Miracle-dz/UniFAR-FTUs huggingface dataset:
Sex Specific Behavioral Profiles in Toddlers At Risk for Autism Spectrum Disorders (ASD)

The Positive Predictive Value (PPV) of the Modified Checklist for Autism in Toddlers (M-CHAT), a parent report autism screening tool, is higher for males than for females (Ludwig et al., IMFAR 2011). Given the long waitlists and high costs for ASD evaluations, there is a need to reduce the number of false positive females on the M-CHAT. The current study examined the sex specific clinical profiles of toddlers who received an ASD evaluation based on M-CHAT screen positive status in order to explore potential differences that may contribute to the differential PPV of the M-CHAT in boys and girls. The sample included 250 males and 106 females (mean age=25.3 months, SD=4.6) who were evaluated based on screen positive status on the M-CHAT. Although children with ASD demonstrated greater ASD symptoms, lower IQ and weaker language and motor skills, minimal sex differences were discovered. INDEX WORDS: Autism, Sex differences, M-CHAT, Screening SEX SPECIFIC BEHAVIORAL PROFILES IN TODDLERS AT RISK FOR AUTISM SPECTRUM DISORDERS (ASD)
Protein fabrication automation

Facile “writing” of DNA fragments that encode entire gene sequences potentially has widespread applications in biological analysis and engineering. Rapid writing of open reading frames (ORFs) for expressed proteins could transform protein engineering and production for protein design, synthetic biology, and structural analysis. Here we present a process, protein fabrication automation (PFA), which facilitates the rapid de novo construction of any desired ORF from oligonucleotides with low effort, high speed, and little human interaction. PFA comprises software for sequence design, data management, and the generation of instruction sets for liquid‐handling robotics, a liquid‐handling robot, a robust PCR scheme for gene assembly from synthetic oligonucleotides, and a genetic selection system to enrich correctly assembled full‐length synthetic ORFs. The process is robust and scalable.
Reusing Motion Data to Animate Visual Speech

In this paper we describe a technique for animating visual speech by concatenating small fragments of speech movements. The technique is analogous to the most effective audio synthesis techniques which form utterances by blending small fragments of speech waveforms. Motion and audio data is initially captured to cover the target synthesis domain; this data is subsequently phonetically labelled and segmented to provide basis units for synthesis. Sentence, word and syllable level units are used by the system to synthesize novel speech utterances. The final synthesized utterances consist of the motion of points on the surface of the skin, these trajectories are retargetted and interpolated across the surface of a target mesh for animation.
Winnicott and Gender Madness

This essay draws on Winnicott's deep insights developed in a series of texts over the last decade of his life and addressing questions of gender and sexuality, as these phenomena shape and are shaped by core experiences in the evolution of intersubjectivity. Interweaving Winnicott's work with that of Loewald, Laplanche and Matte Blanco, the essay speculates about possible developmental processes through which complex and shifting gender identifications intersect and interact with experiences of sexuality and desiring. The essay tracks Winnicott's evolving ideas about gender and madness and opens a way of thinking about one process by which gender may emerge, in a complex, ‘softly assembled’ encounter with desire and being desired. This approach also seeks to reframe and expand the current move in gender theory ‘from etiology to teleology’. The term ‘etiology’ locates us in causation, diagnosis and the sites of pathology, and I think we should continue to be wary of the intrusion of these socially driven biases and ideas into reflections about sexuality and gender in development.
A Method for Generating Infinite Positive Self-adjoint Test Matrices and Riesz Bases

In this article we propose a method to easily generate infinite multi-index positive definite self-adjoint matrices as well as Riesz bases in suitable subspaces of $L^2({\mathbb{R}}^d)$. The method is then applied to obtain some classes of multi-index Toeplitz matrices which are bounded and strictly positive on $\ell^2({\mathbb{Z}}^d)$. The condition number of some of these matrices is also computed.
Effects of depression, anxiety, comorbidity, and antidepressants on resting-state heart rate and its variability: an ELSA-Brasil cohort baseline study.

