Miracle-dz/UniFAR-FTUs huggingface dataset:
Midbrain muscarinic receptor mechanisms underlying regulation of mesoaccumbens and nigrostriatal dopaminergic transmission in the rat

Laterodorsal (LDT) and pedunculopontine (PPT) tegmental nuclei in the mesopontine project cholinergic inputs to the midbrain ventral tegmental area (VTA) and substantia nigra pars compacta (SNc), respectively, to directly and indirectly influence the activity of dopamine neuronal cells via actions on muscarinic and nicotinic receptors. The present study investigated the role of midbrain muscarinic receptors in the functional modulation of VTA and SNc dopamine cell activity as reflected by alterations in, respectively, nucleus accumbens (NAc) and striataldopamine efflux. In vivo chronoamperometry was used to measure changes in basal dopamine efflux via stearate–graphite paste electrodes implanted unilaterally in the NAc or striatum of urethane‐anaesthetized rats, following blockade or activation of, respectively, VTA or SNc muscarinic receptors. Intra‐VTA or ‐SNc infusion of the muscarinic antagonist scopolamine (200 µg/µL) reduced, respectively, NAc and striatal dopamine efflux while infusion of the muscarinic and nicotinic agonist carbachol (0.5 µg/µL) or the prototypical muscarinic agonist muscarine (0.5 µg/µL) increased NAc and striatal dopamine efflux. Transient decreases in dopamine efflux preceded these increases selectively in the striatum, suggesting a reduction in excitatory or increase in inhibitory drive to the SNc by preferential activation of M3 muscarinic receptors on GABA interneurons and glutamatergic inputs. This was confirmed by showing that selective blockade of M3 receptors with p‐F‐HHSiD (0.5 µg/µL) increased striatal, but not NAc, dopamine efflux. Together, these findings suggest that midbrain muscarinic receptors, probably M5 subtypes on VTA and SNc dopamine neurons, contribute to the tonic excitatory regulation of forebrain basal dopamine transmission whereas presynaptic M3 receptors serve to counter excessive excitation of nigral dopamine cell activity.
The role of migrant organisations and personal networks in the Russian federation

As state assistance is limited some migrants in the Russian Federation have set up their own organisations which are proving to be a growing force for assistance advocacy and reform. The widespread upheaval brought about by the collapse of the Soviet Union in 1991 prompted ongoing large-scale population movements. The most significant is that of ethnic Russians and Russian-speaking people from the other former Soviet Republics to the Russian Federation. In the period 1991-2002 the Russian Federation registered some 1.5 million forced migrants or refugees. It is estimated that the total number of people who have arrived from the former Soviet republics is 8-10 million. As the state has only provided limited assistance a number of non-governmental migrants organizations have emerged to try to fill the gap. (excerpt)
Prevalence of lost-time claims for mild traumatic brain injury in the working population: Improving estimates using workers compensation databases

Primary objective: To test the usefulness of a method to improve the measurement of prevalent mild traumatic brain injury (MTBI) among injured workers with a workers compensation claim. Methods: Database codes were selected to identify MTBI cases in the Ontario workers compensation lost-time claims database. A random sample of 210 claims was selected, classified as MTBI or not, and used to calculate proportions with MTBI among code groups. The annual prevalence of MTBI in 1997 and 1998 was calculated by weighting the numerators with the appropriate proportions of MTBI within each code group. Results: Four code groups were created: the head region, cranial region, concussion code group and the brain region. The proportion of MTBI in each group was 29%, 19%, 92% and 32%, respectively. The 1997 prevalence depended on the codes used, from 39/10 000 (95% confidence interval (CI): 35–44) for a weighted version of the ‘concussion’ code to 58/10 000 (95% CI: 50–65) for inclusion of all identified MTBI codes. Conclusions: Restricting the enumeration of MTBI to specific ‘concussion’ codes can lead to under-estimation of the prevalence of MTBI in epidemiological studies using workers compensation data. Approximately six out of every 1000 lost-time claims are associated with MTBI. Given lost-time estimates of disability under-estimate the prevalence of this mild injury, MTBI, is an important workplace injury.
CANE: Context-Aware Network Embedding for Relation Modeling

Network embedding (NE) is playing a critical role in network analysis, due to its ability to represent vertices with efficient low-dimensional embedding vectors. However, existing NE models aim to learn a fixed context-free embedding for each vertex and neglect the diverse roles when interacting with other vertices. In this paper, we assume that one vertex usually shows different aspects when interacting with different neighbor vertices, and should own different embeddings respectively. Therefore, we present Context-Aware Network Embedding (CANE), a novel NE model to address this issue. CANE learns context-aware embeddings for vertices with mutual attention mechanism and is expected to model the semantic relationships between vertices more precisely. In experiments, we compare our model with existing NE models on three real-world datasets. Experimental results show that CANE achieves significant improvement than state-of-the-art methods on link prediction and comparable performance on vertex classification. The source code and datasets can be obtained from https://github.com/thunlp/CANE.
Compressed sensing and best k-term approximation

