Miracle-dz/UniFAR-FTUs huggingface dataset:
Does a Family Meetings Intervention Prevent Depression and Anxiety in Family Caregivers of Dementia Patients? A Randomized Trial

Background Family caregivers of dementia patients are at increased risk of developing depression or anxiety. A multi-component program designed to mobilize support of family networks demonstrated effectiveness in decreasing depressive symptoms in caregivers. However, the impact of an intervention consisting solely of family meetings on depression and anxiety has not yet been evaluated. This study examines the preventive effects of family meetings for primary caregivers of community-dwelling dementia patients. Methods A randomized multicenter trial was conducted among 192 primary caregivers of community dwelling dementia patients. Caregivers did not meet the diagnostic criteria for depressive or anxiety disorder at baseline. Participants were randomized to the family meetings intervention (n = 96) or usual care (n = 96) condition. The intervention consisted of two individual sessions and four family meetings which occurred once every 2 to 3 months for a year. Outcome measures after 12 months were the incidence of a clinical depressive or anxiety disorder and change in depressive and anxiety symptoms (primary outcomes), caregiver burden and quality of life (secondary outcomes). Intention-to-treat as well as per protocol analyses were performed. Results A substantial number of caregivers (72/192) developed a depressive or anxiety disorder within 12 months. The intervention was not superior to usual care either in reducing the risk of disorder onset (adjusted IRR 0.98; 95% CI 0.69 to 1.38) or in reducing depressive (randomization-by-time interaction coefficient = −1.40; 95% CI −3.91 to 1.10) or anxiety symptoms (randomization-by-time interaction coefficient = −0.55; 95% CI −1.59 to 0.49). The intervention did not reduce caregiver burden or their health related quality of life. Conclusion This study did not demonstrate preventive effects of family meetings on the mental health of family caregivers. Further research should determine whether this intervention might be more beneficial if provided in a more concentrated dose, when applied for therapeutic purposes or targeted towards subgroups of caregivers. Trial Registration Controlled-Trials.com ISRCTN90163486
Constrained Nonnegative Matrix Factorization with Applications to Music Transcription

We apply nonnegative matrix factorization to the task of music transcription. In music transcription we are given an audio recording of a musical piece and attempt to find the underlying sheet music which generated the music. We improve upon current transcription results by imposing novel temporal and sparsity constraints which exploit the structure of music. We demonstrate the effectiveness or our technique on the MAPS dataset. 1 Matrix Factorization In this work we consider the problem of matrix factorization and its applications to music transcription. Given a data matrix X with columns as data points, we wish to approximate X as the product of two smaller matrices, B and G.
Research and Analysis of Structural Hole and Matching Coefficient

Measure is a map from the reality or experimental world to the mathematical world, through which people can more easily understand the properties of entities and the relationship between them. But the traditional software measurement methods have been unable to effectively measure this large-scale software. Therefore, trustworthy measurement gives an accurate measurement to these emerging features, providing valuable perspectives and different research dimensions to understand software systems. The paper introduces the complex network theory to software measurement methods and proposes a statistical measurement methodology. First we study the basic parameters of the complex network, and then introduce two new measurement parameters: structural holes, matching coefficient.
An Effective Classification Method for BCI Based on Optimized SVM by GA

This paper proposed an effective method for EEG data classification in a Brain-Computer Interfacing system. We use Principal Component Analysis for feature extracting, then use an optimized Support Vector Machine for classification. The SVM's parameters are optimized by Genetic Algorithm. Furthermore, and optimal signal combination search is performed to get a higher classification rate, an explanation from the human physiological point of view is given. Experiment shows that this method can achieve higher classification accuracy than normal SVM classifier and artificial neural network.
Compression of Polynomial Texture Maps

image compression, texture mapping, 3D graphics, JPEG, JPEG-LS, polynomial texture maps Polynomial Texture Maps (PTM's) are representations of stationary 3D objects which provide enhanced image quality and improved perception of the object surface details at the cost of increased storage requirements. In this paper, we investigate several compression schemes for PTM's, that either preserve every feature of the original texture map (lossless compression), or allow for a small reconstruction error (near-lossless and lossy compression).
Pre-Retrieval based Strategies for Cross Language News Story Search

