dataesr/scientific-paragraphs-categorization huggingface dataset:
7d ). We compared three estimators: (i) posterior means of σ(ρ j ) using VB; (ii) posterior means σ(ρ j ) using MCMC; and (iii) sample accuracies, i. e. , individual maximumlikelihood estimates. The figure shows that posterior estimates based on a mixed-effects model led to a slightly smaller estimation error than sample accuracies. This effect was small in this scenario but became substantial when considering a smaller dataset, as described next. In the second setting (Fig. 7 , bottom row), we carried out the same analyses as above, but based on small datasets of just 8 subjects with different numbers of trials (Fig. 7e ). Regarding test specificity, as before, we found fixed-effects inference to yield highly overoptimistic inferences at low test sizes (Fig. 7f ). The same picture emerged when considering sensitivities (Fig.
Accuracies versus balanced accuracies As described above, the classification accuracy of an algorithm (obtained on an independent test set or through cross-validation) can be a misleading measure of generalization ability when the underlying data are not perfectly balanced. To resolve this problem, we use a straightforward extension of our model, the twofold normal-binomial model (Fig. 4 ), that enables inference on balanced accuracies. To illustrate the differences between the two quantities, we revisited, using our new VB algorithm, an analysis from a previous study in which we had generated an imbalanced synthetic dataset and used a linear support vector machine (SVM) for classification (Fig. 8 ; for details, see  Brodersen et al. , 2012a). We observed that, as expected, the class imbalance caused the classifier to acquire a bias in favor of the majority class.
5. By contrast, the balanced accuracy interval provides a sharply peaked estimate of the true balanced accuracy; its baseline is 0. 5. were indicated by button press (left/right index finger). Details on the underlying experimental design, data acquisition, and preprocessing can be found elsewhere  (Behrens et al. , 2007). Here, we aimed to decode (i. e. , classify) from fMRI measurements which option had been chosen on each trial. Because different choices were associated with different buttons, we expected to find highly discriminative activity in the primary motor cortex. Separately for each subject, a general linear model  (Friston et al. , 1995)  was used to create a set of parameter images representing trial-specific estimates of evoked brain activity in each volume element. These images entered a linear support vector machine (SVM), as implemented by  Chang and Lin (2011) , that was trained and tested using 5-fold cross-validation.
Neither is generally superior to the other; they simply represent (minimally) different assumptions about the distribution of classifier performance across subjects. However, the logit-normal density is a more natural candidate distribution when deriving a Laplace approximation, as we do here, since it implies closed-form, interpretable update equations and since it enables a straightforward approximation to the free energy (cf. Theory section). Should the issue which distribution is optimal for a given dataset at hand become a question of interest for a particular application, one can weigh the evidence for different parameterizations by means of Bayesian model selection, using the code provided in our toolbox, as shown in  Brodersen et al. (2012a). In addition to their excessive runtime, MCMC approaches to model inversion come with a range of practical challenges, such as: how to select the number of required samples; how to check for convergence, or even guarantee it; how long to design the burn-in period; how to choose the proposal distribution in Metropolis steps; how many chains to run in parallel; and how to design overdispersed initial parameter densities.
The balanced accuracy is not confined to binary classification; it readily generalizes to K classes. Specifically, the twofold normal-binomial model becomes a K-fold model, and the balanced accuracy φ ¼ 1 K ∑π k ð Þ is computed on the basis of a convolution of K random variables (cf. Eq. (  54 )). 11 Infraliminal probabilities are then determined w. r. t. the baseline level 1/K. Even more generally, in those applications where one wishes to distinguish between different types of error (as, for example, in differential diagnostics where different misclassifications carry different costs), one could consider a weighted average of class-specific accuracies. At first glance, another solution to dealing with imbalanced datasets would be to stick with the conventional accuracy but relate it to the correct baseline performance, i.
e. , the relative frequency of the majority class, rather than, e. g. , 0. 5 in the case of binary classification. The main weakness of this solution is that each and every report of classification performance would have to include an explicit baseline level, which would make the comparison of accuracies across studies, datasets, or classifiers slightly tedious. Future extensions of the approach presented in this paper might include functional performance measures such as the receiver-operating characteristic (ROC) or the precision-recall curve  (Brodersen et al. , 2010b). Leaving classification studies aside for a moment, it is instructive to remember that mixed-effects inference and Bayesian estimation approaches have been successfully employed in other domains of neuroimaging data analysis (Fig.
