Miracle-dz/UniFAR-FTUs huggingface dataset:
Frontostriatal Connectivity During Reward Anticipation: A Neurobiological Mechanism Cutting Across Alcohol Use Disorder and Depression?

Neurobiological research indicates that altered reward processing is among the most promising risk mechanisms in alcohol use disorder and depression. To elucidate differences and similarities between both disorders, we investigated clinical patients and at-risk individuals in two studies using a functional magnetic resonance imaging (fMRI) monetary reward paradigm. In the first study, alcohol use disorder patients compared to depressed and healthy individuals showed increased activation of the ventral striatum during reward anticipation. In contrast, both patient groups showed reduced frontostriatal connectivity compared to controls. In the second study, at-risk comorbid individuals showed decreased activation in the dorsal striatum along with decreased frontostriatal connectivity. While the connectivity results replicate the common pattern found for the patient groups, the activation results indicate a more depression-related pattern in individuals prone to developing both disorders. In conclusion, frontostriatal connectivity might be a promising transdiagnostic marker for depression, alcohol use disorder, and their comorbidity.
Improving Statistical Machine Translation in the Medical Domain using the Unified Medical Language system

Texts from the medical domain are an important task for natural language processing. This paper investigates the usefulness of a large medical database (the Unified Medical Language System) for the translation of dialogues between doctors and patients using a statistical machine translation system. We are able to show that the extraction of a large dictionary and the usage of semantic type information to generalize the training data significantly improves the translation performance.
Decoding of movement characteristics for Brain Computer Interfaces application

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Image Denoising by Pointwise Thresholding of the Undecimated Wavelet Coefficients: A Global Sure Optimum

We devise a new undecimated wavelet thresholding for denoising images corrupted by additive Gaussian white noise. The first key point of our approach is the use of a linearly parameterized pointwise thresholding function. The second key point consists in optimizing the parameters globally by minimizing Stein's unbiased MSE estimate (SURE) directly in the image-domain, and not separately in the wavelet subbands. Amazingly, our method gives similar results to the best state-of-the-art algorithms, despite using only a simple pointwise thresholding function; we demonstrate it in simulations over a wide range of noise levels for a representative set of standard grayscale images.
A novel technique for misalignment parameter estimation in double compressed JPEG images

The Double JPEG problem and its analysis has been attracting research attention since it is a common way of introducing tamper in digital images. Most Double JPEG based forensic methods assume aligned second compression cycle which is referred as the A-DJPEG scenario. However, when copy-move forgery occurs, the second compression cycle is not always aligned w.r. t the first compression cycle. In such cases the misalignment factor in the horizontal and vertical directions need to be estimated which will be very useful for further blocks in the forensic analysis pipeline. This paper presents a novel technique for estimating the degree of misalignment between the first DCT compression grid and the second DCT compression grid with the motivation that such parameters estimation can be utilized for image forensic applications. The proposed technique is compared with an existing state-of-art technique for misalignment estimation utilizing different grid combinations and for several images from the UCID database in order to validate the superiority of the proposed technique.
A study of hippocampal structure‐function relations along the septo‐temporal axis

This study examined structural–functional differences along the septo‐temporal axis of hippocampus using radial‐maze tasks that involved two different memory processes [reference memory (RM) and working memory (WM)], and the use of two kinds of information (spatial vs. nonspatial cue learning). In addition, retention of the nonspatial cue task was tested nine weeks following completion of acquisition, and the rats then underwent discrimination reversal training. Ibotenic acid lesions limited to either the dorsal pole, intermediate area, or ventral pole had minimal effects on acquisition of the complex place and cue discrimination tasks. The one exception was that rats with lesions confined to the dorsal third of hippocampus made more WM errors on the spatial task (but not the cue task) early in training. Selective lesions of the three hippocampal regions had no effects on either long‐term retention or reversal of the nonspatial cue discrimination task. In contrast, rats that had all of the hippocampus removed were severely impaired in learning the spatial task, making many RM and WM errors, whereas on the nonspatial cue task, the impairment was limited to WM errors. Further analysis of the WM errors made in acquisition showed that rats with complete lesions were significantly more likely on both the spatial and nonspatial cue tasks to reenter arms that had been baited and visited on that trial compared to arms that had not been baited. A similar pattern of errors emerged for complete hippocampal lesioned rats during reversal discrimination. This pattern of errors suggests that in addition to an impairment in handling spatial information, complete removal of hippocampus also interferes with the ability to inhibit responding to cues that signal reward under some conditions but not under others. The finding that selective lesions limited to the intermediate zone of the hippocampus produce no impairment in either WM (“rapid place learning”) or RM in our radial maze tasks serve to limit the generality of the conclusion of Bast et al. (Bast et al. ( 2009 ) PLos Biol 7:730‐746) that the intermediate area is needed for behavioral performance based on rapid learning about spatial cues. © 2011 Wiley Periodicals, Inc.
Storybase: Towards Building a Knowledge Base for News Events

