List all Reports for an Institute or Center for a particular year
All Annual Reports for NIMH for reporting year 2017
97 Annual Reports
Investigator-Initiated Project
1.
MH000478 (2017) Neural Mechanisms of Learning and Memory in Vision
Mortimer Mishkin, PhD
The essential circuit for both item and associative stimulus recognition in any given sensory modality consists of the relevant cortical sensory processing stream(s), the medial temporal rhinal cortex, the ventromedial prefrontal cortex, and the medial dorsal nucleus of the thalamus. Context-free re ...
2.
MH000889 (2017) Dysregulation of Protein Synthesis in Fragile X Syndrome and Other Developmental Disorders
Carolyn E Beebe, PhD
The protocol involved in this project is 06-M-0214, NCT00362843.
During the 2017 funding period, we addressed the following: 1) rCPS measured with the L-1-C-11leucine PET method in patients with FXS and healthy volunteers studied either under dexmedetomidine sedation or in the awake state, 2) Eff ...
3.
MH001090 (2017) Studies of Central Nervous System Functional Anatomy
Miles Herkenham, PhD
Chronic psychosocial stress has been implicated in the etiology and progression of psychiatric disorders such as major depression and post-traumatic stress disorder (PTSD). In mice, we study the effects of psychosocial stress in a conflict paradigm that leads to behavioral alterations such as defici ...
4.
MH001101 (2017) Neural Mechanisms of Learning and Memory in Audition
Mortimer Mishkin, PhD
Several diverse pieces of evidence suggest the possibility that auditory long-term memory may require the the oromotor system. The first set of findings comes from a series of studies carried out on the KE family, half of whose members suffer from an inherited speech and language disorder. The core ...
5.
MH002032 (2017) Neural Coding of Visual Stimuli
Barry J Richmond, MD
At the highest level of vision, perceiving complex objects, and distinguishing among those that overlap in appearance, e.g., we quickly distinguish any red tomato from any red apple, regardless of the variety, is done seemingly with ease. A half-century of behavioral, anatomical and physiological re ...
6.
MH002035 (2017) Functional Anatomy of Perceptual and Attentional Systems in the Primate Brain
Leslie G Ungerleider, PhD
A typical scene contains many different objects that compete for neural representation due to the limited processing capacity of the visual system. At the neural level, competition among multiple objects is evidenced by the mutual suppression of their visually evoked responses. The competition among ...
7.
MH002039 (2017) Pharmacology of Stimulus Memory and Habit Formation
Mortimer Mishkin, PhD
The memory impairment associated with Alzheimers disease has long been associated with a loss in acetylcholine function and pathophysiology of the entorhinal/perirhinal, or rhinal cortex. In monkeys, lesions of this region as well as cholinergic deafferentation lead to severe memory deficits. We rec ...
8.
MH002386 (2017) Chemical Coding of Neurotransmission
Lee E Eiden, PhD
The neuropeptide slow transmitters PACAP (pituitary adenylate cyclase activating polypeptide) and arginine vasopression (AVP) are released at synapses that transduce stress responses to the brain, and mediate homeostatic and allostatic adjustments to stress by the organism. The catecholamine transmi ...
9.
MH002497 (2017) Functional Organization of the Cerebral Cortex and Basal Ganglia
Charles R Gerfen, PhD
Advances in molecular genetic techniques are revealing new details of the neuroanatomical organization of brain circuitry and the functional role of these circuits in behavior. Engineered viral vector constructs have been developed to label axonal projections of targeted neurons with unprecedented c ...
10.
MH002498 (2017) Roles of Oxytocin and Vasopressin in Brain
Walter Scott Young, MD, PhD
--We continued our studies of the CA2 region of the hippocampus where the Oxtr and Avpr1b are located. The CA2 area is an important, although relatively unexplored, component of the hippocampus. We are studying the role of various receptors and in the CA2 by crossing floxed receptor lines with Oxtr ...
11.
MH002537 (2017) Psychobiology and Treatment of Perimenopausal Mood Disorders
Peter J Schmidt, MD
This report includes work arising from the following clinical protocols: NCT00030147, NCT00060736, NCT00001231, and NCT00001322.
The menopause transition is accompanied by a decrease in quality of life (QOL) measures for some women, and several randomized controlled trials of menopausal hormone th ...
12.