Acknowledgments The authors wish to thank  Tom Nichols ,  Tim Behrens ,  Mark Woolrich ,  Adrian Groves ,  Ged Ridgway , and  Falk Lieder  for insightful discussions, and additionally  Tim Behrens  for sharing fMRI data. This research was supported by the  University Research Priority Program 'Foundations of Human Social Behaviour'  at the  University of Zurich (KHB, KES), the SystemsX. chproject 'Neurochoice' (KHB, KES) , and the  René and Susanne Braginsky Foundation (KES).
TECHNOLOGIES INVOLVED There are several technologies that can be used to implement the concept of Internet of Things. In this paper, we discussed the following technologies:  RFID system is composed of one or more reader(s) and several RFID tags. Tags are characterized by a specific address and are applied to objects. Tags uses radio-frequency electromagnetic fields to transfer data attached to an object. The tags contained electronically stored information which can be read by the RFID reader when the object came in the proximity of the reader  (WIKIPEDIA, 2013). RFID allows to monitor objects in real-time, without the need of being in line-of-sight. • Radio Frequency Identification (RFID) • Near Field Communication (NFC) • Machine-to-Machine Communication (M2M) • Vehicle-to-Vehicle Communication (V2V) From the physical point of view RFID tag or label is a tiny microchip combined with an antenna in a compact package.
Rather like a URL, the entire electronic product code number can be used as a key into a global database to exclusively identify a particular product  (BURLINGTON, 2009). The RFID tags are used in many applications like Monitoring the life cycle of a product, manage the inventory in the warehouse, tracking of goods, tracking of animals, airport baggage tracking logistics  (HARRISON, 2009) , mobile payment, etc. We can combine the RFID technology with the other technologies like sensing technology to open a new horizon for new applications.
The previous experiments provide insight into real-time sentence processing in the model, but with a rather limited data set. The question immediately arises as to whether the system can accommodate a more extended set of constructions. Here we attempt to determine the performance with an extended corpus. Using a context free grammar we systematically generated a set of 462 novel grammatical constructions (see Methods), each expressing meanings consisting of between 1 and 6 nouns, with 1 to 2 levels of hierarchical structure (i. e. with only a main clause, or a main and relative clause, and 1 or 2 verbs, respectively). Each grammatical construction is a mapping between a unique surface form and a unique coded meaning. An exact mapping between inputs and outputs is thus possible. Using a model with 1000 neurons, the system learned the full ''462 corpus'' set perfectly (with no error when using the full corpus both as training and testing set) for sentence final learning (i.
Training During training, inputs were presented to the reservoir, and the reservoir internal state trajectory was recorded. This data and the desired output was then used in a ridge regression (also known as Tikhonov regularization) to train the readout weights so as to minimize the mean squared error between the target output and the projection of the reservoir state through the trained readout weights. We used ridge regression rather than classic linear regression, as it effectively limits the magnitude of the weights, thus reducing the probability of overfitting and thus enhancing the generalization capabilities. See  [38]  for a review of reservoir learning methods. We considered training conditions in which learning occurred starting at the presentation of the first word until the end of the sentence (continuous learning), or only at the end of the sentence (sentence final learning).
Experiment-Specific Simulation Conditions and Parameters Python code for Experiments 1-4 can be found in the Zipped Archive S1. The Text S3 has instructions on how to install and run the model. Below are details on specific parameters for the different experiments. Experiment 1: Basic Syntactic Comprehension. Learning and on-line processing with a set of 26 constructions: constructions numbered 15-40 in the Text S1 (see results in Figure  2 ). The reservoir was composed of 300 internal units. For Experiments 1-4 semantic words were coded with two separate input neurons for nouns (N) and verbs (V). Meaning was evaluated only for nouns, in assigning the thematic roles of agent, object and recipient (see Input and Output Coding). Experiment 2: Neural Coding Explanation of P600. Online processing of subject-and object-relatives with different training corpora.
