Miracle-dz/UniFAR-FTUs huggingface dataset:
Serum oxytocin and vasopressin levels in children with social anxiety disorder and the effects of parent characteristics

ABSTRACT OBJECTIVES: We aim to determine serum oxytocin, vasopressin levels and examine parent characteristics in children diagnosed with social anxiety disorder (SAD). METHODS: Thirty four children diagnosed with SAD and 34 mothers were compared with a healthy control group (21 control children and their mothers) in this case–control study. Assessment performed via State-Trait Anxiety Inventory (STAI), Symptom Checklist-90 (SCL-90), Parental Attitude Research Instrument (PARI), Beck Depression Inventory (BDI), Liebowitz Social Anxiety Scale (LSAS) and Social Anxiety Scale for Children-Revised (SASC-R). Serum samples collected for detection of oxytocin and vasopressin levels. RESULTS: The distribution range of vasopressin levels were found statistically higher in control group than SAD group (p = 0,002). Additionally results showed no statistically significant differences according to the mean levels of serum oxytocin and vasopressin between groups. The scores of STAI-C, SASC-R and democratic attitudes/egalitarianism subscales of PARI were found significantly higher in children with SAD. Similarly we reported that mean scores of SCL-90 scale, LSAS and SCL-90 subscales were higher in mothers of patients group. CONCLUSIONS: Although significantly lower distribution range of vasopressin levels was found in SAD patients, mean oxytocin and vasopressin levels were not associated with SAD etiology. Additionally psychopathologies particularly anxious behaviour in mothers may contribute SAD development in early period of childhood.
Speaker and Expression Factorization for Audiobook Data: Expressiveness and Transplantation

Expressive synthesis from text is a challenging problem. There are two issues. First, read text is often highly expressive to convey the emotion and scenario in the text. Second, since the expressive training speech is not always available for different speakers, it is necessary to develop methods to share the expressive information over speakers. This paper investigates the approach of using very expressive, highly diverse audiobook data from multiple speakers to build an expressive speech synthesis system. Both of two problems are addressed by considering a factorized framework where speaker and emotion are modeled in separate sub-spaces of a cluster adaptive training (CAT) parametric speech synthesis system. The sub-spaces for the expressive state of a speaker and the characteristics of the speaker are jointly trained using a set of audiobooks. In this work, the expressive speech synthesis system works in two distinct modes. In the first mode, the expressive information is given by audio data and the adaptation method is used to extract the expressive information in the audio data. In the second mode, the input of the synthesis system is plain text and a full expressive synthesis system is examined where the expressive state is predicted from the text. In both modes, the expressive information is shared and transplanted over different speakers. Experimental results show that in both modes, the expressive speech synthesis method proposed in this work significantly improves the expressiveness of the synthetic speech for different speakers. Finally, this paper also examines whether it is possible to predict the expressive states from text for multiple speakers using a single model, or whether the prediction process needs to be speaker specific.
Compiling Pseudo-Boolean Constraints to SAT with Order Encoding

This paper presents a SAT-based pseudo-Boolean (PB for short) solver named PBSugar. PBSugar translates a PB instance to a SAT instance by using the order encoding, andsearches its solution by using an external SAT solver, such as Glucose. We first introduce an optimized version of the order encoding, and it is appliedto encode each PB constraint a1 x1 +. an xn # k. The encoding isreformulated as a sparse Boolean matrix, named Counter Matrix, of size n × (k+1) constructed for each PB constraint. The same Counter Matrix can be usedfor any relations ≥, ≤, =, and ≠, and can be reused for other PBconstraints having common sub-terms. The experimental results for 669 instances of DEC-SMALLINT-LIN category(decision problems, small integers, linear constraints) demonstrates thesuperior performance of PBSugar compared to other state-of-the-art PB solvers interms of the number of solved instances within the given time limit.
siRNA Delivery Technology for Cancer Therapy: Promise and Challenges

Abstract- Small interfering RNAs (siRNA) technology has shown great promise as a new class of therapeutic interventions for the treatment of cancer and other diseases. It is a remarkable endogenous pathway that can regulate sequence-specific gene silencing. Despite the excitement about possible applications of this biological process for sequence-specific gene regulation, the major limitations against the use of siRNA-based therapeutics are their rapid degradation by serum nuclease, poor cellular uptake, and rapid renal clearance following systemic delivery, off-target effects and the induction of immune responses. Many researchers have tried to overcome these limitations by developing nuclease-resistant chemically-modified siRNAs and a variety of synthetic and natural biodegradable lipids and polymers to enhance the efficacy and safety profiles of siRNA delivery. Ideal siRNA-based delivery systems for cancer therapy must be clinically suitable, safe and effective. In this review, we introduce the greatest challenges in achieving efficient RNAi delivery and discuss design criteria and various delivery strategies for cancer therapy, including chemical modifications, lipid-based nano-vectors, polymer-mediated delivery systems, conjugate delivery systems, and others.
LogCEP - Complex Event Processing based on Pushdown Automaton