MH002581 (2017) Childhood Onset Schizophrenia
Judith L Rapoport, MD
In comparison with adult onset schizophrenia patients, childhood onset patients have a greater number of pre-psychotic developmental disorders. One fifth of our COS cases have earlier forms of autism spectrum disorder, and one third have motor, speech, language, attention or impulse related developm ...
13.
MH002588 (2017) The Neural Foundations for Memory and Social Cognition In The Human Brain
Alex Martin, PhD
This past year we have continued to focus on a major part of long-term memory, termed semantic memory, that is composed of general information, such as facts, ideas, and the meaning of objects and words. We are particularly interested in characterizing the neural substrate mediating object and word ...
14.
MH002619 (2017) Neural Mechanisms of Motivation and Reward
Barry J Richmond, MD
To be effective in modulating behavior information arising in the cerebral cortex about the value of the behavior must pass through the subcortical striatum. It is known from many experiments including ours that the orbitofrontal cortex (OFC) is important for distinguishing among rewards with differ ...
15.
MH002652 (2017) Imaging of Neuropsychiatric Disorders with Developmental and Genetic Mechanisms
Karen F Berman, MD
The core aim of this project is to elucidate the nature, molecular foundations, underlying neurochemistry, and clinical correlates of neural systems-level dysfunction in schizophrenia. Toward that end, this year, the Clinical and Translational Neuroscience Branch has successfully executed comprehens ...
16.
MH002666 (2017) Evaluation and Treatment of Obsessive Compulsive and Related Disorders
Susan Elizabeth Swedo, MD
Obsessive-compulsive disorder (OCD) affects 1-2% of children and adolescents, causing significant distress and impairments from the unrelenting obsessional thoughts and compulsive behaviors. Approximately 1/4 of children with OCD report an abrupt, dramatic onset of their symptoms. When these sympto ...
17.
MH002717 (2017) Neuroimaging of Brain Circuits and Molecular Mechanisms in Normal Cognition
Karen F Berman, MD
The Clinical and Translational Neuroscience Branch continues to make advances on several fronts in order to delineate the neurochemical, neurogenetic, and neuropsychological contributions to neural systems function and development relevant to mental illness. We have embarked on data collection for t ...
18.
MH002736 (2017) Neural Substrates of Stimulus Recognition and Association Memory
Elisabeth Adams Murray, PhD
The prevailing model of amnesia holds that it depends on damage to a group of loosely related structures, collectively called the medial temporal lobe (MTL). One influential theory holds that these areas work together as a single functional unit. According to this theory, the structures in the MTL s ...
19.
MH002768 (2017) Subcellular Compartmentalization of Neuronal Gene Expression
Barry Bernard Kaplan, PhD
Although once highly controversial, it is now well-accepted that there exists a diverse population of mRNAs and non-coding microRNAs in the distal structural/functional domains of the neuron which include the dendrite, axon, and presynaptic nerve terminal. It has also become well-established that p ...
20.
MH002778 (2017) The Phenomenology and Neurophysiology of Juvenile Bipolar Disorder
Ellen Leibenluft, MD
Since the beginning of this project we have screened over 3000 subjects for this study. We have enrolled approximately 215 youth with bipolar disorder (BD), 320 subjects at risk for BD because they have a parent or sibling with the illness, and 120 adults with BD. This year, approximately 15 new sub ...
21.
MH002781 (2017) fMRI Studies Of Pediatric Mood And Anxiety Disorders
Daniel Pine, MD
This work is conducted under protocol 01-M-0192, NCT00018057.
We are using functional magnetic resonance imaging (fMRI) to examine neurocognitive correlates of pediatric anxiety disorders. Through ongoing collaborative studies, we also have increasingly compared findings in these syndromes with fin ...
22.
MH002782 (2017) fMRI Studies Of Risk For Mood And Anxiety Disorders In Children
Daniel Pine, MD
This work is conducted under protocol 03-M-0186, NCT00060775.
In this project, our group is examining biological aspects of risk for mood and anxiety disorders in children. Such work has major public impact. By identifying biological risks in children, information on novel treatments and preventati ...
23.
MH002783 (2017) Functional MRI Method Development
Peter A Bandettini, BS, PhD
Protocol number 93-M-0170, NCT00001360
The Section on Functional Imaging Methods (SFIM) has advanced functional MRI (fMRI) methodology through development of processing and acquisition methods as well as research on the underlying mechanisms behind the fMRI signal. Our work over the past year has ...