DERIVING AUDITORY CLASSIFICATION IMAGES Each stimulus noise n i is characterized by its power spectrogram, whose components are entered as predictor variables in the model. Power spectrograms were calculated with Matlab function spectrogram, using a Short-Time Fourier Transform with a moving 512 points Hamming window and no overlap, resulting in 86. 13 Hz frequency resolution and 11. 6 ms temporal resolution. Since the last 340 ms of the signal were almost silent, we limited our analysis to a time range of 0-0. 34 s and a frequency range of 0-4048 Hz, thus ensuring that the size of the data-set would not exceed computational limits. The resulting 46-by-30 matrix (frequency bins by time bins) is reshaped into a 1380-by-1 vector of time-frequency bins, labeled X i. A similar treatment is applied to both targets, resulting in vectorized power spectrograms T 0 and T 1 (Figure  1 ).
Thus, calculating: θ ML = argmax θ L θ (6) by a standard maximization algorithm (e. g. , the built-in Matlab function glmfit) would provide us maximum likelihood estimates of the CIm, β and of the stimulus factor s. Unfortunately, these estimates would be presumably too noisy to be decipherable. Indeed GLMs, as well as reverse-correlation, when comprising a large number of predictors (1382 in this example), are prone to overfitting, which means that the model will describe the trial-dependent noise as well as the underlying classification mechanism. Estimates of the observer's template by GLM can therefore be quite noisy, and the model will not be able to generalize to novel data. For proper predictions of previously unseen data, templates should not be determined by the specific distribution of noise in trials used to fit the model, but rather reflect the decision process of the observer.
One solution has been developed in the GLM framework under the name "Penalized Likelihood," which has been widely used for estimating the receptive fields of single neurons  (Wu et al. , 2006; Calabrese et al. , 2011; Park and Pillow, 2011)  and adapted to CIm by  Knoblauch and Maloney (2008)  and later by  Mineault et al. (2009). Another example of using a similar method for an application in the auditory domain can be found in  Schönfelder and Wichmann (2012) , who modeled results from a classical auditory tone-in-noise detection task using this approach. Among the various R or Matlab toolboxes available, we decided to use Mineault's function glmfitqp (http://www. mathworks. com/matlabcentral/ fileexchange/31661-fit-glm-with-quadratic-penalty) that allows optimizing a GLM with quadratic penalty.
The optimal estimate is a tradeoff between fitting the data well and satisfying the constraints of the penalty term. Therefore, a prior on smoothness will favor CIm with slow variations in time and frequency (but note that other types of priors exist  (Wu et al. , 2006) , e. g. , implying assumptions on independence  (Machens et al. , 2004) , sparseness  (Mineault et al. , 2009; Schönfelder and Wichmann, 2012) , or locality  (Park and Pillow, 2011)  of the model parameters). In agreement with the Matlab function we use, we chose our smoothness prior to be a sum of two quadratic forms: P θ|λ 1 , λ 2 = λ 1 θ T L 1 θ + λ 2 θ T L 2 θ (9) where L 1 is the Laplacian matrix along dimension 1 (time), L 2 the Laplacian matrix along dimension 2 (frequency) in the timefrequency space  (Wu et al. , 2006).
g. , in  Wu et al. , 2006; Schönfelder and Wichmann, 2012). This approach involves a partition of the data between a "training" and a "test" set. For each given couple of hyperparameters, we can estimate the model parameters on the training-set by maximum a posteriori, as explained previously. It thus becomes possible to assess how the model parameters would generalize to an independent dataset, by comparing the predicted responses on the test-set to the actual responses. When the model predicts the most accurately unseen data, the strength of priors is considered as optimal. We determined one single couple of optimal hyperparameters λ 1, opt , λ 2, opt by participant. More precisely, the selection of λ 1,opt and λ 2,opt is performed on a model gathering together trials on which signal 0 or 1 was presented, following the equation: P (r i = 1) = (X i T • β + b) (10) This GLM relates strongly to that derived from Equation (  4 ), except that it does not take into account information about the target signals that was actually presented at each trial (the two level factor s being replaced with a constant term b).