Complex (or Composite) event processing systems have become more popular in a number of areas. Non-deterministic finite automata (NFA) are frequently used to evaluate CEP queries. However, it is complex or difficult to use the traditional NFA-based method to process patterns with conjunction and negation. In this paper, we proposed a new CEP system LogCEP using pushdown automaton to support efficient processing of conjunction and negation. First, the semantic and query language specification of LogCEP system are presented. Then, an automaton named LogPDA is proposed for query processing in LogCEP system. LogPDA construction method describes how to convert a query to LogPDA automation. The LogPDA execution approach describes how to detect the specified pattern using LogPDA. Meanwhile, most of previous NFA-based optimizations can be employed to improve the evaluation efficiency. Finally, our simulation based experimental results show that our method not only extended the expressibility and processing capability but also didn't lead to efficiency decreasing.
Impedance analysis of the fragment-type tag antenna using FDTD

The impedance matching between the tag antenna and the IC chip is a crucial problem attracting increasing attentions recently. This paper firstly presents a study on impedance calculation of a metal strip dipole using finite-difference time-domain (FDTD) method with unsplit perfectly matched layer (UPML) absorbing boundary condition. On this basis, the impedance characteristic of the fragment-type tag antenna is analyzed. More accurate results are obtained. Comparisons with the commercial software results show the accuracy and validity of the method.
Trends in Crypto-Currencies and Blockchain Technologies: A Monetary Theory and Regulation Perspective

The internet era has generated a requirement for low-cost, anonymous and rapidly verifiable transactions to be used for online barter, and fast settling money has emerged as a consequence. For the most part, electronic money (e-money) has fulfilled this role, but the last few years have seen two new types of money emerge — centralized virtual currencies, usually for the purpose of transacting in social and gaming economies, and cryptocurrencies, which aim to eliminate the need for financial intermediaries by offering direct peer-to-peer (P2P) online payments. We describe the historical context that led to the development of these currencies and some modern and recent trends in their uptake, in terms of both usage in the real economy and as investment products. As these currencies are purely digital constructs, with no government or local authority backing, we discuss them in the context of monetary theory, in order to determine how they may be valued under each. Finally, we provide an overview of the state of regulatory readiness in terms of dealing with transactions in these currencies in various regions of the world.
Optical monitoring of PKS 1510-089: a binary black hole system?

Three deep flux minima with nearly the same time-scales and intervals have been observed for the blazar PKS 1510−089 in the past few years. It has been proposed that there is a binary black hole system at the nucleus of this object, and a new minimum was predicted to occur in 2002 March. We monitored this source with a 60/90 cm Schmidt telescope from 2002 February to April. In combination with the data obtained by Xie et al. in the same period, for the 2002 minimum we present a nearly symmetric light curve, which would be required by an eclipsing model of a binary black hole system. We also constrain the time-scale of the minimum to be 35 min, which is more consistent with the time-scales (∼42 min) of the three previous minima than is the 89 min time-scale given by Xie et al. The wiggling milliarcsecond radio jet observed in this object is taken as further evidence for the binary black hole system. The ‘coupling’ of the periodicity in the light curve and the helicity in the radio jet is discussed for blazars in the framework of a binary black hole system.
A HIGH REYNOLDS NUMBER ALGORITHM FOR POLYTROPIC ACCRETION DISK STUDIES

An algorithm is proposed to study two-dimensional vortices in thin, polytropic accretion disks. It is based on a method which has been used to study vortices in planetary atmo- spheres. It is special in that it conserves both energy and potential enstrophy in the absence of dissipation. This leads to desirable stability properties which permit calculations at relatively high Reynolds numbers. The algorithm is tested and compared to ex- isting methods, in particular with a Total Variation Diminishing method.
Highlights extraction from sports video based on an audio-visual marker detection framework

We propose to use a visual object (e.g., the baseball catcher) detection algorithm to find local, semantic objects in video frames in addition to an audio classification algorithm to find semantic audio objects in the audio track for sports highlights extraction. The highlight candidates are then further grouped into finer-resolution highlight segments, using color or motion information. During the grouping phase, many of the false alarms can be correctly identified and eliminated. Our experimental results with baseball, soccer and golf video are promising.
Study on state estimator for wave-piercing catamaran longitudinal motion based on EKF