The typical paradigm for obtaining a compressed version of a discrete signal represented by a vector x ∈ R is to choose an appropriate basis, compute the coefficients of x in this basis, and then retain only the k largest of these with k < N . If we are interested in a bit stream representation, we also need in addition to quantize these k coefficients. Assuming, without loss of generality, that x already represents the coefficients of the signal in the appropriate basis, this means that we pick an approximation to x in the set Σk of k-sparse vectors (1.1) Σk := {x ∈ R : # supp(x) ≤ k}, where supp(x) is the support of x, i.e., the set of i for which xi = 0, and #A is the number of elements in the set A. The best performance that we can achieve by such an approximation process in some given norm ‖ · ‖X of interest is described by the best k-term approximation error
Impact of Periodic Current Pulses on Li-Ion Battery Performance

Pulse charging and pulse discharging have been reported by many authors in the literature to improve the performance of various secondary electrochemical cells. Only a few authors mentioned the effects of such charge and discharge method on lithium-ion batteries. The overall objective of this work is to experimentally investigate the impact of certain current pulse profiles on the electrical performance of Li-ion batteries. The results highlight a detrimental impact of periodic pulses on the cell performance compared to profiles with constant current.
Multi-Role Project (MRP): A New Project-Based Learning Method for STEM

This paper presents the “Multi-Role Project” method (MRP), a broadly applicable project-based learning method, and describes its implementation and evaluation in the context of a Science, Technology, Engineering, and Mathematics (STEM) course. The MRP method is designed around a meta-principle that considers the project learning activity as a role-playing game based on two projects: a learning project and an engineering project. The meta-principle is complemented by five principles that provide a framework to guide the working practices of student teams: distribution of responsibilities; regular interactions and solicitations within the team; anticipation and continuous improvement; positive interdependence and alternating individual/collective work; and open communication and content management. This paper presents the implementation of MRP in a course teaching software engineering, UML language, and project management. The results show that MRP helped the course's students to acquire important professional knowledge and skills, experience near-real-world professional realities, and develop their abilities to work both in teams and autonomously.
Cobordism of singular maps

Throughout this paper we consider smooth maps of positive codimensions, having only stable singularities (see Arnold, Gusein-Zade and Varchenko [3, Section 1.4, Chapter 1]. We prove a conjecture, due to M Kazarian, connecting two classifying spaces in singularity theory for this type of singular maps. These spaces are: 1) Kazarian’s space (generalising Vassiliev’s algebraic complex and) showing which cohomology classes are represented by singularity strata. 2) The space X giving homotopy representation of cobordisms of singular maps with a given list of allowed singularities as in work of Rimanyi and the author [29; 34; 35].
An integrative approach towards understanding the psychological and neural basis of congenital prosopagnosia.

Faces have distinctive evolutionary and social significance and, therefore, it is not surprising that face perception is probably the most developed visual perceptual skill in humans. At an input level, faces are perceptually similar homogeneous exemplars drawn from a single class and are all composed of essentially the same local elements (e.g., two eyes, a nose, cheeks and a mouth) in the same spatial layout (e.g., eyes above the nose). Despite this fundamental visual similarity, individual faces convey a large amount of critical information upon which we, as human observers, rely heavily for social interaction and communication. A quick look at a person’s face reveals valuable information such as age, gender, emotional state, gaze direction, and useful speech cues. We can also identify individuals accurately and rapidly across radically different viewing conditions (e.g. lighting, vantage points) and structural changes of the face as the person ages or conveys different expressions. Moreover, notwithstanding some individual differences (Rotshtein et al., 2007), we can represent the unique identity of an almost unlimited number of faces and, when presented with a familiar face, can easily come up with the name of the person as well as other relevant biographical information that pertains to the individual. Faces are also interesting at the neural level. It has been shown that there are spe-
Erotic Capital: The Power of Attraction in the Boardroom and the Bedroom

In 2010, pioneering sociologist Catherine Hakim shocked the world with a provocative new theory: In addition to the three recognized personal assets (economic, cultural, and social capital), each individual has a fourth asset--erotic capital--that he or she can, and should, use to advance within society. In this bold and controversial book, Hakim explores the applications and significance of erotic capital, challenging the disapproval meted out to women and men who use sex appeal to get ahead in life. Social scientists have paid little serious attention to these modes of personal empowerment, despite overwhelming evidence of their importance. In Erotic Capital, Hakim marshals a trove of research to show that rather than degrading those who employ it, erotic capital represents a powerful and potentially equalizing tool--one that we scorn only to our own detriment.
Prescription patterns of psychotropic medications in elderly compared with younger participants who achieved a "recovered" status in the systematic treatment enhancement program for bipolar disorder.