Nevertheless, it allows us to track how the calculated template generalizes to new data sets, excluding predictors other than noise. For low values of hyperparameters, the model is overfitted and cannot generalize to the "test" dataset, resulting in prediction performances around chance level (50%). For high values of hyperparameters, the classification image is flat and the model always gives the same answer, which corresponds to the response bias of the observer (in this case 52% of Michel Hoen's answers were "aba"). In between a couple of hyperparameters may be found that maximizes prediction performances. Figure  4A  shows the CIm β obtained by the GLM method with smoothness priors, as well as the optimal values of λ 1 and λ 2 for the three listeners. For each participants, the classification image provides a measure of the strength of the relation between the noise at different time-frequency locations and the speech identification scores.
Note that in our experiment the amount of phase uncertainty is reduced by the presentation of a signal in both conditions. We additionally assumed here that non-linearities in the auditory system may be locally approximated by a linear function within the SNR range studied, a hypothesis supported by the local linearity of psychometric functions  (Abbey and Eckstein, 2006). To explore this assumption empirically, we estimated the model parameters for all participants by taking into account only trials with signal contrast in the linear part of their psychometric function. The resulting CIm are very similar to those obtained previously on the whole dataset. Furthermore, even if higher-order computations are involved, the actual mechanisms of phoneme categorization are very likely to rely on time-frequency regions highlighted by our CIm because, to some extent, noise in these regions predicts the response of the participant.
ADAPTIVE SNR Another related theoretical issue of interest here relates to the use of an adaptive-SNR method. When gathering together data from the whole experiment in order to calculate a classification image, we assume that the observer's strategy for phoneme categorization in noise does not change drastically with SNR. However, this is somewhat unlikely as a number of neurophysiological studies have highlighted significant changes in cortical activity with the level of acoustic degradation of speech sounds  (Obleser et al. , 2008; Miettinen et al. , 2010; Obleser and Kotz, 2011; Wild et al. , 2012). This led us to investigate the effect of SNR on the classification image, by estimating the model parameters only on the half of the dataset with the lowest SNR (from min to median for each participant) and on the half of the dataset with the lowest SNR (from median to max) separately (Figure  8 ).
Proposed Approach In order to solve the above described problem, we propose a multi-agent model as developed by Zidi  [46]  to which we were add some features that we suspect necessary for the proper functioning of the system to adequately address the problem. Our model as described in Figure  1  was composed by a "System Agent" and a set of "Zone Subsystems". The "System Agent" has to create and supervise all other agents. Each "Zone Subsystems" was composed of a "Planning Subsystem", an "Area Manager Agent", "Information Manager Agent", "Forecast Agent", "Disturbance Agent", and a local database. The "Zone Subsystem" was managed by the "Area Manager Agent" which provides communication with the "System Agent" and other "Zone Subsystems". The "Information Manager Agent" was responsible for the management of information necessary for the proper conduct of the rescue operation.
In fact it provides information collection, their filtration, and updates the local database. The "Forecast Agent" allowed forecasting of emergency calls based on the information received and based on historical relief interventions. The "Disturbance Agent" will be used to detect disturbances that can occur, transmit "Planning Subsystem" to be processed. The "Planning Subsystem" was responsible for emergency planning, integration of new applications and correction planning in case of a disturbance. The "Planning Subsystem" was composed of three types of agents namely "Supervisor Agent" who supervises the other components in addition to its roles in the "Planning Subsystem". The role of "Vehicle Agent" is finding a path for a vehicle. The "Interface Agent" handles communication with the external environment.