24.
MH002784 (2017) Neurogenesis in the Adult Brain
Heather A Cameron, PhD
This project focuses on the interactions between experience and adult neurogenesis in the hippocampal region of the brain. We are interested in understanding how experiences, including learning and stressful experiences, regulate adult neurogenesis and how the new neurons alter responses in these si ...
25.
MH002786 (2017) The Pathophysiology and Treatment of Children with Severe Irritability
Ellen Leibenluft, MD
Given concerns about the appropriate diagnosis for children with chronic, severe irritability, we defined the syndrome of severe mood dysregulation(SMD)to capture youth with extremely severe irritability and symptoms of hyperarousal. Our SMD phenotype formed the basis for the new pediatric diagnosis ...
26.
MH002793 (2017) PET Radiopharmaceutical Sciences
Victor W Pike, PhD
The Molecular Imaging Branch (MIB) aims to exploit positron emission tomography (PET) as a radiotracer imaging technique for investigating neuropsychiatric disorders, such as autism, depression, addiction, schizophrenia, and Alzheimer's Disease (AD). Fundamental to this mission is the development of ...
27.
MH002795 (2017) Translational Evaluation of PET Radiotracers
Robert Innis, MD
Examples of progress made during the prior year are summarized below.
1. 11C-(R)-rolipram PET detects Disc1 inhibition of phosphodiesterase type 4 (PDE4) in living Disc1 locus-impaired mice
Phosphodiesterase type 4 (PDE4), a key enzyme that metabolizes second messenger cyclic adenosine monophosp ...
28.
MH002797 (2017) Neuronal Avalanches in the Neocortex
Dietmar Plenz, PhD
1. Critical brain dynamics are increasingly recognized to be important in cortex function. We have expanded on our previous finding of neuronal avalanches maintained during the resting state. We now have demonstrated the first study on linking deviations in avalanche dynamics with basal ganglia dy ...
29.
MH002798 (2017) The Psychophysiology of Fear and Anxiety
Christian Grillon, PhD
This project has identified core psychological and behavioral findings and their underlying neural mechanisms related to anxiety and anxiety disorders. Using a high-resolution 7 Tesla scanner, we have mapped the functional connectivity of small structures that are key nodes of the brain circuits und ...
30.
MH002800 (2017) Identifying Neural Substrates of Behavior in Drosophila Melanogaster
Benjamin H White, PhD
Insect ecdysis sequences represent a simple, robust, and tractable model for studying the neuromodulatory mechanisms that govern behavior. Because initiation of an ecdysis sequence involves a profound shift in behavioral priorities, study of these sequences offers the opportunity to understand the n ...
31.
MH002803 (2017) Magnetic Resonance Spectroscopy and Imaging Studies of Brain Functions
Jun Shen, PhD
The goal of this research is to develop advanced magnetic resonance spectroscopy (MRS) and imaging techniques and to apply them and other complementary methods to studying brain metabolism, neurotransmission and enzyme activity. MRS allows measurement of the neurotransmission of glutamate and GABA i ...
32.
MH002804 (2017) Family Study of Affective and Anxiety Spectrum Disorders
Kathleen R Merikangas, PhD
The major aims of this project have been accomplished through the establishment of a large community-based family study of adult probands who participate in comprehensive clinical and biologic assessments followed by comparable evaluations of their adult and child relatives. Recruitment of probands ...
33.
MH002810 (2017) Functional Genomics of Bipolar Disorder
Francis Joseph McMahon, MD
NCT00001174
In order to better interpret the impact of genetic variation on the brain biology of bipolar disorder, we are pursuing a variety of functional genomics studies, including brain imaging, RNA-sequencing in post-mortem brain tissue, and cellular phenotyping of neurons derived from induce ...
34.
MH002838 (2017) Neurophysiology of Visual Perception
David A Leopold, PhD
To understand our vision, it is critical to place humans within an appropriate phylogentic context: humans are primates, and their dominant use of vision is typical of primates and different from other mammals. As a sensing faculty, vision places individuals at a great advantage over their environm ...
35.
MH002843 (2017) Identification of Genes Involved in Major Mood Disorders
Francis Joseph McMahon, MD
NCT00001174
Starting in 1993, in collaboration with 10 academic centers across the US, we recruited a large sample of over 3,000 individuals with bipolar or other mood disorders. All participants did a diagnostic interview and provided a blood sample for DNA analysis. DNA and clinical data are av ...
