Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving historical spelling normalization with bi-directional LSTMs and multi-task learning

Natural-language processing of historical documents is complicated by the abundance of variant spellings and lack of annotated data. A common approach is to normalize the spelling of historical words to modern forms. We explore the suitability of a deep neural network architecture for this task, particularly a deep bi-LSTM network applied on a character level. Our model compares well to previously established normalization algorithms when evaluated on a diverse set of texts from Early New High German. We show that multi-task learning with additional normalization data can improve our model’s performance further.
Understanding Deep Features with Computer-Generated Imagery

We introduce an approach for analyzing the variation of features generated by convolutional neural networks (CNNs) trained on large image datasets with respect to scene factors that occur in natural images. Such factors may include object style, 3D viewpoint, color, and scene lighting configuration. Our approach analyzes CNN feature responses with respect to different scene factors by controlling for them via rendering using a large database of 3D CAD models. The rendered images are presented to a trained CNN and responses for different layers are studied with respect to the input scene factors. We perform a linear decomposition of the responses based on knowledge of the input scene factors and analyze the resulting components. In particular, we quantify their relative importance in the CNN responses and visualize them using principal component analysis. We show qualitative and quantitative results of our study on three trained CNNs: AlexNet [18], Places [43], and Oxford VGG [8]. We observe important differences across the different networks and CNN layers with respect to different scene factors and object categories. Finally, we demonstrate that our analysis based on computer-generated imagery translates to the network representation of natural images.
Human body measurement method

The present invention relates to the generation methods of the 3 d body data of object.Method includes the following steps: the first or more source images of object are captured using digital imaging apparatus.Using one or more dividing methods, heuristics and/or pre-determined mapping, one or more source images of the object are divided into multiple sections or probability distribution.The result of every kind of dividing method is combined to generate the unique probability graphs of one or more for representing the object and then unique probability graph compares with the database that 3 D human body represents to determine unique probability graph or each unique probability graph and from the closest match or optimum mapping between representative determining in database.Then the 3 d body data of the object and/or measured value are generated and are potentially based on the optimum mapping and exported.
Speech recognition challenge in the wild: Arabic MGB-3

This paper describes the Arabic MGB-3 Challenge — Arabic Speech Recognition in the Wild. Unlike last year's Arabic MGB-2 Challenge, for which the recognition task was based on more than 1,200 hours broadcast TV news recordings from Aljazeera Arabic TV programs, MGB-3 emphasises dialectal Arabic using a multi-genre collection of Egyptian YouTube videos. Seven genres were used for the data collection: comedy, cooking, family/kids, fashion, drama, sports, and science (TEDx). A total of 16 hours of videos, split evenly across the different genres, were divided into adaptation, development and evaluation data sets. The Arabic MGB-Challenge comprised two tasks: A) Speech transcription, evaluated on the MGB-3 test set, along with the 10 hour MGB-2 test set to report progress on the MGB-2 evaluation; B) Arabic dialect identification, introduced this year in order to distinguish between four major Arabic dialects — Egyptian, Levantine, North African, Gulf, as well as Modern Standard Arabic. Two hours of audio per dialect were released for development and a further two hours were used for evaluation. For dialect identification, both lexical features and i-vector bottleneck features were shared with participants in addition to the raw audio recordings. Overall, thirteen teams submitted ten systems to the challenge. We outline the approaches adopted in each system, and summarise the evaluation results.
Long-term Excess Mortality in Pediatric Patients With Cerebral Aneurysms

Background and Purpose—Knowledge of the long-term excess mortality in pediatric aneurysm patients is lacking. The aim of this study was to assess the long-term excess mortality of 102 pediatric patients with cerebral aneurysm treated at the department of neurosurgery at Helsinki University Central Hospital between 1937 and 2009. Methods—Patients were followed from diagnosis until death or the end of the year 2010. Relative survival ratio provided the measure of excess mortality in these patients compared with mortality of the general Finnish population matched by age, sex, and calendar time. Results—A majority of the patients (n=89) presented with subarachnoid hemorrhage. Aneurysms (n=118) were treated operatively (n=79), endovascularly (n=1), or conservatively (n=36). The mean follow-up time was 26.8 years (range, 0–55.6 years). By the end of follow-up, 34 of the 102 patients had died; 26 of these deaths (76%) were aneurysm-related. There was overall excess mortality of 10% (cumulative relative survival .
Low Genetic Differentiation in the Populations of the Malabar Carp Labeo dussumieri as revealed by Allozymes, Microsatellites and RAPD

