problem may become intractable when the network structure varies daily or is known with less certainty over longer period. In such instances, a high quality heuristic solution may be more preferable than an exact solution with excessive run time. We propose an improved version of column generation approach integrating an efficient genetic algorithm to obtain fast and high-quality solutions for a sustainable spare parts delivery problem. More specifically, we propose to retain the traditional column generation iterative framework, with master problem solved exactly, but with pricing subproblem solved using a metaheuristic. Computational results on a real dataset indicate that this approach yields improved solutions compared to the current best-case business-as-usual costs. It also substantially decreases the computational time; allowing for high-quality, tractable solutions to be obtained in few minutes.
Computational Experiments Both Column Generation (CG) and Genetic Column Generation (GCG) are programmed in MATLAB and the IBM ILOG CPLEX solver API has been employed as the the Integer Linear Programming (ILP) solver. While the ILP solver is called for solving both the master problem and subproblem in CG, in GCG it is only used for solving the Master problem. The experiments have been run on Linux operating system on a single AMD Opteron CPU with 2. 3 GHz speed. We generated 40 random problem instances named inst01-inst40, with 20,30,40 and 50 drop-points. For each class of a specific size, 10 random problems were generated, in which each drop-point location is defined by an (x, y)-pair where x and y are uniformly random and 0 < x, y < 100. Each problem instance was run once with the CG procedure and 10 times with the GCG procedure to obtain an average result.
Shared memory architectures are usually used to efficiently deal with tasks in parallel. A parallel implementation of the MMsFE method for one single node has been proposed in  [12]  using the Matlab MRST toolbox  [13]. For multi and manycores architectures, a parallel implementation has also been successfully developed in  [14]. However, for large scale simulations, distributed memory computing is required. A recent work presents a hybrid parallel implementation within the DUNE framework  [15]. Here, the hybrid parallel programming model consists in using a shared memory programming within SMP nodes and MPI-communications between nodes. In this approach, no communication is needed within one node. However, this requires to develop effective threadsafe components everywhere in the simulator.
Recent years have witnessed a growing interest in behavioral and neuroimaging studies on the processing of symbolic communicative gestures, such as pantomimes and emblems, but well-controlled stimuli have been scarce. This study describes a dataset of more than 200 video clips of an actress performing pantomimes (gestures that mimic object-directed/object-use actions; e. g. , playing guitar), emblems (conventional gestures; e. g. , thumbs up), and meaningless gestures. Gestures were divided into four lists. For each of these four lists, 50 Italian and 50 American raters judged the meaningfulness of the gestures and provided names and descriptions for them. The results of these rating and norming measures are reported separately for the Italian and American raters, offering the first normed set of meaningful and meaningless gestures for experimental studies.
Moreover, in different studies the stimuli have undergone different procedures for selection (e. g. , different dimensions of the stimuli have been considered and rated in different studies). Crucially, not all previous studies on symbolic gestures have tested their comprehensibility and the overlap between the intended meaning (i. e. , the meaning intended by the producer) and the meaning understood by the perceiver (i. e. , the subject). All of these are sources of variability that could significantly affect the results of behavioral and neuroimaging studies and hinder replication of the observations from previous studies. In the present study, we aimed to provide a publicly available database of high-resolution videos of pantomimes and emblems and a control set of meaningless gestures. Meaningless gestures are movements that do not evoke any conventional meaning that are designed to reproduce the surface features of the meaningful gestures and to provide stimuli for a control condition in behavioral and neuroimaging studies that seek to study the processing and representation of meaningful gestures.
The gestures were based on the Italian culture and were performed by an Italian actress in Italy. To investigate possible cultural differences and evaluate the generalizability of the dataset to a non-Italian population, the gestures were rated by two groups of participants: Italians and Americans. Our aim was to provide a measure of the comprehensibility of these gestures across two cultures, by asking participants to complete three tasks: (1) judging the meaningfulness of each gesture, (2) naming the gesture (naming task), and (3) describing the meaning of the gesture (description). To the best of our knowledge, two sets of video clips are currently publicly available, one involving ballet dance movements  (Christensen, Nadal, Cela-Conde, & Gomila, 2014 ) and another involving pantomimes  (van Nispen et al.