To better organize and understand online news information, we propose Storybase 1 , a knowledge base for news events that builds upon Wikipedia current events and daily Web news. It first constructs stories and their timelines based on Wikipedia current events and then detects and links daily news to enrich those Wikipedia stories with more comprehensive events. We encode events and develop efficient event clustering and chaining techniques in an event space. We demonstrate Storybase with a news events search engine that helps find historical and ongoing news stories and inspect their dynamic timelines.
Transverse Fivebranes in Matrix Theory

M-theory on the maximally supersymmetric plane wave background of eleven-dimensional supergravity admits spherical BPS transverse M5-branes with zero light-cone energy. We give direct evidence that the single M5-brane state corresponds to the trivial (X = 0) classical vacuum in the large N limit of the plane wave matrix theory. In particular, we show that the linear fluctuation spectrum of the spherical fivebrane matches exactly with the set of exactly protected excited states about the X = 0 vacuum in the matrix model. These states include geometrical fluctuations of the sphere, excitations of the worldvolume two-form field, and fermion excitations. In addition, we propose a description of multiple fivebrane states in terms of matrix model vacua. Finally, we discuss how to obtain the continuum D2/M2 and NS5/M5 theories on spheres from the matrix model. The matrix model can be viewed as a regularization for these theories.
Multiclass Semi-supervised Learning for Animal Behavior Recognition from Accelerometer Data

In this paper we present a new Multiclass semi-supervised learning algorithm that uses a base classifier in combination with a similarity function applied to all data to find a classifier that maximizes the margin and consistency over all data. A novel multiclass loss function is presented and used to derive the algorithm. We apply the algorithm to animal behavior recognition from accelerometer data. Animal-borne accelerometer data are collected from free-ranging animals and then labeled by a human expert. The resulting data are used to train a classifier. However, labeling is not easy from accelerometer data only and it is often not feasible to observe animals fitted with an accelerometer. All current approaches to this behavior recognition task use supervised or unsupervised learning. Since unlabeled data are easy to acquire and collect, a semi-supervised approach seems appropriate and reduces the human efforts for labeling. Experiments with accelerometer data collected from free-ranging gulls and benchmark UCI datasets show that the algorithm is effective and compares favorably with existing algorithms for multiclass semi-supervised learning.
The impact of relative distance on artificial-noise-aided secure transmission

In this paper, we investigate the impact of relative distance and large-scale fading of legitimate user and eavesdropper on the artificial noise (AN) assisted secrecy scheme in a multiple-input single-output (MISO) wiretap channel. We show that AN does not always improve the achievable ergodic secrecy rate (ESR), and provide an exact signal-to-noise ratio (SNR) threshold below which AN does not work. We also derive approximately closed-form expressions of AN secrecy gain, which is defined as the difference between the ESR with AN and without AN. Analytical and numerical results verify that the relative distance impacts greatly on the SNR threshold and the AN secrecy gain.
Churn Prediction in Telecommunications Industry. A Study Based on Bagging Classifiers

Churn rate refers to the proportion of contractual customers who leave a supplier during a given time period. This phenomenon is very common in highly competitive markets such as telecommunications industry. In a statistical setting, churn can be considered as an outcome of some characteristics and past behavior of customers. In this paper, churn prediction is performed considering a real dataset of an European telecommunications company. An appealing parallelized version of bagging classiﬁers is used leading to a substantial reduction of the classiﬁcation error rates. The results are detailed discussed.
Decomposing the United Kingdom ’ s GDP growth : dematerialisation and IT

In this article Danny T Quah examines how, when an economy grows, its patterns of production and consumption systematically change. He describes one such large-scale evolution, namely, the increasing weightlessness of aggregate output across advanced economies. In all fast-growing successful countries, growth in information technology has contributed positively both to increasing weightlessness and to economic growth. In the sample of countries studied here, the richer the country the higher the contribution to growth of information technology and services; in no country has manufacturing, as traditionally construed, continued to be as important.
Social interaction in 'there'