36.
MH002844 (2017) Genetic Determinants of Antidepressant Response
Francis Joseph McMahon, MD
NCT00088699
Strong evidence from family and twin studies demonstrates that major depressive disorder (MDD) is heritable, yet there has been limited progress in identifying the actual genes involved. A separate, perhaps overlapping set of genes is expected to play a role in individual variation in ...
37.
MH002852 (2017) Clinical Evaluation of PET Radiotracers
Robert Innis, MD
Examples of progress made during the prior year are summarized below.
1. Building a database for brain 18kDa translocator protein (TSPO) imaged using 11CPBR28 in healthy subjects
Translocator protein 18 kDa (TSPO) has been widely imaged as a marker of neuroinflammation using several radioligands ...
38.
MH002857 (2017) Glutamatergic Modulators for Rapid and Sustained Antidepressant Effect
Carlos Alberto Zarate, MD
This Report involves work collected under protocols 04-M-0222 (NCT00088699) and 17-M-0060 (NCT03065335).
Our research suggests that the glutamatergic system is involved in the mechanism of action of rapid antidepressant response. In addition, this system may be a feasible target for developing tr ...
39.
MH002863 (2017) Multimodal Neuroimaging of Gene-Brain Relationships in Williams Syndrome
Karen F Berman, MD
The Clinical and Translational Neuroscience Branch continues to work toward discovery of novel genetic contributions to brain structure, function, and clinically relevant behavior and cognition, through a series of ongoing multimodal neuroimaging studies of individuals with copy number variation in ...
40.
MH002865 (2017) Reproductive Endocrine Related Mood Disorders-Differential Sensitivity
Peter J Schmidt, MD
This report includes work arising from the following clinical protocols: NCT00005011, NCT00056901, NCT00059228, NCT00082043, NCT00100360, NCT00001177, NCT00001259, and NCT00001481.
Overlapping co-morbidities between premenstrual dysphoric disorder (PMDD) and postpartum depression (PPD) have led to ...
41.
MH002868 (2017) Clinical and Behavioral Phenotyping of Autism and Related Disorders
Susan Elizabeth Swedo, MD
Autism spectrum disorder is currently defined as a single neurodevelopmental disorder characterized by impairments in social communication and the presence of restricted interests and repetitive behaviors. However, there is mounting evidence that autism spectrum disorder represents a collection of o ...
42.
MH002874 (2017) The Neuroregulatory Effects of Gonadal Steroids in Humans
Peter J Schmidt, MD
This report includes work arising from the following clinical protocols: NCT00026832, NCT00100360, NCT00001177, and NCT00001322.
Previous studies have identified the presence of abnormalities in resting state activity and reward processing in mood disorders. Indeed, altered resting state function ...
43.
MH002881 (2017) Molecular Mechanisms of Synapse Development and Plasticity
Zheng Li, PhD
Synapses can change their efficacy through synaptic plasticity. Long-lasting forms of synaptic plasticity (such as long-term potentiation and long-term depression of synaptic transmission) are important cellular mechanisms underlying information storage in the brain and the establishment of proper n ...
44.
MH002882 (2017) Characterizing the Function of miRNAs in Neural Development, Synaptic Plasticity and Schizophrenia.
Zheng Li, PhD
miRNAs have been implicated in schizophrenia. Mutations in genes encoding miRNAs or components in the miRNA biogenesis machinery are associated with increased risk for schizophrenia. It is hypothesized that miRNA expression is aberrant in schizophrenia patients. Using microarray and quantitative PCR ...
45.
MH002886 (2017) Neural mechanisms of reward processing and emotion
Elisabeth Adams Murray, PhD
The amygdala, OFC and MFC contribute to learning, in part, by evaluating feedback. In an effort to extend our understanding of the physiological mechanisms underlying affective processing, we developed an fMRI paradigm to reveal blood-oxygen-level dependent (BOLD) responses to visual images that sig ...
46.
MH002887 (2017) Neural Substrates of Reward Processing and Emotion
Elisabeth Adams Murray, PhD
OFC is thought to be essential for at least two aspects of stimulus-reward learning: 1) representing the current value of anticipated outcomes and 2) learning to choose based on the relationship between particular choices and the probability of receiving a reward. These two aspects might be consider ...
47.