, 2017). Therefore, this is the first normed database of video clips to include different types of meaningful gestures (emblems and pantomimes) along with meaningless (control) gestures. The features of this database make it suitable for research on neural, cognitive, social, and anthropological aspects of gesture processing. As of today, the stimuli from the current database have been used to investigate the performance in action recognition and prediction of individuals with upper limb dysplasia  (Vannuscorps & Caramazza, 2016)  and to investigate the neural representation of gestures in the middle temporal gyrus  (Agostini, Papeo, & Lingnau, 2016).
Procedure For each item in the four lists, raters were asked to (1) rate the meaningfulness of the gesture, (2) name the gesture, and (3) verbally describe the gesture. Data were collected using an online rating site called Qualtrics (http://www. qualtrics. com/). For Americans, the rating was administered through Amazon Mechanical Turk (https://www. mturk. com/). To ensure the inclusion of reliable raters, only raters with at least a 95% approval rate in previous surveys were included. Since Mechanical Turk is not widely used in Italy, the rating for Italians was administered via email. The Italian participants were recruited using the Center for Mind/Brain Sciences (CIMeC) Facebook page. In each trial, a video clip was presented at the upper part of the screen on a black background, followed by the three tasks: (1) Meaningfulness rating Raters were asked to indicate the meaningfulness of each gesture on a 7-point Likert scale (1 = not meaningful at all, 7 = very meaningful).
They were instructed to use only one or two words, if possible. This task was designed to check the comprehensibility of the gestures (i. e. , the correspondence between the intended meaning and the meaning assigned by the participants) and the consistency across participants of the interpretation and labeling of the gesture. (3) Description Raters were asked to provide a description of the meaning of the gesture. The aim of this task was to examine the consistency across participants of the interpretation of the gesture. At the beginning of each survey, a video clip was presented as an example, for each of the three stimulus types (pantomimes, emblems, and meaningless gestures). The videos used as examples were not included in the final dataset. Raters did not have any time restriction and were allowed to watch a video as many times as needed.
Data analysis Meaningfulness rating For the analysis of each video clip, we carried out the following steps: (1) We performed outlier removal by calculating the z value for each response and , object-directed actions; e. g. , putting on a ring), emblems (i. e. , conventional communicative gestures; e. g. , thumbs up), and meaningless gestures (i. e. , gestures that do not carry any meaning; e. g. , ND_35). Each video clip lasted 2. 5 s excluding ratings above or below two standard deviations from the mean rate assigned to that video clip; (2) the meaningfulness of each item was computed as the mean for all raters, except for those discarded in Step 1; (3) we also calculated the median of the meaningfulness ratings; (4) gestures with a median >5 were considered to be meaningful gestures, and gestures with a median <3 were meaningless.
Pantomime naming: actions versus objects The pantomimes in the current dataset were gestures that involved the use of an object. We observed that many raters, when asked to name a gesture, reported the name of the object instead of the name of the action. We analyzed the percentage of raters who provided a verb (denoting the action), a noun (denoting the object), or another grammatical form (adjectives, adverbs, sounds, or short sentences) in the naming task. Since the name of the object and the action are often the same in English, this additional analysis was carried out only on the data from the Italian raters. Emblem naming: events versus states The emblems in the current dataset can denote actions (e. g. , to clap) or physical/ psychological states (e. g. , thumbs up to mean agreement).