The population structure of Labeo dussumieri, an endangered and endemic cyprinid from three riverine locations in the Western Ghats, India was investigated using allozyme, microsatellite and RAPD markers. L. dussumieri samples were obtained from Meenachil, Manimala and Pamba River basins, Kerala. Fourteen (46.7%) out of 30 allozyme loci, seven microsatellite loci and 12 RAPD Operon decamers gave polymorphic pattern. Six allozyme loci (AAT-2*, EST-4*, GLDH*, GPI-2*, G6PDH* and LDH-2*) and three microsatellite loci (LdussG1, MFW19 and Bgon22) exhibited consistent significant deviation from Hardy-Weinberg Equilibrium expectations in different populations after probability level (P<0.05) was adjusted for sequential Bonferroni correction. All the three marker types demonstrated concordant results and various estimates revealed genetic variability within the subpopulations but surprisingly low level (= 0.0034 to 0.0081) of genetic differentiation among L. dussumieri from different river samples. AMOVA analysis also indicated low differentiation among subpopulations. No evidence for a recent genetic bottleneck was observed in L. dussumieri populations based on allozyme and microsatellite data set analysis. Meenachil, Manimala and Pamba Rivers open in to the southern end of Vembanad Lake in Kerala and are connected to each other in the lower reaches through an extensive network of natural canals. Common ancestry in the prehistoric period; and possible mixing of fish populations resulting in high gene flow across the rivers through the lake and interconnecting canals could have been responsible for the lack of significant allelic heterogeneity among the L. dussumieri populations. The stocks from the three rivers do not require different management strategies and for propagation assisted river ranching programme of this species, large effective breeding population can be developed by mixing individuals from three rivers
Unsupervised Object Discovery and Tracking in Video Collections

This paper addresses the problem of automatically localizing dominant objects as spatio-temporal tubes in a noisy collection of videos with minimal or even no supervision. We formulate the problem as a combination of two complementary processes: discovery and tracking. The first one establishes correspondences between prominent regions across videos, and the second one associates similar object regions within the same video. Interestingly, our algorithm also discovers the implicit topology of frames associated with instances of the same object class across different videos, a role normally left to supervisory information in the form of class labels in conventional image and video understanding methods. Indeed, as demonstrated by our experiments, our method can handle video collections featuring multiple object classes, and substantially outperforms the state of the art in colocalization, even though it tackles a broader problem with much less supervision.
Sound event detection using spatial features and convolutional recurrent neural network

This paper proposes to use low-level spatial features extracted from multichannel audio for sound event detection. We extend the convolutional recurrent neural network to handle more than one type of these multichannel features by learning from each of them separately in the initial stages. We show that instead of concatenating the features of each channel into a single feature vector the network learns sound events in multichannel audio better when they are presented as separate layers of a volume. Using the proposed spatial features over monaural features on the same network gives an absolute F-score improvement of 6.1% on the publicly available TUT-SED 2016 dataset and 2.7% on the TUT-SED 2009 dataset that is fifteen times larger.
Informative data projections: a framework and two examples

Methods for Projection Pursuit aim to facilitate the visual exploration of high-dimensional data by identifying interesting low-dimensional projections. A major challenge is the design of a suitable quality metric of projections, commonly referred to as the projection index, to be maximized by the Projection Pursuit algorithm. In this paper, we introduce a new information-theoretic strategy for tackling this problem, based on quantifying the amount of information the projection conveys to a user given their prior beliefs about the data. The resulting projection index is a subjective quantity, explicitly dependent on the intended user. As a useful illustration, we developed this idea for two particular kinds of prior beliefs. The first kind leads to PCA (Principal Component Analysis), shining new light on when PCA is (not) appropriate. The second kind leads to a novel projection index, the maximization of which can be regarded as a robust variant of PCA. We show how this projection index, though non-convex, can be effectively maximized using a modified power method as well as using a semidefinite programming relaxation. The usefulness of this new projection index is demonstrated in comparative empirical experiments against PCA and a popular Projection Pursuit method.
Residual Conv-Deconv Grid Network for Semantic Segmentation

This paper presents GridNet, a new Convolutional Neural Network (CNN) architecture for semantic image segmentation (full scene labelling). Classical neural networks are implemented as one stream from the input to the output with subsampling operators applied in the stream in order to reduce the feature maps size and to increase the receptive field for the final prediction. However, for semantic image segmentation, where the task consists in providing a semantic class to each pixel of an image, feature maps reduction is harmful because it leads to a resolution loss in the output prediction. To tackle this problem, our GridNet follows a grid pattern allowing multiple interconnected streams to work at different resolutions. We show that our network generalizes many well known networks such as conv-deconv, residual or U-Net networks. GridNet is trained from scratch and achieves competitive results on the Cityscapes dataset.
Bayesian Joint Modelling for Object Localisation in Weakly Labelled Images