Introduction This article considers the image classification problem: given an image, we wish to annotate it with one or multiple keywords corresponding to different semantic classes. We are especially interested in the large-scale setting where one has to deal with a large number of images and classes. Largescale image classification is a problem which has received an increasing amount of attention over the past few years as larger labeled images datasets have become available to the research community. For instance, as of today, ImageNet 1  consists of more than 14M images of 22K concepts  (Deng et al, 2009)  and Flickr contains thousands of groups 2  -some of which with hundreds of thousands of pictures -which can be exploited to learn object classifiers  (Perronnin et al, 2010c , Wang et al, 2009).
The use of many channels and non-linear SVMswhose training cost scales somewhere between quadratically and cubically in the number of training samples -was made possible by the modest size of the available databases. In recent years only the computational cost has become a central issue in image classification and object detection. Maji et al (2008)  showed that the runtime cost of an intersection kernel (IK) SVM could be made independent of the number of support vectors with a negligible performance degradation. Maji and Berg (2009)  and  Wang et al (2009)  then proposed efficient algorithms to learn IKSVMs in a time linear in the number of training samples. Vedaldi and Zisserman (2010)  and  Perronnin et al (2010b)  subsequently generalized this principle to any additive classifier. Attempts have been made also to go beyond additive classifiers  (Perronnin et al, 2010b , Sreekanth et al, 2010).
We address this problem using Product Quantization (PQ)  (Gray and Neuhoff, 1998)  which has been popularized in the computer vision field by  Jégou et al (2011)  for large-scale nearest neighbor search. We show theoretically why such a compression scheme makes sense when learning linear classifiers. We also show experimentally that FVs can be compressed by a factor of at least 32 with only very limited impact on the classification accuracy. The remainder of this article is organized as follows. In Section 2, we introduce the FK principle and describe its application to images. We also introduce a set of normalization steps which greatly improve the classification performance of the FV. Finally, we relate the FV to several recent patch encoding methods and kernels on sets. In Section 3, we provide a first set of experimental results on three smalland medium-scale datasets -PASCAL VOC 2007  (Everingham et al, 2007) , Caltech 256  (Griffin et al, 2007)  and SUN 397  (Xiao et al, 2010)  -showing that the FV outperforms significantly the BoV.
In Section 4, we present PQ compression, explain how it can be combined with largescale SGD learning and provide a theoretical analysis of why such a compression algorithm makes sense when learning a linear classifier. In Section 5, we present results on two large datasets, namely ILSVRC 2010 (Berg et al, 2010) (1K classes and approx. 1. 4M images) and ImageNet10K  (Deng et al, 2010) (approx. 10K classes and 9M images). Finally, we present our conclusions in Section 6. This paper extends our previous work  (Perronnin and Dance, 2007 , Perronnin et al, 2010c , Sánchez and Perronnin, 2011)  with: (1) a more detailed description of the FK framework and especially of the computation of the Fisher information matrix, (2) a more detailed analysis of the recent related work, (3) a detailed experimental validation of the proposed normalizations of the FV, (4) more experiments on several small-medium-scale datasets with state-of-theart results, (5) a theoretical analysis of PQ compression for linear classifier learning and (6) more detailed experiments on large-scale image classification with, especially, a comparison to k-NN classification.
PASCAL VOC 2007 We first report a set of detailed experiments on PASCAL VOC 2007  (Everingham et al, 2007). Indeed, VOC 2007 is small enough (20 classes and approximately 10K images) to enable running a large number of experiments in a reasonable amount of time but challenging enough (as shown in  (Torralba and Efros, 2011) ) so that the conclusions we draw from our experiments extrapolate to other (equally challenging) datasets. We use the standard protocol which consists in training and validating on the "train" and "val" sets and testing on the "test" set. We measure accuracy using the standard measure on this dataset which is the interpolated Average Precision (AP). We report the average over 20 categories (mean AP or mAP) in %. In the following experiments, we use a GMM with 256 Gaussians, which results in 128K-dim FVs, unless otherwise specified.