Datasets In the dataset, we report the collected norms for 89 pantomimes, 104 emblems, and 77 gestures, for Italians (see Table  S1 ) and Americans (see Table  S2 ). For each item, we provide the following information: (a) the position in the table with respect to the other items, depending on their rating (Position); (b) the English name/description of the item, as intended during the recording (Item), which is also the name under which the video appears in the database; (c) the mean and median of the meaningfulness ratings, rated on a Likert scale from 1 to 7; (d) the most frequently produced meaning; (e) the agreement on meaning, split by the percentage of raters who introduced the most frequent meaning (p), the percentage of raters who judged the gesture as being meaningless (i. e. , ratings of 1 on the meaningfulness scale) and did not provide a description (NA), and meaning agreement (H index); (f) the percentages of raters who named the gesture with a verb, with a noun, or with another grammatical form (pantomimes rated by Italians only-see the Pantomime Naming: Actions vs.
Discussion The aim of this study was to develop and make publicly available a library of video clips of symbolic gestures (pantomimes and emblems), and matched meaningless gestures. This database has been conceived to be used in a wide range of research areas addressing the processing of meaningful manual gestures, including neuroimaging studies on the neural basis of symbolic gesture recognition, research on integration of manual and symbolic aspects of communicative gestures, on the contribution of culture in shaping and understanding pantomimes and emblems, as well as on the selective impairment of some aspects of gestural production or comprehension in brain-damaged patients. This library has several advantages. The first one is its size: it provides norms for pantomimes, emblems, and meaningless gestures for a total of 270 gestures.
This library, available for non-commercial purposes via the database Figshare (https://doi. org/10. 17637/rh. c. 4219988), aims to encourage and facilitate research on symbolic gestures, but also to promote replicability of results among studies. The present study yielded a number of observations, highlighting distinctions within the broad category of symbolic gestures. First, we found higher consistency between Italians and Americans in the rating of pantomimes, as compared to the rating of emblems, although both sets of gestures were based on the Italian culture. In particular, we found that Italians and Americans provided consistent judgments for most pantomimes (the 67% of pantomimes were considered meaningful and interpreted as expected by both groups). In contrast, only the 45% of the emblems were considered meaningful and were assigned the correct (intended) meaning, consistently by both groups.
Pantomimes are object-directed actions that are not constructed on the basis of cultural conventions, but rather by reproducing the real-world referent (i. e. , the object-directed action;  van Nispen et al. , 2017). In the case of emblems, the relationship between meaning and form is culture-specific and can be hardly understood without prior contact with that culture. In our dataset, the higher agreement between Italian and American raters on the interpretation of pantomimes, relative to the interpretation of emblems confirms this distinction  (Calbris, 1990; Ekman, 1976; Ekman & Friesen, 1969; Kita, 2009; Payrató, 1993; Poggi & Zomparelli, 1987). The different susceptibilities of pantomimes and emblems to culture was first investigated from an anthropological perspective  (Efron, 1941 (Efron, , 1972;; Ekman & Friesen, 1969 , 1972; Kendon, 1992; Poggi & Zomparelli, 1987; Payrató, 1993).
Overall, our dataset emphasizes the variability of individuals' interpretations of pantomimes and-to a greater extent-of emblems. This variability is such that the same gesture (e. g. , yawning) can be named with a verb (Bto yawn^) or a noun (Bthe yawn^) or can be mapped on different meanings, on the basis of which it could fall in one gesture category or another (e. g. , the pantomime for Byawning^becomes an emblem if it is interpreted as BI am bored^). Acknowledging this variability, the present work identifies statistical regularities at the population level (one gesture is more frequently interpreted as X than Y), rather than strict categorical distinctions (one gesture is X and not Y). Finally, 83% of the meaningless gestures were consistently rated as meaningless by Italians and Americans alike, showing that this set of videos can serve as a meaningless control condition in behavioral and neuroimaging studies.