We address the problem of localisation of objects as bounding boxes in images and videos with weak labels. This weakly supervised object localisation problem has been tackled in the past using discriminative models where each object class is localised independently from other classes. In this paper, a novel framework based on Bayesian joint topic modelling is proposed, which differs significantly from the existing ones in that: (1) All foreground object classes are modelled jointly in a single generative model that encodes multiple object co-existence so that “explaining away” inference can resolve ambiguity and lead to better learning and localisation. (2) Image backgrounds are shared across classes to better learn varying surroundings and “push out” objects of interest. (3) Our model can be learned with a mixture of weakly labelled and unlabelled data, allowing the large volume of unlabelled images on the Internet to be exploited for learning. Moreover, the Bayesian formulation enables the exploitation of various types of prior knowledge to compensate for the limited supervision offered by weakly labelled data, as well as Bayesian domain adaptation for transfer learning. Extensive experiments on the PASCAL VOC, ImageNet and YouTube-Object videos datasets demonstrate the effectiveness of our Bayesian joint model for weakly supervised object localisation.
Learning multiple visual domains with residual adapters

There is a growing interest in learning data representations that work well for many different types of problems and data. In this paper, we look in particular at the task of learning a single visual representation that can be successfully utilized in the analysis of very different types of images, from dog breeds to stop signs and digits. Inspired by recent work on learning networks that predict the parameters of another, we develop a tunable deep network architecture that, by means of adapter residual modules, can be steered on the fly to diverse visual domains. Our method achieves a high degree of parameter sharing while maintaining or even improving the accuracy of domain-specific representations. We also introduce the Visual Decathlon Challenge, a benchmark that evaluates the ability of representations to capture simultaneously ten very different visual domains and measures their ability to recognize well uniformly.
Transactions on Pattern Analysis and Machine Intelligence 1 Action Recognition with Dynamic Image Networks

—We introduce the concept of dynamic image, a novel compact representation of videos useful for video analysis especially when convolutional neural networks (CNNs) are used. The dynamic image is based on the rank pooling concept and is obtained through the parameters of a ranking machine that encodes the temporal evolution of the frames of the video. Dynamic images are obtained by directly applying rank pooling on the raw image pixels of a video producing a single RGB image per video. This idea is simple but powerful as it enables the use of existing CNN models directly on video data with fine-tuning. We present an efficient and effective approximate rank pooling operator, speeding it up orders of magnitude compared to rank pooling. Our new approximate rank pooling CNN layer allows us to generalize dynamic images to dynamic feature maps and we demonstrate the power of our new representations on standard benchmarks in action recognition achieving state-of-the-art performance.
Text Classification using KNN with different Features Selection Methods

This paper presents a fast and efficient approach for text classification using KNN for different feature selection method. Typically, this approach evaluates the performance of the system for minimum number of features required to classify the text documents. 20 Newsgroup dataset collected by Ken Lang, have been taken to check performance of the KNN classifier algorithm. The above dataset is separated into two parts viz. training set (60%) and test set (40%). The KNN classifier has been implemented against the different number of stemmed and unstemmed features for CHI (Chi-Squared Statistic), IG (Information Gain) and MI (Mutual Information). The Accuracy, Precision, Recall and F1-Score are used to test the system.
Trace semantics for polymorphic references*

We introduce a trace semantics for a call-by-value language with full polymorphism and higher-order references. This is an operational game semantics model based on a nominal interpretation of parametricity whereby polymorphic values are abstracted with special kinds of names. The use of polymorphic references leads to violations of parametricity which we counter by closely recoding the disclosure of typing information in the semantics. We prove the model sound for the full language and strengthen our result to full abstraction for a large fragment where polymorphic references obey specific inhabitation conditions.
Convolutional recurrent neural networks for music classification

We introduce a convolutional recurrent neural network (CRNN) for music tagging. CRNNs take advantage of convolutional neural networks (CNNs) for local feature extraction and recurrent neural networks for temporal summarisation of the extracted features. We compare CRNN with three CNN structures that have been used for music tagging while controlling the number of parameters with respect to their performance and training time per sample. Overall, we found that CRNNs show a strong performance with respect to the number of parameter and training time, indicating the effectiveness of its hybrid structure in music feature extraction and feature summarisation.
Technologies in the twilight zone: early lie detectors, machine learning and reformist legal realism