OBJECTIVE Increases in resting-state heart rate and decreases in its variability are associated with substantial morbidity and mortality, yet contradictory findings have been reported for the effects of the mood and anxiety disorders and of antidepressants. The authors investigated heart rate and heart rate variability in a large cohort from Brazil, using propensity score weighting, a relatively novel method, to control for numerous potential confounders. METHOD A total of 15,105 participants were recruited in the Brazilian Longitudinal Study of Adult Health. Mood and anxiety disorders were ascertained using the Portuguese version of the Clinical Interview Schedule-Revised. Heart rate and its variability were extracted from 10-minute resting-state electrocardiograms. Regressions weighted by propensity scores were carried out to compare participants with and without depressive or anxiety disorders, as well as users and non-users of antidepressants, on heart rate and heart rate variability. RESULTS Use of antidepressants was associated with increases in heart rate and decreases in its variability. Effects were most pronounced for the tricyclic antidepressants (Cohen's d, 0.72-0.81), followed by serotonin and norepinephrine reuptake inhibitors (Cohen's d, 0.42-0.95) and other antidepressants (Cohen's d, 0.37-0.40), relative to participants not on antidepressants. Only participants with generalized anxiety disorder showed robust, though small, increases in heart rate and decreases in its variability after propensity score weighting. CONCLUSIONS The findings may, in part, underpin epidemiological findings of increased risk for cardiovascular morbidity and mortality. Many factors that have an adverse impact on cardiac activity were controlled for in this study, highlighting the importance of cardiovascular risk reduction strategies. Further study is needed to examine whether, how, and when such effects contribute to morbidity and mortality.
An Approach to Error Concealment for Entire Right Frame Loss in Stereoscopic Video Transmission

A compressed stereoscopic video stream is sensitive to the loss of data packets when transmitted over Internet. One approach to combat the impact of such a case is the use of error concealment at the decoder. In this paper, an error concealment algorithm is proposed for restoring the entire lost right frame. Firstly, the characteristics of stereoscopic video sequence are analyzed, then based on relativity of prediction modes for right frames, prediction mode of each MB in the lost frame is chosen, and finally utilized to restore the lost MB according to the estimated motion vector or disparity vector. Experimental results show that the proposed algorithm can restore the lost frame with good quality, and is efficient for error concealment of entire lost right frame in stereoscopic video sequence
Spatial Heterogeneity of the Nonlinear Dynamics in the FMRI BOLD Response

Recent studies of blood oxygenation level dependent (BOLD) signal responses averaged over a region of interest have demonstrated that the response is nonlinear with respect to stimulus duration. Specifically, shorter duration stimuli produce signal changes larger than expected from a linear system. The focus of this study is to characterize the spatial heterogeneity of this nonlinear effect. A series of MR images of the visual and motor cortexes were acquired during visual stimulation and finger tapping, respectively, at five different stimulus durations (SD). The nonlinearity was assessed by fitting ideal linear responses to the responses at each SD. This amplitude, which is constant for different SD in a linear system, was normalized by the amplitude of the response to a blocked design, thus describing the amount by which the stimulus is larger than predicted from a linear extrapolation of the response to the long duration stimulus. The amplitude of the BOLD response showed a nonlinear behavior that varied considerably and consistently over space, ranging from almost linear to 10 times larger than a linear prediction at short SD. In the motor cortex different nonlinear behavior was found in the primary and supplementary motor cortexes.
Graph Matching for Context Recognition

In the software implementation of a general Ambient Intelligence (AmI) system, there are two major issues, on which depend the flexibility and the performance of the project. One is the implementation paradigm - how the various entities are organized and how they interact; the other is the management of context information, and how context-awareness is integrated as a first-class element in the implementation. This paper is framed in a research effort to develop an agent-based platform for AmI applications. While in previous research we have already argued in favor of using an agent-oriented paradigm for the implementation, and we have already introduced the concept of context graphs and context patterns, it is in this paper that we argue that matching context patters against context graphs is a valid method for detecting the user's situation and acting upon the user's context. In support of this, we analyze several algorithms for graph matching, adapted to our problem, and compare their performance on specific examples of context matching.
Contractile activity of the pectoralis in the zebra finch according to mode and velocity of flap-bounding flight

SUMMARY We studied flying zebra finch (Taeniopygia guttata, N=12), to provide a new test of a long-standing `fixed-gear' hypothesis that flap-bounding birds use only intermittent non-flapping phases, instead of variation in muscle activity, to vary mechanical power output in flight. Using sonomicrometry and electromyography, we measured in vivo fascicle length and neuromuscular recruitment in the pectoralis as the birds flew in different flight modes (level, ascending, descending; mean velocity 1.6±0.3 m s–1) and across velocities in a new, variable-speed wind tunnel (0–12 m s–1). Synchronized high-speed digital video (250 Hz) provided a record of wing kinematics. Flight mode had a significant effect upon pectoralis strain, strain rate, fractional shortening and the relative timing of muscle activity (onset, offset and duration). Among flight velocities, we observed significant variation in pectoralis strain, fractional lengthening and shortening, strain rate, relative electromyographic (EMG) amplitude, and EMG duration and offset. In particular, variation in strain rate and relative EMG amplitude indicates that the fixed-gear hypothesis should be rejected. Instead, it appears that zebra finch vary work and power output within wingbeats by modulating muscle contractile behavior and between wingbeats using intermittent bounds. Muscle activity patterns and wing kinematics were similar between free flight and wind tunnel flight at similar speeds. Comparing flights with and without surgically implanted transducers and electrodes, zebra finch exhibited a reduction in maximum velocity (from 14 to 12 m s–1) and a significant increase in wingbeat frequency and percent time flapping. This identifies a potential limitation of in vivo flight measurements, and similar studies of bird flight should, therefore, include measurements of the extent to which flight performance is compromised by experimental protocol.
An Empirical Analysis of Data Requirements for Financial Forecasting with Neural Networks