S4 ). Indeed, even IM-I fits these infectious diseases very well, which confirms the importance of the thymic involution timescale. This provides confidence in applying our approach further. Cancer Incidence. We next tested our model against cancer incidence curves, across 101 cancer types under the ICDO3 WHO2008 classification  (11). Fitting IM-II to the incidence curves, the median R 2 was found to be 0. 956, with 57 cancer types fitting very well (R 2 > 0. 95). Since IM-II has the same number of fitting parameters as the widely used PLM of cancer incidence, a direct comparison is possible. The PLM performs slightly worse overall (R 2 > 0. 95 for 48 cancer types, median R 2 = 0. 947; R 2 and associated fitting measures for each cancer type can be found in Dataset S1), with cancers whose incidence rises exponentially, such as chronic myeloid leukemia (CML) and brain cancer, fitting IM-I and IM-II better than the PLM.
Many cancer types, including colon and gallbladder, fit both the PLM and IM-II very well (Fig. 3 ). There are no examples of PLM fitting well and notably better than IM-II. The ability of IM-II to capture the power law behavior seen in cancer incidence curves is an unexpected feature of the model and is discussed further in SI Theory [where we show that IM-II exhibits an apparent power law with power e/(e -2) ∼ 3. 78 in the age range of 33-82 y]. We note that, of the top 10 best-fit cancers, the 9 carcinomas have pivot ages tightly clustered from 56. 3 to 60. 5 y (Dataset S1), suggesting a clinical significance of the mid-to late fifties as an age of particular importance for screening and intervention. In contrast, the PLM by definition is "scale-free" and thus has no associated age range of particular importance from a clinical perspective.
3E  and Fig. S9D ), each showing good data collapse. Interestingly, a similar gender bias is found in the average mutation burden in cancer biopsies, showing a steeper increase with age in males  (14). WNV, the only infectious disease in our dataset with gender separation, also shows steeper increase in risk for males (value of P < 0. 01). For the majority of cancers, both the PLM and IM-II fit very well. To investigate further, we constructed a combined power law immunological model (PLIM), which includes rising risk with age from accumulating mutations and from immune system decline. This model has three fitting parameters, and is therefore relatively weaker as a predictive model, but does contain as submodels the PLM and IM-II (and hence IM-I). The model predicts risk profiles of the following form: R = A e Be -αt -1 t γ , where A, B, and γ are fitting parameters (Methods).
Their analysis suggests that lung and colon cancer are caused by approximately n ∼ 2. 3 driver mutations, rather than n ∼ 6. 3 as would be inferred from the PLM alone. Our combined PLIM also predicts approximately n ∼ 2. 2 driver mutations for colon cancer, although good fits (R 2 > 0. 95) are also found in other areas of parameter space. Moreover, a correlation has been found between the risk of cancer in a given organ and the total number of stem cell divisions estimated for that organ  (19). It has been noted that this correlation does not show a highly nonlinear relationship, which would be expected from the mutation accumulation hypothesis  (20). Indeed, if we apply the PLM to this dataset (Dataset S2), we find that the number of driver mutations is just n ∼ 0. 91 (SI Theory) consistent with the assumptions underpinning the immunological model.
While IM-II has only two emergent fitting parameters, the underlying random-walk model has three biological parameters, resulting in an underdetermined system (Methods). To get estimates for these biological parameters, such as the size of the IET, additional assumptions are required. Given that the estimated total number of stem cell divisions provides a good predictor of cancer risk  (19) , the rate of stem cell divisions can be assumed to be proportional to the rate of cancer initiation attempts in the immunological model (SI Theory). From this, we can obtain values for the model parameters of IM-II. We found that the size of the IET is typically ∼10 6 , which would imply that a population growing beyond 10 6 cancer cells would overwhelm the immune system and result in immune escape (see Dataset S1 for values for each cancer).