The task of measuring text reuse and linking target and source documents becomes challenging if reused text has been translated in other language and even more challenging if translation language uses a different script and syntactical structure. This year, in CLINSS, focus was on journalistic text reuse between texts written in two different languages. This paper focuses on identifying text reuse between texts written in two different languages-English and Hindi and aims at evaluating the CLINSS dataset by linking 25 target English news stories with top 100 Hindi news stories out of corpora of 50691 stories. This is our first participation at CLINSS task and we have submitted two sets of runs as MANIT-1 and MANIT-2. In MANIT-1 two pre-retrieval strategies 1) Query formed using Proper Noun and 2) Query formed using words whose frequency is equal to or higher than average frequency are used to formulate the query. Query is translated using either dictionary based or machine translation CLIR based approach before retrieving the documents. In Run 1 and 2 pre-retrieval strategies clubbed up with CLIR's dictionary based approach is used to link English news stories with Hindi news stories. In Run-3 instead of using dictionary based approach, freely available online Google translate [18] and online Changathi Hindi transliterater [6] is used as Hindi resources for translating/transliterating the query. Through MANIT-1 runs the attempt is to evaluate the performance of two pre-retrieval strategies and to compare the existing dictionary based and machine translation based approaches of CLIR. It is observed that dictionary based approach clubbed up with proper noun based pre-retrieval strategy performed better.
Classification of Aortic Stiffness From Eigendecomposition of the Digital Volume Pulse Waveform

Aortic stiffness as measured by aortic pulse wave velocity (PWV) has been shown to be an independent predictor of cardiovascular disease (CVD), however, the measurement of PWV is time consuming. Recent studies have shown that pulse contour characteristics depend on arterial properties such as arterial stiffness. This paper presents a method for estimating PWV from the digital volume pulse (DVP), a waveform that can be rapidly and simply acquired by measuring transmission of infra-red light through the finger pulp. PWV and DVP were measured on 461 subjects attending a cardiovascular prevention clinic at St Thomas' Hospital, London. Using a non-linear kernel based support vector machine (SVM) classifier, it is possible to achieve results of up to 88% sensitivity and 82% specificity on unseen data. Further, we show that this approach outperforms traditional artificial neural network (ANN) methods. This technique could be employed by health professionals to rapidly diagnose patients' cardiovascular fitness in general practice clinics
An iterative multigrid regularization method for Toeplitz discrete ill-posed problems ∗

Iterative regularization multigrid methods have been successful applied to signal/image deblurring problems. When zero-Dirichlet boundary conditions are imposed the deblurring matrix has a Toeplitz structure and it is potentially full. A crucial task of a multilevel strategy is to preserve the Toeplitz structure at the coarse levels which can be exploited to obtain fast computations. The smoother has to be an iterative regularization method. The grid transfer operator should preserve the regularization property of the smoother. This paper improves the iterative multigrid method proposed in [11] introducing a wavelet soft-thresholding denoising post-smoother. Such post-smoother avoids the noise amplification that is the cause of the semi-convergence of iterative regularization methods and reduces ringing effects. The resulting iterative multigrid regularization method stabilizes the iterations so that and imprecise (over) estimate of the stopping iteration does not have a deleterious effect on the computed solution. Numerical examples of signal and image deblurring problems confirm the effectiveness of the proposed method. AMS subject classifications: 65F22; 65N55; 15B05
L-Type Calcium Channels Mediate a Slow Excitatory Synaptic Transmission in Rat Midbrain Dopaminergic Neurons