ABSTRACT Contemporary discussions and disagreements about the deployment of machine learning, especially in criminal justice contexts, have no foreseeable end. Developers, practitioners and regulators could however usefully look back one hundred years to the similar arguments made when polygraph machines were first introduced in the United States. While polygraph devices and machine learning operate in distinctly different ways, at their heart, they both attempt to predict something about a person based on how others have behaved. This paper, through an historical perspective, examines the development of the polygraph within the justice system – both in courts and during criminal investigations – and draws parallels to today’s discussion. It can be argued that the promotion of lie detectors supported a reforming legal realist approach, something that continues today in the debates over the deployment of machine learning where ‘public good’ aims are in play, and raises questions around how key principles of the rule of law can best be upheld. Finally, this paper will propose a number of regulatory solutions informed by the early lie detector experience.
Automated algebraic analysis of structure-preserving signature schemes

Structure-preserving signature schemes can be very useful in the construction of new cryptographic operations like blind signatures. Recently several of these schemes have been proposed. The security of signature-preserving signature schemes is still proved by hand, which can be a laborious task. One of the ways to prove security of these schemes algebraic analysis can be used. We present an approach to perform this analysis and the first tool, CheckSPS , that can do an algebraic security analysis of these schemes, using SMT solvers as backend. This can help in constructing new schemes and analyse existing schemes. Our tool can handle all the common security objectives for signature schemes, i.e. existential unforgeability and strong existential unforgeability, and all the common capabilities for adversaries, i.e. random message attacks, non-adaptive chosen message attacks and adaptive chosen message attacks. The tool is sound, so if an attack is found it is actually possible to construct a forged signature.
On the Hardness of Partially Dynamic Graph Problems and Connections to Diameter

Conditional lower bounds for dynamic graph problems has received a great deal of attention in recent years. While many results are now known for the fully-dynamic case and such bounds often imply worst-case bounds for the partially dynamic setting, it seems much more difficult to prove amortized bounds for incremental and decremental algorithms. In this paper we consider partially dynamic versions of three classic problems in graph theory. Based on popular conjectures we show that: -- No algorithm with amortized update time $O(n^{1-\varepsilon})$ exists for incremental or decremental maximum cardinality bipartite matching. This significantly improves on the $O(m^{1/2-\varepsilon})$ bound for sparse graphs of Henzinger et al. [STOC'15] and $O(n^{1/3-\varepsilon})$ bound of Kopelowitz, Pettie and Porat. Our linear bound also appears more natural. In addition, the result we present separates the node-addition model from the edge insertion model, as an algorithm with total update time $O(m\sqrt{n})$ exists for the former by Bosek et al. [FOCS'14]. -- No algorithm with amortized update time $O(m^{1-\varepsilon})$ exists for incremental or decremental maximum flow in directed and weighted sparse graphs. No such lower bound was known for partially dynamic maximum flow previously. Furthermore no algorithm with amortized update time $O(n^{1-\varepsilon})$ exists for directed and unweighted graphs or undirected and weighted graphs. -- No algorithm with amortized update time $O(n^{1/2 - \varepsilon})$ exists for incremental or decremental $(4/3-\varepsilon')$-approximating the diameter of an unweighted graph. We also show a slightly stronger bound if node additions are allowed. [. ]
Robust Joint Alignment of Multiple Versions of a Piece of Music

Large music content libraries often comprise multiple versions of a piece of music. To establish a link between different versions, automatic music alignment methods map each position in one version to a corresponding position in another version. Due to the leeway in interpreting a piece, any two versions can differ significantly, for example, in terms of local tempo, articulation, or playing style. For a given pair of versions, these differences can be significant such that even state-of-the-art methods fail to identify a correct alignment. In this paper, we present a novel method that increases the robustness for difficult to align cases. Instead of aligning only pairs of versions as done in previous methods, our method aligns multiple versions in a joint manner. This way, the alignment can be computed by comparing each version not only with one but with several versions, which stabilizes the comparison and leads to an increase in alignment robustness. Using recordings from the Mazurka Project, the alignment error for our proposed method was 14% lower on average compared to a stateof-the-art method, with significantly less outliers (standard deviation 53% lower).
Confusion Assessment Method for the Intensive Care Unit

This work was created by the NIDUS Measurement and Harmonization Core (Leaders Richard N. Jones, ScD and Dale M. Needham, MD, PhD), funded by NIA R24AG054259, and is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/. Requested citation: NIDUS-Network for Investigation of Delirium: Unifying Scientists, https://deliriumnetwork.org/ Page 1 of 2 References: Ely EW, Margolin R, Francis J, May L, Truman B, Dittus R, Speroff T, Gautam S, Bernard GR, Inouye SK. Evaluation of Delirium in Critically Ill Patients: Validation of the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU). Crit Care Med. 2001 Jul;29(7):1370-9. [COSMIN reference]