Persistent online environments, such as multi-player games, exhibit a complex social organisation. These environments often feature large social groupings and elaborate cooperative behaviours. This paper discusses 'There', one such environment, focusing on how users interact online. Resources such as overlapping chat and emotional gestures create a compelling social experience, although not one without its problems. We draw three lessons from There for the design of games and virtual environments: that text chat can be better integrated into the virtual environment, that gestures are valuable as communication topics as well as resources, and that social interactions can improve the social presence felt in virtual environments.
Automatic Recognition of Personality Traits: A Multimodal Approach

A system being capable of recognize personality traits may be utilized in an enormous number of applications. Adding personality-dependency may be useful to build speaker-adaptive models, e.g., to improve Spoken Dialogue Systems (SDSs) or to monitor agents in call-centers. Therefore, the First Audio/Visual Mapping Personality Traits Challenge (MAPTRAITS 2014) focuses on estimating personality traits. In this context, this study presents the results for multimodal recognition of personality traits using support vector machines. As only small portions of the data is used for personality estimation at a time (which are later combined to a final estimate), different segmentation methods (and how to derive a final hypothesis) are analyzed regarding the task as both a regression and a classification problem.
The Auckland data set : an access link observed

In 1999 the WAND network research group of the University of Waikato set up a bi-directional measurement system on the OC3c ATM access link that connects the University of Auckland to the public Internet. The system uses Dag cards, synchronised by a GPS time receiver. A total volume of 118 Gigabytes of compressed IP header traces (more than 2 billion IP headers) have now been taken and archived, and we describe various research projects that have used these data.
Volatile Organic Compound (VOC) measurements in the Pearl River Delta (PRD) region, China

We measured levels of ambient volatile organic compounds (VOCs) at seven sites in the Pearl River Delta (PRD) region of China during the Air Quality Monitoring Campaign spanning 4 October to 3 November 2004. Two of the sites, Guangzhou (GZ) and Xinken (XK), were intensive sites at which we collected multiple daily canister samples. The observations reported here provide a look at the VOC distribution, speciation, and photochemical implications in the PRD region. Alkanes constituted the largest percentage (>40%) in mixing ratios of the quantified VOCs at six sites; the exception was one major industrial site that was domi- nated by aromatics (about 52%). Highly elevated VOC lev- els occurred at GZ during two pollution episodes; however, the chemical composition of VOCs did not exhibit notice- able changes during these episodes. We calculated the OH loss rate to estimate the chemical reactivity of all VOCs. Of the anthropogenic VOCs, alkenes played a predominant role in VOC reactivity at GZ, whereas the contributions of reac- tive aromatics were more important at XK. Our preliminary analysis of the VOC correlations suggests that the ambient VOCs at GZ came directly from local sources (i.e., automo- biles); those at XK were influenced by both local emissions and transportation of air mass from upwind areas. and a population of about 45.5 million. It has been the most economically dynamic region of mainland China over the last two decades, with a per capita GDP of US$ 6583 in 2004. The average annual rate of GDP growth in the PRD from 2000 to 2004 was 13.6%, which is well above the na- tional GDP growth rate (8.6%) (China Yearbook of Statistics, 2004). Guangzhou (GZ), the capital of Guangdong Province, had the highest GDP value (US $ 496 billion) in the PRD re- gion in 2004. Dongguan (DG) is the city with the fastest growth rate GDP (18.7% per year from 2002-2006); it is a major manufacturing base for a wide range of products, including electronics, communication, paper, garments and textiles, food, shoes, and plastic.
Mutual Identification and Key Exchange Scheme in Secure VANETs Based on Group Signature

This paper proposes an identilication and key exchange scheme in secure VANETs based on group signature. Security requirements such as authentication, conditional privacy, non-repudiation, and confidentiality are required to satisfy various vehicular applications. Although the existinig group sIgnature schemes are suitable for secure vehicular communications, they do not provide mutual identification and key exchange for data confidentiality. The principal idea of this paper is that the proposed scheme allows only one credential to authenticate ephemeral Diffie-Hellman parameters generated be all the session keys. Our scheme achieves security requirements for various VANET-based applications.
Automatic learning rate adjustment for self-supervising autonomous robot control