MH002890 (2017) Integrating EEG/MEG and fMRI
Richard Coppola, SB, DSc
Our earlier basic studies have shown that MEG recordings during cognitive tasks have the ability to localize brain activity in comparable fashion to functional MRI. In specific, reduction in power in the beta frequency band at the cortex in general agrees with BOLD activation results. However, elect ...
48.
MH002893 (2017) Learning and plasticity in the human brain
Christopher Ian Baker, PhD
The primary goals of this research are i) to establish how learning impacts the structure and function of the brain, and ii) to determine how learning can be modulated by factors such as feedback (positive or negative). Over the past year we have focused equally on both of these goals.
1) Impact ...
49.
MH002896 (2017) The Neural Basis of Functional MRI Responses
David A Leopold, PhD
Nearly everybody has seen maps of brain activity generated by functional MRI (fMRI). This method, which at its core measures blood flow-based magnetic effects on water molecules, allows researchers to map out areas of the brain based on sensory responses, motor actions, and even complex cognitive t ...
50.
MH002897 (2017) Molecular and Cellular Mechanisms of Experience-Dependent Cortical Processing
Kuan Hong Wang, PhD
Our brain can be considered as a type of information processing system like a computer, where input signals need to be first detected and properly represented, then integrated for decision-making and output control. A unique feature of the brain as an information processing system lies in its adapt ...
51.
MH002898 (2017) Social Processing and Neural Plasticity
David A Leopold, PhD
Neuroscientists studying the brain use stimuli with different levels of complexity to study the neural mechanism of sight. These stimuli range from dots and lines, to faces and bodies. Decades of investigation using electrophysiological and functional imaging methods have revealed that the brains ...
52.
MH002909 (2017) Object, face, body and scene representations in the human brain
Christopher Ian Baker, PhD
The goal of this research is to understand how we see what we see: how does the brain analyze the light falling on the retina of the eye to reveal a world full of objects, people and things?
During the past year we have focused on the perception of complex visual stimuli, in particular real-worl ...
53.
MH002917 (2017) Office of the Scientific Director
Susan G Amara, PhD
The NIMH Division of Intramural Research Programs' (IRP) scientists conduct research ranging from studies into the mechanisms of normal brain function at the behavioral, systems, cellular, and molecular levels, to clinical investigations into the diagnosis, treatment and prevention of mental illness ...
54.
MH002918 (2017) Functional Anatomy of Face Processing in the Primate Brain
Leslie G Ungerleider, PhD
Lesions of inferior temporal (IT) cortex in humans can result in the syndrome termed prosopagnosia, an inability to recognize familiar faces. Single-cell recordings from IT cortex of monkeys have revealed the existence of neurons that are selectively activated by visual images of faces. Additionally ...
55.
MH002920 (2017) The Cognitive Neuroscience of Autism Spectrum Disorders
Alex Martin, PhD
This past year we have continued to evaluate the neural characteristics and associated behavioral consequences of Autism Spectrum Disorders (ASD).
Prevailing theories suggest that ASD results from impaired brain communication due to aberrant timing or coordination of neuronal firing patterns (im ...
56.
MH002922 (2017) Psychiatry Consultation Liaison Service Research
Maryland Pao, MD
The three areas of OCD research are:
Suicide Risk in the Medical Setting
Suicide is an international public health threat. In the U.S., it is the 2nd leading cause of death for youth and the 10th leading cause of death for adults. The Director of NIMH, Dr. Joshua Gordon has made suicide preventi ...
57.
MH002927 (2017) Neurobiology and Target Validation of Novel Therapeutic Agents in Mood Disorders
Carlos Alberto Zarate, MD
This Report involves work collected under protocols 01-M-0254 (NCT00024635 ); 08-M-0196 (NCT00759395); 08-M-0150 (NCT00697268); 14-M-0085 (NCT02122562); 07-M-0021 (NCT00397111); 14-M-0041 (NCT02049385); and 07-M-0152 (NCT00472576), 09-M-N230; 15-M-0151 (NCT 02484456), 15-M-0188 (NCT02543983).
Res ...
58.
MH002928 (2017) Frontal Striatal Systems and Dopamine in Normal and Pathological Behavior
Bruno B Averbeck
Our lab uses a combination of neurophysiology, pharmacology and imaging to understand how a set of brain areas composed of the amygdala, ventral-striatum, prefrontal cortical areas and dopamine underlie learning. We specifically study learning to predict whether choices tend to lead to good or bad ...