OBJECTIVES This study compares prescription patterns between young adults and elderly with bipolar disorder who achieved a recovery status during the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD). DESIGN STEP-BD is a multicenter National Institute of Mental Health-funded project designed to evaluate the longitudinal outcome of patients with bipolar disorder. The STEP-BD study involved extensive assessment across multiple domains including demographic data, diagnosis, symptom severity, treatment, and clinical status. Patients achieved "recovered" status when they experienced eight consecutive weeks without significant symptoms. PARTICIPANTS The authors analyzed data of all subjects who achieved a recovered status at least once in their participation. MEASUREMENTS The authors compared treatment regimes and doses among young participants with middle age (N = 3,364), 20-59 years old, and older participants 60 and above (N = 246). RESULTS Of the 3,615 STEP-BD participants who had a lifetime diagnosis of bipolar subtypes I or II, 67.6% (N = 2442) achieved a recovered status during their participation. A total of 78.5% (N = 193) of older patients recovered compared with 66.8% of the younger cohort. On average, participants who reached a recovered status took 2.05 medications with no difference between age groups. Lithium was prescribed to 37.8% of younger patients compared with only 29.5% of older participants. The mean dosages taken by younger and older patients differed significantly only for lithium, valproate, and risperidone with elderly individuals prescribed lower daily dosages. Significant reduction in lithium dosing was observed among individuals aged 50 and older and among individuals 60 and older for valproate. Although valproate was more often prescribed, 42.1% of recovered bipolar elder achieved recovery with lithium alone compared with only 21.3% of the younger cohort. CONCLUSION This data shows recovery is achievable in the elderly though more than one medication is often needed regardless of age.
Understanding the Impact of Web Personalization on User Information Processing and Decision Outcomes

Personalized information technology services have become a ubiquitous phenomenon. Companies worldwide are using the web to provide personalized offerings and unique experiences to their customers. While there is a lot of hype about delivering personalized services over the web, little is known about the effectiveness of web personalization and the link between the IT artifact (the personalization agent) and the effects it exerts on a user's information processing and decision making. To address the impact of personalized content, this article theoretically develops and empirically tests a model of web personalization. The model is grounded on social cognition and consumer research theories adapted to the peculiar features of web personalization. The influence of a personalization agent is mediated by two variables: content relevance and self reference. Hypotheses generated from the model are empirically tested in a laboratory experiment and a field study. The findings indicate that content relevance, self reference, and goal specificity affect the attention, cognitive processes, and decisions of web users in various ways. Also, users are found to be receptive to personalized content and find it useful as a decision aid. Theoretical and practical implications of the findings are discussed.
R-MAC: Reservation Medium Access Control Protocol for Wireless Sensor Networks

Energy consumption is a critical issue in wireless sensor networks as the battery of a sensor node, in most cases, cannot be recharged or replaced after deployment. In order to detect an event, a sensor node spends most of the time in monitoring its environment, during which a significant amount of energy can be saved by placing the radio in the low power sleep mode when no reception and/or transmission of data is involved. In this paper, we discuss the design of a new MAC protocol for wireless sensor networks, which mainly avoids overhearing, collisions, and frequent commutation between sleep and active modes. These issues are generally considered to be the most important reasons behind energy waste in heavy loaded conditions of wireless sensor networks. The proposed protocol, called Reservation-MAC (R-MAC), uses two separate periods during the communication process. In the first period, nodes compete for time slots reservation for their future transmissions, and in the second period, each node transmits its data or receive data from a corresponding sender. Once a node is aware of its transmission and/or reception time slot, it stays active only for these time slots and goes back to the sleep mode during the remaining time of the transmission period. In our experiments, the performance of the R-MAC protocol is studied in saturated conditions and compared with the well known S-MAC and T- MAC protocols. Depending on the traffic load, the proposed MAC protocol significantly improves the energy consumption compared to S-MAC and T-MAC.
Evaluation and Selection of Appropriate Reference Genes for Real-Time Quantitative PCR Analysis of Gene Expression in Nile Tilapia (Oreochromis niloticus) during Vaccination and Infection

qPCR as a powerful and attractive methodology has been widely applied to aquaculture researches for gene expression analyses. However, the suitable reference selection is critical for normalizing target genes expression in qPCR. In the present study, six commonly used endogenous controls were selected as candidate reference genes to evaluate and analyze their expression levels, stabilities and normalization to immune-related gene IgM expression during vaccination and infection in spleen of tilapia with RefFinder and GeNorm programs. The results showed that all of these candidate reference genes exhibited transcriptional variations to some extent at different periods. Among them, EF1A was the most stable reference with RefFinder, followed by 18S rRNA, ACTB, UBCE, TUBA and GAPDH respectively and the optimal number of reference genes for IgM normalization under different experiment sets was two with GeNorm. Meanwhile, combination the Cq (quantification cycle) value and the recommended comprehensive ranking of reference genes, EF1A and ACTB, the two optimal reference genes, were used together as reference genes for accurate analysis of immune-related gene expression during vaccination and infection in Nile tilapia with qPCR. Moreover, the highest IgM expression level was at two weeks post-vaccination when normalized to EF1A, 18S rRNA, ACTB, and EF1A together with ACTB compared to one week post-vaccination before normalizing, which was also consistent with the IgM antibody titers detection by ELISA.
Linguistic Features for Subjectivity Classification