Described is an application in which an Artificial Neural Network (ANN) controls the positioning of a robot arm with five degrees of freedom by using visual feedback provided by two cameras. This application and the specific ANN model, local liner maps, are based on the work of Ritter, Martinetz, and Schulten. We extended their approach by generating a filtered, average positioning error from the continuous camera feedback and by coupling the learning rate to this error. When the network learns to position the arm, the positioning error decreases and so does the learning rate until the system stabilizes at a minimum error and learning rate. This abolishes the need for a predetermined cooling schedule. The automatic cooling procedure results in a closed loop control with no distinction between a learning phase and a production phase. If the positioning error suddenly starts to increase due to an internal failure such as a broken joint, or an environmental change such as a camera moving, the learning rate increases accordingly. Thus, learning is automatically activated and the network adapts to the new condition after which the error decreases again and learning is 'shut off'. The automatic cooling is therefore a prerequisite for the autonomy and the fault tolerance of the system.
A self-calibration method for a multi stereo-rig system based on robust 3D motion estimation

This paper discusses self-calibration method for a multi stereo-rig passive face recognition system with overlapping views of individual cameras. Two cameras fixed inside a rig can be easily calibrated in a factory environment so each rig will be considered individually calibrated. It is incredibly important to estimate relative positions and orientations of the stereo rigs with respect to each other without using any calibration objects. We propose calibration method using point correspondences between all cameras. In this paper we present a novel more robust method that calculates metric reconstruction. Our method reconstructs 3D point set for every stereo rig from multiple dynamic scene images. Point correspondences are established by tracking points over all images captured simultaneously. So pairing between points is known but data keeping outliers. Then RANSAC technique is applied to reject outliers and finally 3D motion is estimated. In this paper it is shown that the presented method can be accepted as sufficient in accuracy. Our method can be easily adopted for various numbers of stereo rigs.
Cadence Detection in Western Traditional Stanzaic Songs using Melodic and Textual Features

Many Western songs are hierarchically structured in stanzas and phrases. The melody of the song is repeated for each stanza, while the lyrics vary. Each stanza is subdivided into phrases. It is to be expected that melodic and textual formulas at the end of the phrases offer intrinsic clues of closure to a listener or singer. In the current paper we aim at a method to detect such cadences in symbolically encoded folk songs. We take a trigram approach in which we classify trigrams of notes and pitches as cadential or as non-cadential. We use pitch, contour, rhythmic, textual, and contextual features, and a group of features based on the conditions of closure as stated by Narmour [11]. We employ a random forest classification algorithm. The precision of the classifier is considerably improved by taking the class labels of adjacent trigrams into account. An ablation study shows that none of the kinds of features is sufficient to account for good classification, while some of the groups perform moderately well on their own.
Complexity of Representation and Inference in Compositional Models with Part Sharing

This paper performs a complexity analysis of a class of serial and parallel compositional models of multiple objects and shows that they enable efficient representation and rapid inference. Compositional models are generative and represent objects in a hierarchically distributed manner in terms of parts and subparts, which are constructed recursively by part-subpart compositions. Parts are represented more coarsely at higher level of the hierarchy, so that the upper levels give coarse summary descriptions (e.g., there is a horse in the image) while the lower levels represents the details (e.g., the positions of the legs of the horse). This hierarchically distributed representation obeys the executive summary principle, meaning that a high level executive only requires a coarse summary description and can, if necessary, get more details by consulting lower level executives. The parts and subparts are organized in terms of hierarchical dictionaries which enables part sharing between different objects allowing efficient representation of many objects. The first main contribution of this paper is to show that compositional models can be mapped onto a parallel visual architecture similar to that used by bio-inspired visual models such as deep convolutional networks but more explicit in terms of representation, hence enabling part detection as well as object detection, and suitable for complexity analysis. Inference algorithms can be run on this architecture to exploit the gains caused by part sharing and executive summary. Effectively, this compositional architecture enables us to perform exact inference simultaneously over a large class of generative models of objects. The second contribution is an analysis of the complexity of compositional models in terms of computation time (for serial computers) and numbers of nodes (e.g., "neurons") for parallel computers. In particular, we compute the complexity gains by part sharing and executive summary and their dependence on how the dictionary scales with the level of the hierarchy. We explore three regimes of scaling behavior where the dictionary size (i) increases exponentially with the level of the hierarchy, (ii) is determined by an unsupervised compositional learning algorithm applied to real data, (iii) decreases exponentially with scale. This analysis shows that in some regimes the use of shared parts enables algorithms which can perform inference in time linear in the number of levels for an exponential number of objects. In other regimes part sharing has little advantage for serial computers but can enable linear processing on parallel computers.