Neural networks have been shown to be a promising tool for forecasting financial time series. Several design factors significantly impact the accuracy of neural network forecasts. These factors include selection of input variables, architecture of the network, and quantity of training data. The questions of input variable selection and system architecture design have been widely researched, but the corresponding question of how much information to use in producing high-quality neural network models has not been adequately addressed. In this paper, the effects of different sizes of training sample sets on forecasting currency exchange rates are examined. It is shown that those neural networks-given an appropriate amount of historical knowledge-can forecast future currency exchange rates with 60 percent accuracy, while those neural networks trained on a larger training set have a worse forecasting performance. In addition to higher-quality forecasts, the reduced training set sizes reduce development cost and time.
Dramatic Activation of Antibiotic Production in Streptomyces coelicolor by Cumulative Drug Resistance Mutations

ABSTRACT We recently described a new method to activate antibiotic production in bacteria by introducing a mutation conferring resistance to a drug such as streptomycin, rifampin, paromomycin, or gentamicin. This method, however, enhanced antibiotic production by only up to an order of magnitude. Working with Streptomyces coelicolor A3(2), we established a method for the dramatic activation of antibiotic production by the sequential introduction of multiple drug resistance mutations. Septuple and octuple mutants, C7 and C8, thus obtained by screening for resistance to seven or eight drugs, produced huge amounts (1.63 g/liter) of the polyketide antibiotic actinorhodin, 180-fold higher than the level produced by the wild type. This dramatic overproduction was due to the acquisition of mutant ribosomes, with aberrant protein and ppGpp synthesis activity, as demonstrated by in vitro protein synthesis assays and by the abolition of antibiotic overproduction with relA disruption. This new approach, called “ribosome engineering,” requires less time, cost, and labor than other methods and may be widely utilized for bacterial strain improvement.
D-FW: Communication efficient distributed algorithms for high-dimensional sparse optimization

We propose distributed algorithms for high-dimensional sparse optimization. In many applications, the parameter is sparse but high-dimensional. This is pathological for existing distributed algorithms as the latter require an information exchange stage involving transmission of the full parameter, which may not be sparse during the intermediate steps of optimization. The novelty of this work is to develop communication efficient algorithms using the stochastic Frank-Wolfe (sFW) algorithm, where the gradient computation is inexact but controllable. For star network topology, we propose an algorithm with low communication cost and establishes its convergence. The proposed algorithm is then extended to perform decentralized optimization on general network topology. Numerical experiments are conducted to verify our findings.
Tracking a coordinated group using expectation maximisation

A common assumption in multiple target tracking is each target moves independently of other targets; however, there are cases where targets travel in a group. In these cases, there are dependencies between the targets; consequently, group target tracking should be used. We investigate efficient approximate methods to produce real-time group target tracking algorithms. Specifically, an algorithm based on expectation maximisation (EM) and belief propagation (BP) is introduced to track a single group of targets where the number of targets is known. The group expectation maximisation belief propagation (GEMBP) filter is compared to the joint probabilistic data association (JPDA) filter, the probabilistic multiple hypothesis tracker (PMHT) and the group PMHT for scenarios involving missed detections and clutter. We introduce a variant of the optimal subpattern assignment (OSPA) metric that includes a penalty for track swaps; results show that the GEMBP filter exhibits fewer track swaps than the JPDA filter.
Key Technologies for Green Data Center

With the repaid development of multi-media and network, information are generated and spread quickly than before. The industry is expected to meet the needs by producing high performance CPU and high-density disk. But dramatic increase in energy requirements becomes a new challenging. Energy and human cost become the major parts of data center. More and more vendor provides efficiency products, such as MAID(Massive array idle disk). SPEC has developed a power benchmark for single server (SPECPowerssj2008), SNIA has developed a green storage measure standard. In this paper, we summary several technologies to reduce energy consume of data center. These technologies can be divided into three levels: power and refrigeration, green components, intelligence energy management. At the last part of paper, we introduced the test standard of energy usage of server and storage.
Psychometric Properties of the Reactive-Proactive Aggression Questionnaire Among a Sample of Detained and Community Girls