For the characteristics of wave-piercing catamaran longitudinal motion when sailing with high speed in random waves, this paper proposes a state estimation algorithm for wave-piercing catamaran longitudinal motion based on EKF. On the one hand noise is filtered out from the noise pollution measurement signal as much as possible, and the desired signal is separated therefrom; on the other hand, all state information of the system can be obtained by simply selecting the pitch angle and heave displacement of wave-piercing catamaran. Theoretical analysis and simulation results show that the proposed state estimation algorithm can achieve satisfactory filtering accuracy and filtering efficiency.
Understanding and designing computing technologies that convey concerning health news

As people receiving health information through computing technology increases, there is a potential loss of the human element that occurs during face-to-face doctor-patient dialogue. Technologies that convey health information have the potential to cause unnecessary anxiety and worry or cause avoidance of a serious problem. The purpose of this study is to make recommendations on how to reduce the discomfort of patients by re-examining the design of health information technologies and focusing on the ways information is presented to the users. We report on a literature survey of health information technologies and results from interviews with patients, clinicians, and patients’ family members on how they communicate and accept concerning health news. We identify strategies clinicians use when giving a diagnosis, including building a partnership of trust with patients, being honest and giving hope, presenting information simply, acknowledging physical and emotional discomfort, and communicating through non-verbal means. Based on the findings, we discuss design considerations for technologies that convey concerning health news to help people assimilate the information better and make informed decisions.
Use of Instance Typicality for Efficient Detection of Outliers with Neural Network Classifiers

Detection of outliers is one of the data pre-processing tasks. In all the applications, outliers need to be detected to enhance the accuracy of the classifiers. Several different techniques, such as statistical, distance-based and deviation-based outlier detection exist to detect outliers. Many of these techniques use filter method. A wrapper method using the concept of instance typicality may also be used to detect outliers. This paper deals with a new wrapper method that builds an initial model using neural networks and treats values at the output of neurons in the output layer as the typicality scores. Instances with lowest output values are treated as potential outliers. In addition, the method is also useful to build compact and accurate classifiers by selecting a few most typical instances resulting in significant reduction in storage space. The method is generic and thus can also be used for instance selection with any kind of classifiers. Resultant compact models are useful for imputation of missing values.
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BSTRACT The paper proposes a neural agent that performs self-organizing classification to assist in searching and contributing to webs of documents and in the process of document reuse. By applying the Kohonen self-organizing feature map (SOFM) algorithm to patterns of influence links among documents it is possible to originate clusters of documents that help infer the aspects that such documents implicitly share. The approach complements search techniques based on semantic indexes. The resulting classification is sensitive to the multiple aspects of a document that may belong to multiple classes with a varying degree and allows for treating effectively items that typically have a limited life span, either because they are means to the collaborative production of a more complex item, or b ecause they belong to fast evolving domains. The method has been implemented by Lotus Notes Domino Web server for a casebased application in the domain of information systems design.
MelodicBrush: a novel system for cross-modal digital art creation linking calligraphy and music

MelodicBrush is a novel system that connects two ancient art forms: Chinese ink-brush calligraphy and Chinese music. Our system uses vision-based techniques to create a digitized ink-brush calligraphic writing surface with enhanced interaction functionalities. The music generation combines cross-modal stroke-note mapping and statistical language modeling techniques into a hybrid model that generates music as a real-time, auditory response and feedback to the user's calligraphic strokes. Our system is in fact a new cross-modal musical system that endows the ancient art of calligraphy writing with a novel auditory representation to provide the users with a natural and novel artistic experience. Experiment evaluations with real users suggest that MelodicBrush is intuitive and realistic, and can also be easily used to exercise creativity and support art generation.
2011the International Conference on Advanced Power System Automation and Protection Configuration and Performance Testing of Iec 61850 Goose

The IEC 61850 standard part 8-1 proposes Generic Object Oriented Substation Event (GOOSE) message for time critical applications over the Ethernet network. In order to cover the wide range of applications and achieve flexibility in implementation , GOOSE messages are kept generic in the standard. However, this flexibility leads to configuration problem achieving multi-vendor interoperability. Therefore, some efforts have been carried out in this work to present a systematic GOOSE configuration approach, as well as, verification and performance testing of the GOOSE. First part of this paper configuration of Ethernet switched network, including IEEE 1588 based time synchronization, Rapid Spanning Tree Protocol (RSTP), and IEEE 802.1Q based Quality of Services (QoS). In the second part, the paper leads to step-by-step configuration process comprising IEC 61850 data modeling, datasets of GOOSE within individual IEDs, and system integration of GOOSE. Finally, the verification of configured GOOSE messages is presented using network analyzer tools, and performance testing time delay) over the network is carried out for various network scenarios.
Effect of chemo- or radiotherapy on sperm parameters of testicular cancer patients.