Patch pipettes were used to record whole-cell synaptic currents under voltage-clamp in dopaminergic neurons in slices of rat substantia nigra pars compacta and ventral tegmental area. We report that dihydropyridines (DHPs), L-type Ca2+ channel antagonists, depressed a slow EPSC (EPSCslow) evoked by a train of focally delivered electrical stimuli. In fact, the amplitude of the EPSCslow was reduced by the DHP antagonists nifedipine (1–100 μm), nimodipine (1–100 μm), and isradipine (30 nm-100 μm) in a concentration-dependent and reversible manner. On the other hand, Bay-K 8644 (1 μm), an L-type Ca2+ channel agonist, increased the EPSCslow. The DHPs depressed the EPSCslow only when the high-frequency stimulation that was used to evoke this synaptic current lasted >70 msec. On the other hand, Bay-K 8644 increased the amplitude of the EPSCslow only when it was evoked by a train <70 msec. Moreover, the DHPs did not affect the EPSCfast, the IPSCfast, and the IPSCslow. The inhibition of the EPSCslow caused by the DHPs is attributed to presynaptic mechanisms because (1) the inward current generated by exogenously administered glutamate was not affected and (2) the EPSCslow was reduced to a similar degree even when the activation state of postsynaptic L-type Ca2+ channels was changed by holding the neurons at −100, −60, and +30 mV. Finally, a DHP-sensitive component of the EPSCslow could even be detected after the blockade of N-, Q-, and P-type Ca2+ channels by the combination of ω-conotoxin GVIA, ω-agatoxin IVA, and ω-conotoxin MVIIC. Taken together, these results indicate that under certain patterns of synaptic activity, L-type Ca2+ channels regulate the synaptic release of excitatory amino acids on the dopaminergic neurons of the ventral mesencephalon.
EphA2 Targeted Chemotherapy Using an Antibody Drug Conjugate in Endometrial Carcinoma

Purpose: EphA2 overexpression is frequently observed in endometrial cancers and is predictive of poor clinical outcome. Here, we use an antibody drug conjugate (MEDI-547) composed of a fully human monoclonal antibody against both human and murine EphA2 (1C1) and the tubulin polymerization inhibitor monomethylauristatin F. Experimental Design: EphA2 expression was examined in endometrial cancer cell lines by Western blot. Specificity of MEDI-547 was examined by antibody degradation and internalization assays. Viability and apoptosis were investigated in endometrial cancer cell lines and orthotopic tumor models. Results: EphA2 was expressed in the Hec-1A and Ishikawa cells but was absent in the SPEC-2 cells. Antibody degradation and internalization assays showed that the antibody drug conjugate decreased EphA2 protein levels and was internalized in EphA2-positive cells (Hec-1A and Ishikawa). Moreover, in vitro cytotoxicity and apoptosis assays showed that the antibody drug conjugate decreased viability and increased apoptosis of Hec-1A and Ishikawa cells. In vivo therapy experiments in mouse orthotopic models with this antibody drug conjugate resulted in 86% to 88% growth inhibition (P < 0.001) in the orthotopic Hec-1A and Ishikawa models compared with controls. Moreover, the mice treated with this antibody drug conjugate had a lower incidence of distant metastasis compared with controls. The antitumor effects of the therapy were related to decreased proliferation and increased apoptosis of tumor and associated endothelial cells. Conclusions: The preclinical data for endometrial cancer treatment using MEDI-547 show substantial antitumor activity. Clin Cancer Res; 16(9); 2562–70. ©2010 AACR.
Node Discovery in Freelib , a Peer-to-peer-based Digital Library

Peer-to-peer systems are being used for multiple purposes today ranging from file sharing, spam filtering, and commercial product evaluations among many. Only recently, has digital libraries been added to its use. Freelib is a peer-to-peer-based digital library that evolves its participants into communities. In this paper, we address the issue of node discovery in Freelib. Discovery in the context of Freelib refers to finding key information about peers such as their network addresses and the port numbers on which to communicate with them. Discovery is particularly important when nodes sign off from the network and join the network with different IP addresses. It is essential as it enables nodes to connect to their communities and previously known peers when they join back after being offline. We present three discovery protocols: Discovery by Flooding, DHT discovery, Link Discovery. These algorithms are not just useful for Freelib but can be adapted to other peer-to-peer systems. We analyze and compare these protocols and discuss them for other applications.
Exploring Effective Information Retrieval Technique for the Medical Web Documents: SNUMedinfo at CLEFeHealth2014 Task 3