Research on adolescent aggression often distinguishes between reactive and proactive aggression, but there is limited knowledge regarding the application or measurement of these subtypes of aggression among female youth, especially forensic samples of female youth. Drawing on 377 girls (103 selected from forensic settings and 274 selected from school settings) from Portugal, the current study is the first to simultaneously examine the psychometric properties of the Reactive-Proactive Aggression Questionnaire (RPQ) in incarcerated girls and community girls. The results support the use of the RPQ in terms of its two-factor structure, measurement invariance, internal consistency, convergent validity, discriminant validity, and known-groups validity. Significant associations were found with several criterion-related variables such as age of criminal onset, age of first problem with the law, conduct disorder, crime severity, violent crimes, and alcohol and drug use. Findings are discussed in terms of the use of the RPQ among incarcerated girls and community girls.
Traffic Network Equilibrium and the Environment : A Multicriteria Decision-Making Perspective

This paper develops a traffic network equilibrium model in which the users or travelers on the network are assumed to be multicriteria decision-makers with an explicit environmental criterion. The members of a class of traveler perceive their generalized cost on a route as a weighting of travel time, travel cost, and the emissions generated. The model allows the weights to be not only class-dependent but also link-dependent. The multiclass, multicriteria network equilibrium conditions are shown to satisfy a finite-dimensional variational inequality problem. Qualitative properties of the solution are obtained. A special case of the model is then used to obtain sharper results and to illustrate the relationship between the weights and the attainment of a desired environmental quality standard. An algorithm is proposed for the computation of the equilibrium pattern, along with convergence results, and then applied to solve a numerical example. The multiclass, multicriteria network equilibrium model is the first to incorporate an environmental criterion.
Linvail: A General-Purpose Platform for Shadow Execution of JavaScript

We present Linvail, a novel instrumentation platform for developing dynamic analyses of JavaScript programs. Linvail is particularly well-suited to implementing shadow executions which involve tagging runtime values with analysis-specific data. In contrast to existing instrumentation platforms, Linvail is capable of tracking both tagged objects and tagged primitive values during their entire life-time in a behavior-preserving manner. To demonstrate the expressiveness of our platform, we present the implementation of several state-of-the-art analyses. Our experiments demonstrate that Linvail's accuracy comes at the price of a performance overhead, but we believe that real-world applications will remain usable under analysis.
Malicious web script-based cyber attack protection technology

Recent web-based cyber attacks are evolving into a new form of attacks such as private information theft and DDoS attack exploiting JavaScript within a web page. These attacks can be made just by accessing a web site without distribution of malicious codes and infection. Script-based cyber attacks are hard to detect with traditional security equipments such as Firewall and IPS because they inject malicious scripts in a response message for a normal web request. Furthermore, they are hard to trace because attacks such as DDoS can be made just by visiting a web page. Due to these reasons, it is expected that they could result in direct damages and great ripple effects. To cope with these issues, in this article, a proposal is made for techniques that are used to detect malicious scripts through real-time web content analysis and to automatically generate detection signatures for malicious JavaScript.
QoS Provisioning issues in Wireless Multimedia Sensor Networks

Increasing demand of imaging and video sensors in many applications of wireless sensor networks has led to the evolution of Wireless Multimedia Sensor Networks (WMSN). WMSN are wireless sensor networks that are able to retrieve multimedia content from the environment. WMSN applications require effective communication of event features in the form of multimedia such as audio, image, and video. The challenges faced in WMSN are same as Wireless Sensor Networks (WSN) but some additional challenges such as reliability, QoS, and high bandwidth demands, must be addressed as well. The main focus of research so far is on minimizing the energy consumption in WSNs, mechanisms to efficiently deliver application-level QoS, and to map these requirements to network-layer metrics such as latency and jitter, have not been primary concerns of research on sensor networks. Therefore, for fulfilling real- time requirements for multimedia delivery of data, new protocols need to be developed which not only ensure bounded response time but also strive to minimize energy consumption in data processing and communication. The QoS requirements in WMSN are quite different from traditional networks. In this paper we will discuss various QoS issues in network layer in WMSN and explain the reasons for the failure of traditional QoS models in WMSN. We review the work being carried out in provisioning QoS in WMSN followed by some open research issues.
Posterior Urethra Rupture: Contrast-Enhanced Computed Tomography Scan and Urethrocystography Demonstrations

In the follow-up study of patients with pelvic fractures, rupture of the posterior urethra is registered in 3–25% of cases (Koraitim et al., 1996). The diagnostic gold standard for the assessment of hemodynamically stable trauma patients is contrast-enhanced CT scan, especially helical CT. Nevertheless, simultaneous suprapubic cystography and ascending urethrograms (the so-called up-and-downogram) are the investigation of choice in assessing the site, severity, and length of urethral injuries. (Carlin and Resnick, 1995) This paper discusses the evaluation and diagnosis of urethral injury in multiple-trauma patient.