BACKGROUND The aims of our study were to investigate the short- and long-term effects of chemo- or radiotherapy on spermatogenesis in patients with testicular cancer and to establish any correlation between pre-therapy sperm parameters, histotype and treatment type/intensity and the progress of spermatogenesis during the post-therapy period. METHODS We evaluated 166 patients affected by testicular cancer, who cryobanked about 1 month after the removal of the cancerous testis and before beginning chemo- (CH group; n = 71) or radiotherapy (RT group; n = 95). RESULTS For the CH group, there was a statistically significant decrease in sperm parameters, which was most significant 3 months after the end of chemotherapy. For the RT group, this decrease was most relevant 6 months after the end of radiotherapy. Two years after therapy, 3% of the CH group and 6% of the RT group remained azoospermic. To evaluate whether spermatogenesis recovery is a function of baseline semen quality, we divided each group into two subgroups by pre-therapy total sperm count (A, <40 x 10(6)/ejaculate; B, >or=40 x 10(6)/ejaculate). At t(24), subgroup A of both the CH and RT groups showed improved sperm parameters over the baseline, whereas subgroup B for both CH and RT groups showed a return of sperm parameters to those of baseline values. CONCLUSIONS In conclusion, the recovery of spermatogenesis after chemo- or radiotherapy in our group of testicular cancer patients was not a function of pre-therapy sperm parameter quality. Cryopreservation of sperm before performing such therapy is therefore imperative.
Wnt signaling regulates neuronal migration in the cerebral cortex

Cortical layers are generated by precisely coordinated cell migration events and the mammalian cortex is particularly susceptible to disorders of migration. Understanding the underlying mechanisms and their regulation is therefore crucial to evaluate how subtle alterations of the migratory process contribute to neurodevelopmental disorders. Here, we evaluated the role of Wnt signaling during different stages of radial migration. Wnt canonical as well as non-canonical signaling are active in migrating layer 2/3 callosal projection neurons. These neurons are important for intracortical network formation, and their migration is of special interest since they have a relatively long and complicated trajectory before reaching their definitive position in supragranular layers. In the first part of my thesis, we showed that canonical Wnt signaling plays a crucial role at the onset of the radial migration during the multipolar-to-bipolar transition. Indeed, we demonstrated that transient down-regulation of canonical Wnt signaling is necessary during the multipolar…
Utilizing social links for location privacy in opportunistic delay-tolerant networks

This paper is concerned with improving location-privacy for users accessing location-based services in opportunistic DTNs. We design a protocol that offers location privacy through request/reply location obfuscation technique that uses the nodes' own social network to drive the forwarding heuristic. We propose a fully distributed social-based location privacy protocol (SLPD) that utilizes social ties between nodes to ensure K-Anonymity, i.e. the requesting node's locations cannot be determined from at least k-1 other nodes in its social network. We evaluate SLPD using extensive simulations and real connectivity data traces. We compare our results to a benchmark protocol that requires centralized trusted server. We show that our distributed protocol is applicable to DTNs with various mobility patterns, and provides the user with the required privacy at less than 30% of the privacy range we define. SLPD achieves success ratios similar to the ones obtained using centralized benchmark solutions up to 15% privacy requirements.
JukeTools: a toolbox for implementing and evaluating jukebox schedulers

Scheduling jukebox resources is important to build efficient and flexible hierarchical storage systems. JukeTools is a toolbox that helps in the complex tasks of implementing and evaluating jukebox schedulers. It allows the fast development of jukebox schedulers. The schedulers can be tested in numerous environments, real and simulated. JukeTools helps the developer to easily detect errors in the schedules. Analyzer tools create detailed reports on the behavior and performance of any of the scheduler, and provide comparisons between different schedulers. This paper describes the functionality offered by JukeTools, with special emphasis on how the toolbox can be used to develop jukebox schedulers.
Adaptive Filters and Machine Learning June 3 , 2014 1 A Learning Approach based on Support Vector Regression for Single-Image Super-Resolution

7 For object recognition, generally, a novel object is explained solely in terms 8 of a small set of exemplar objects to which it is visually similar. Inspired by 9 this method, I propose a recognition method which learns separate distance 10 functions for each exemplar. However, the distances are interpretable on an 11 absolute scale and can be thresholded to detect the presence of an object. I 12 use the distance functions to detect in novel images by association for each 13 exemplar. The exemplars can be represented as image regions and the 14 learned distances capture the relative importance of shape, color, and 15 texture features for that region. The experiment results show the algorithm 16 on real-world outdoor scenes from the LabelMe dataset. 17 18