59.
MH002933 (2017) Endocrine and Neurobiologic Events Accompanying Puberty
Peter J Schmidt, MD
This report includes work arising from the following clinical protocol: NCT01434368.
We have recruited approximately half of the proposed sample, and several of the children in the original 12-13 year-old cohort have completed the study through to their 18th birthday. To date we have studied 44 pr ...
60.
MH002935 (2017) Mathematical and Statistical Analysis Techniques for in Vivo Imaging Studies
Carolyn E Beebe, PhD
In the current year we have refined our data analysis methods for estimation of rates of cerebral protein synthesis (rCPS) with L-1-C-11leucine and PET. Effects on rCPS with changes in physiological and even pathological states are expected to be modest, so it is essential that the analyses achieve ...
61.
MH002936 (2017) Cerebral Protein Synthesis During Sleep and Memory Consolidation
Carolyn E Beebe, PhD
The protocol involved in this project is 09-M-0123, NCT00884702.
During the FY 2017 funding period, we addressed the following:
In collaboration with our colleagues from the Walter Reed Army Institute of Research (T Balkin) and NINDS (D. Picchioni), we applied the L-1-C-11leucine PET metho ...
62.
MH002942 (2017) Multimodal Imaging: Genetic and Environmental Effects in Neuropsychiatry
Karen F Berman, MD
In order to better understand how schizophrenia risk genetics operate at the cognitive and neural systems level, our recent work under this project has focused on (1) identifying novel phenotypes that either (a) distinguish both people with schizophrenia and unaffected family members from unrelated ...
63.
MH002946 (2017) Structure, Function and Pharmacology of Neurotransmitter Reuptake Systems
Susan G Amara, PhD
Our laboratory has shown that amphetamines trigger the internalization of the dopamine transporter (DAT) by a series of intracellular events that are distinct from the generally established actions of amphetamines to inhibit dopamine (DA) uptake or to increase DA efflux. We have found that when appl ...
64.
MH002948 (2017) Cerebral Protein Synthesis as a Measure of Degenerative Changes in a Transgenic Rat Model of Alzheimer's Disease
Carolyn E Beebe, PhD
During the 2017 funding period, we completed studies of rCPS with the autoradiographic L-1-C-14leucine method in cohorts of WT and TgF344-AD at six, 14, and 18 months of age. In these animals, we also measured activity by means of open field behavior, memory by means of the T-maze, olfaction by mean ...
65.
MH002949 (2017) Developmental Neurogenomics Unit
Armin Raznahan
This work is conducted under protocol 89-M-0006 (NCT00001246), and falls into two broad research themes:
Theme 1: Studies of behavior and brain organization in typically-developing groups
Recruitment
During the last year, we completed phase 1 of a Bring Back Project which recruited individuals ...
66.
MH002950 (2017) Dissecting the stress circuits of the paraventricular nucleus of the thalamus
Mario Alexander Penzo
This fiscal year we made substantial progress in characterizing the various neuronal populations of the PVT. For this purpose, we combined anatomical tracings, mouse genetics and viral vector methodologies, and identified three potential subclasses of PVT neurons. In particular, a subpopulation of P ...
67.
MH002951 (2017) Neural Circuits underlying the control of Executive and Emotional Behavior
Yogita Chudasama, PhD
In recent years, the work in my lab and that of other investigators has revealed that so-called prefrontal function, in fact, involves a broad network of areas including not only the prefrontal cortex but also the hippocampus, basal ganglia, and certain thalamic nuclei. These structures are anatomic ...
68.
MH002953 (2017) Population-Based Epidemiologic Research
Kathleen R Merikangas, PhD
During the past 12 months, we have used NCS-A data to investigate several important research questions that may impact nosology, clinical practice, and intervention strategies. We examined the clinical correlates of disruptive mood dysregulation disorder (DMDD) and found that this diagnosis is pres ...
69.
MH002954 (2017) Motor Activity Research Consortium for Health (mMarch)
Kathleen R Merikangas, PhD
There is growing interest in studying the environmental, biologic and genetic correlates of the components of motor activity as well as the relationships between motor activity with sleep, exercise, mood, and cognitive functioning. Aggregation of the findings across these studies is complicated by t ...
70.
MH002955 (2017) Non-invasive Neuromodulation Unit (NNU)
Sarah Hollingsworth Lisanby
Work was conducted under clinical protocols NCT03065335 and NCT03289923.