This paper describes the participation of the SNUMedinfo team at the CLEFeHealth2014 task 3. We submitted 7 runs to Task3a (monolingual infor- mation retrieval): 1 baseline run using query likelihood model in Indri search engine; 3 runs applying UMLS based lexical query expansion utilizing dis- charge summary as an expansion term filter; 3 runs applying learning to rank technique utilizing various document features. We submitted 4 runs to Task3b (multilingual information retrieval): 1 baseline run using Google Translate for the English translation; 3 runs applying learning to rank technique on the trans- lated query.
A Risk-Neutral Stochastic Volatility

We construct a risk-neutral stochastic volatility model using no-arbitrage pricing principles. We then study the behavior of the implied volatility of options that are deep in and out of the money according to this model. The motivation of this study is to show the diierence in the asymptotic behavior of the distribution tails between the usual Black-Scholes log-normal distribution and the risk-neutral stochastic volatility distribution. In the second part of the paper, we further explore this risk-neutral stochas-tic volatility model by a Monte-Carlo study on the implied volatility curve (implied volatility as a function of the option strikes) for near-the-money options. We study the behavior of this \smile" curve under diierent choices of parameter for the model, and determine how the shape and skewness of the \smile" curve is aaected by the volatility of volatility (\V-vol") and the correlation between the underlying asset and its volatility.
Optimal hybrid control for switched affine systems under safety and liveness constraints

In this contribution, we propose a two-level hierarchical control strategy to solve the problem of minimising a cost function for a switched affine system under safety and liveness constraints. The constraints are handled on the lower level by a discrete supervisory controller designed using l-complete approximation. Its action can be represented as state invariants added to the plant model. In a bottom-up strategy, we can then synthesis a high-level controller, which computes a set of switching tables using the remaining degrees of freedom to optimise a quadratic performance index
Explicit Defense Actions Against Test-Set Attacks

Automated learning and decision making systems in public-facing applications are vulnerable to malicious attacks. Examples of such systems include spam detectors, credit card fraud detectors, and network intrusion detection systems. These systems are at further risk of attack when money is directly involved, such as market forecasters or decision systems used in determining insurance or loan rates. In this paper, we consider the setting where a predictor Bob has a fixed model, and an unknown attacker Alice aims to perturb (or poison) future test instances so as to alter Bob's prediction to her benefit. We focus specifically on Bob's optimal defense actions to limit Alice's effectiveness. We define a general framework for determining Bob's optimal defense action against Alice's worst-case attack. We then demonstrate our framework by considering linear predictors, where we provide tractable methods of determining the optimal defense action. Using these methods, we perform an empirical investigation of optimal defense actions for a particular class of linear models -- autoregressive forecasters -- and find that for ten real world futures markets, the optimal defense action reduces the Bob's loss by between 78 and 97%.
Putting Ostracism into Perspective: Young Children Tell More Mentalistic Stories after Exclusion, But Not When Anxious

Much is known about when children acquire an understanding of mental states, but few, if any, experiments identify social contexts in which children tend to use this capacity and dispositions that influence its usage. Social exclusion is a common situation that compels us to reconnect with new parties, which may crucially involve attending to those parties’ mental states. Across two studies, this line of inquiry was extended to typically developing preschoolers (Study 1) and young children with and without anxiety disorder (AD) (Study 2). Children played the virtual game of toss “Cyberball” ostensibly over the Internet with two peers who first played fair (inclusion), but eventually threw very few balls to the child (exclusion). Before and after Cyberball, children in both studies completed stories about peer-scenarios. For Study 1, 36 typically developing 5-year-olds were randomly assigned to regular exclusion (for no apparent reason) or accidental exclusion (due to an alleged computer malfunction). Compared to accidental exclusion, regular exclusion led children to portray story-characters more strongly as intentional agents (intentionality), with use of more mental state language (MSL), and more between-character affiliation in post-Cyberball stories. For Study 2, 20 clinically referred 4 to 8-year-olds with AD and 15 age- and gender-matched non-anxious controls completed stories before and after regular exclusion. While we replicated the post regular-exclusion increase of intentional and MSL portrayals of story-characters among non-anxious controls, anxious children exhibited a decline on both dimensions after regular exclusion. We conclude that exclusion typically induces young children to mentalize, enabling more effective reconnection with others. However, excessive anxiety may impair controlled mentalizing, which may, in turn, hamper effective reconnection with others after exclusion.
Measurement based modeling and assessment of QoE index in P2P IPTV services