Opinions are subjective expressions that describe people's viewpoints, perspectives or feelings about entities, events and theirs properties. Detecting subjective expressions is the task of identifying whether a given text is subjective (i.e. an opinion)or objective (i.e. a reports fact). This task is considered as the first problem and it is very important for opinion mining and sentiment analysis which is now attracting many researchers cause its applicable capacity. Improvements in subjectivity classification will positively impact on the performance of a sentiment analysis system. Actually, features play the most important role for getting accurate subjective sentences. In this paper, we will enrich features by using syntactic information of the text. From our observation when investigating opinion evidences in the texts, we will propose syntax-based patterns which are used for extracting rich linguistic features. Combining these new features with conventional features from previous studies, we obtain a high accuracy (about 92.1%) for detecting subjective sentences on the Movie review data.
DCU-Symantec Submission for the WMT 2012 Quality Estimation Task

This paper describes the features and the machine learning methods used by Dublin City University (DCU) and SYMANTEC for the WMT 2012 quality estimation task. Two sets of features are proposed: one constrained, i.e. respecting the data limitation suggested by the workshop organisers, and one unconstrained, i.e. using data or tools trained on data that was not provided by the workshop organisers. In total, more than 300 features were extracted and used to train classifiers in order to predict the translation quality of unseen data. In this paper, we focus on a subset of our feature set that we consider to be relatively novel: features based on a topic model built using the Latent Dirichlet Allocation approach, and features based on source and target language syntax extracted using part-of-speech (POS) taggers and parsers. We evaluate nine feature combinations using four classification-based and four regression-based machine learning techniques.
Meta-Analytically Informed Network Analysis of Resting State fMRI Reveals Hyperconnectivity in an Introspective Socio-Affective Network in Depression

Alterations of social cognition and dysfunctional interpersonal expectations are thought to play an important role in the etiology of depression and have, thus, become a key target of psychotherapeutic interventions. The underlying neurobiology, however, remains elusive. Based upon the idea of a close link between affective and introspective processes relevant for social interactions and alterations thereof in states of depression, we used a meta-analytically informed network analysis to investigate resting-state functional connectivity in an introspective socio-affective (ISA) network in individuals with and without depression. Results of our analysis demonstrate significant differences between the groups with depressed individuals showing hyperconnectivity of the ISA network. These findings demonstrate that neurofunctional alterations exist in individuals with depression in a neural network relevant for introspection and socio-affective processing, which may contribute to the interpersonal difficulties that are linked to depressive symptomatology.
Spin squeezing of high-spin, spatially extended quantum fields

Investigations of spin squeezing in ensembles of quantum particles have been limited primarily to a subspace of spin fluctuations and a single spatial mode in high-spin and spatially extended ensembles. Here, we show that a wider range of spin squeezing is attainable in ensembles of high-spin atoms, characterized by sub-quantum-limited fluctuations in several independent planes of spin-fluctuation observables. Further, considering the quantum dynamics of an f=1 ferromagnetic spinor Bose–Einstein condensate, we demonstrate theoretically that a high degree of spin squeezing is attained in multiple spatial modes of a spatially extended quantum field and that such squeezing can be extracted from spatially resolved measurements of magnetization and nematicity, i.e. the vector and quadrupole magnetic moments, of the quantum gas. Taking into account several experimental limitations, we predict that the variance of the atomic magnetization and nematicity may be reduced as far as 20 dB below the standard quantum limits.
Ieee Transactions on Signal Processing Riemannian Medians and Means with Applications to Radar Signal Processing

— We develop a new geometric approach for high resolution Doppler processing based on the Rieman-nian geometry of Toeplitz covariance matrices and the notion of Riemannian p-means. This paper summarizes briefly our recent work in this direction. First of all, we introduce radar data and the problem of target detection. Then we show how to transform the original radar data into Toeplitz covariance matrices. After that, we give our results on the Riemannian geometry of Toeplitz covariance matrices. In order to compute p-means in practical cases, we propose deterministic and stochastic algorithms, of which the convergence results are given, as well as the rate of convergence and error estimates. Finally, we propose a new detector based on Riemannian median and show its advantage over the existing processing methods.