All cancer incidence data are obtained from Surveillance, Epidemiology, and End Results Program (SEER) in the United States  (11). Bacterial infection incidence data are obtained from the ABC surveillance program run by the Centers for Disease Control and Prevention (CDC). This program studies seven key bacterial diseases in detail (https://www. cdc. gov/ abcs/reports-findings/surv-reports. html). Incidence data for viral diseases is obtained from studies with the largest possible sample sizes. WNV disease incidence data are obtained from a 9-y survey covering the United States from 1999 to 2008 (available at https:// www. cdc. gov/mmwr/preview/mmwrhtml/ss5902a1. htm; accessed  February 23, 2016). Influenza A incidence data are obtained from a 22-y survey covering the United States  (36). Tuberculosis prevalence in Cambodia is obtained from ref.
37. Stem cell counts and division rate estimates are taken from ref. 19. Statistical Methods. For incidence of infectious diseases and cancers, CIs are calculated assuming a χ 2 distribution. All fitting of incidence curves is performed on log-transformed values. To calculate the overall ratio of male TRECs to female TRECs, we computed the ratio of the means and then used a bootstrapping approach to calculate the SD of that measurement. To show that cancer risk rises more steeply for males compared with females, we fit pure exponentials to the incidence curves and recorded the exponents as Female alpha and Male alpha in Dataset S1. To calculate the value of P for the statement that risk rises more steeply for WNV in males compared with females, we used the ANCOVA method. All of the code for our analysis is available online at https://github.
ACKNOWLEDGMENTS. We thank  Toni Aebischer ,  Md. Al Mamun ,  Doreen Cantrell ,  Mel Greaves ,  Sarah Howie ,  Philipp Kruger ,  Dianbo Liu ,  Luke McNally ,  Jacques Miller ,  Rob Newton , and  Rose Zamoyska  for useful discussions and comments on the manuscript. This work was supported by  Scottish Universities Life Sciences Alliance  and  NIH through Physical Sciences in Oncology Centres  Grant  U54 CA143682  (to S. P. , T. J. N. , and L. A. ), the  Medical Research Council  (C. C. B. ), the  European Union Seventh Framework Programme  ( FP7/2007-2013 ) collaborative project  ThymiStem  under Grant Agreement  602587  (to C. C. B. ), and the  Instituts Thématiques Multi-Organismes Cancer  within the framework of the Plan Cancer 2014-2019 and convention  Biologie des Systèmes   BIO2014  ( COMET  project, to L.
The risk of contracting a disease, R, as a function of age, t, can be modeled by immunological models or the PLM. The model parameter α is given by 0. 044 y -1 , while the free parameters are obtained by fitting the models to each disease and are available in Dataset S1. 1884 | www. pnas. org/cgi/doi/10. 1073/pnas. 1714478115 Palmer et al.
To identify which parts need human inspection, one needs tools that are able to detect both recurring patterns and sparsely distributed events. Identifying recurring patterns allows the user to focus on certain time points for manual annotation, while detection of more rare structures enables discovery of unforeseen phenomena. With this aim, we need to design an algorithm for acoustic similarity retrieval, where the audio fragments judged "most similar" to a given query recording must be extracted from some larger dataset. To construct such an algorithm, we are required to represent an audio recording in a way that captures its distinctive qualities. A widespread choice of representation is the bag of frames  [9] , which describes an auditory scene recording using summary statistics of short-time features.
A richer representation, the scattering transform, has enjoyed significant success in various audio  [15]  and biomedical  [16]  signal classification tasks. Its structure is that of a convolutional neural network  [17] [18] [19] [20] , but with fixed filters. Specifically, it alternates convolutions with wavelet filters and pointwise nonlinearities to ensure timeshift invariance and time-warping stability  [21]. For our task, one advantage of the scattering transform is that it does not require a training step, allowing for a wider range of applications compared to learned features. Indeed, for data mining of previously unheard datasets, the properties of relevant audio structures remains to be defined, leading to an unsupervised setting. In this work, we propose a new model for acoustic scenes, where the signal is represented at sub-second scales by scattering transforms, while larger scales are captured by a cluster model.