Internet Protocol television (IPTV) is an increasingly hot technical topic both in the academic researching organizations and service providers which provides digital television services over Internet Protocol (IP) for residential and business users at a lower cost. At the meanwhile, the quality of experience (QoE) from the users point of view is the most important evaluation for IPTV service provider. A convenient and valid QoE assessment mechanism can provide high-efficiency performance feedback for SPs especially in a limited network resource environment. Our contributions in this paper are three-fold: (1) we demonstrate the key parameters that would impact the QoE in P2P live applications based on our 6-month trace; (2) the candidate index model is described for QoE representation according to the analysis result of the traffic trace; (3) we figure out the effectiveness of our passive QoE evaluation during the real popularization in campus networks. We hope that the assessment will lead to a convincing reference to the IPTV service providers and practical improvement of IPTV deployment in future work.
A spreading-activation theory of semantic processing

This paper presents a spreading-acti vation theory of human semantic processing, which can be applied to a wide range of recent experimental results. The theory is based on Quillian's theory of semantic memory search and semantic preparation, or priming. In conjunction with this, several of the miscondeptions concerning Qullian's theory are discussed. A number of additional assumptions are proposed for his theory in order to apply it to recent experiments. The present paper shows how the extended theory can account for results of several production experiments by Loftus, Juola and Atkinson's multiple-category experiment, Conrad's sentence-verification experiments, and several categorization experiments on the effect of semantic relatedness and typicality by Holyoak and Glass, Rips, Shoben, and Smith, and Rosch. The paper also provides a critique of the Smith, Shoben, and Rips model for categorization judgments. Some years ago, Quillian1 (1962, 1967) proposed a spreading-acti vation theory of human semantic processing that he tried to implement in computer simulations of memory search (Quillian, 1966) and comprehension (Quillian, 1969). The theory viewed memory search as activation spreading from two or more concept nodes in a semantic network until an intersection was found. The effects of preparation (or priming) in semantic memory were also explained in terms of spreading activation from the node of the primed concept. Rather than a theory to explain data, it was a theory designed to show how to build human semantic structure and processing into a computer.
Prediction of Hepatocellular Carcinoma Development after Hepatitis C Virus Eradication Using Serum Wisteria floribunda Agglutinin-Positive Mac-2-Binding Protein

We aimed to clarify the association between a novel serum fibrosis marker, Wisteria floribunda agglutinin-positive Mac-2-binding protein (WFA+-M2BP), and hepatocellular carcinoma (HCC) development in 355 patients with chronic hepatitis C who achieved sustained virologic response (SVR) through interferon-based antiviral therapy. Pretreatment serum WFA+-M2BP levels were quantified and the hazard ratios (HRs) for HCC development were retrospectively analyzed by Cox proportional hazard analysis. During the median follow-up time of 2.9 years, 12 patients developed HCC. Multivariate analysis demonstrated that high serum WFA+-M2BP (≥2.80 cut off index (COI), HR = 15.20, p = 0.013) and high fibrosis-4 (FIB-4) index (≥3.7, HR = 5.62, p = 0.034) were independent risk factors for HCC development. The three- and five-year cumulative incidence of HCC in patients with low WFA+-M2BP were 0.4% and 0.4%, respectively, whereas those of patients with high WFA+-M2BP were 7.7% and 17.6%, respectively (p < 0.001). In addition, combination of serum WFA+-M2BP and FIB-4 indices successfully stratified the risk of HCC: the five-year cumulative incidences of HCC were 26.9%, 6.8%, and 0.0% in patients with both, either, and none of these risk factors, respectively (p < 0.001). In conclusion, pretreatment serum WFA+-M2BP level is a useful predictor for HCC development after achieving SVR.