Major accomplishments for the NNU were the recruitment of additional staff, acquiring research data from our first subjects in our collaboration with the Experimental Therapeutics and Pathophysiology Branch (ETPB), launching ...
71.
MH002956 (2017) Patterns of neuronal activity underlying behavioral decisions
Mark H Histed, PhD
In one project, we are examining how particular sets of neurons are chosen by the brain to guide decisions. Whenever humans or animals see sensory stimuli, many brain areas are activated. We hypothesize that there are particular representations particular patterns that are created in each brain ...
72.
MH002957 (2017) Mood, Brain, and Development Unit
Argyrios Stringaris
Under NCT00025935, we have already screened over 150 patients with depression and have scanned over 35 participants with depression, other conditions, and healthy volunteers. We have analyzed existing data and have already published papers since the start of my tenure here. On the methodological si ...
73.
MH002958 (2017) Unit on Neurons, Circuits and Behavior
Arash Afraz, MD, PhD
We have just started our adventure. We are currently setting up a temporary physiology rig in our temporary space, hoping to -at least- start training of animals before the permanent lab space gets ready. The protocol for the research in NCB is submitted. Two postbacs are hired and two postdocs are ...
74.
MH002959 (2017) Circuit mechanisms underlying cortical communications
Soohyun Lee
In starting a new unit, we have been building infrastructure, designing and conducting our experiments, and collecting preliminary data. First, to achieve mechanistic understanding of the functional connectivity among different cortical areas, we have started systematically examining the synaptic st ...
75.
MH002964 (2017) Section on Light and Circadian Rhythms
Samer Saleh Hattar
Research projects carried during the Fiscal Year 2017
1- Contribution of rod/cone photoreceptors to non-image functions:
In 2010, we published that rods and melanopsin phototransduction are complementary for signaling light to circadian photoentrainment (Altimus et al., Nature Neurosci 2010) and r ...
76.
MH002965 (2017) Statistical Genomics and Data Analysis Core
Yin Y Yao, PhD
In FY2017, we finished approximately 14 requested services. For example, we completed an automated computer program in R language for one lab and helped staff to calculate the number of mutations in a systematic way. Moreover, we collaborated with another research team to finish a genome wide associ ...
77.
MH002966 (2017) Multiple Scales of Stimulus Representation in the Human Brain
Elisha Prem Merriam
During the past year, we have focused on 1) characterizing the representation of stimulus features in primary visual cortex, and 2) studying how these representations interact with endogenous brain states, such as spatial attention and arousal.
1) Characterizing stimulus representations in human ...
78.
MH002967 (2017) Computation by recurrent circuits of the cerebral cortex
Mark H Histed, PhD
In one project, we are developing models of spiking networks that relate neurons responses to circuit and cellular properties. The theoretical work is being done in collaboration with Nicolas Brunel (Duke), and we are testing predictions in the laboratory using neurophysiology with silicon multi-si ...
Scientific Core
1.
MH002884 (2017) Functional MRI Core Facility
Peter A Bandettini, BS, PhD
Protocol number 93-M-0170, NCT00001360
Space Utilization:
The Functional MRI Facility (FMRIF) currently occupies approximately 4800 sq. ft of space, divided between the scanner bays, control rooms and electronics/machine rooms for 3TA, 3TB, 3TC, 3TD, and 7T MRI scanners located within the NMR ...
2.
MH002888 (2017) Scientific and Statistical Computing Core
Robert Cox, PhD
The principal mission of the Core is to help NIH researchers with analyses of their fMRI (brain activation mapping) data. Along the way, we also help non-NIH investigators, mostly in the USA but also some abroad. Several levels of help are provided, from short-term immediate aid to long-term develop ...
3.
MH002889 (2017) NIMH MEG Core Facility
Richard Coppola, SB, DSc
The MEG Core staff works interactively with a large group of PIs in NIMH, NINDS, and NIDCD, and NICHD for study design, task development, acquisition protocols, signal processing and data analysis. Procedures for data security, transfer and storage have been improved by moving the main data archive ...
4.
MH002894 (2017) Magnetic Resonance Spectroscopy Core Facility
Jun Shen, PhD
As a noninvasive method for measuring the concentration and distribution of chemicals in the living brain, MRS is an important tool for studying brain function and disorders. However, acquiring robust measurement of MRS signals requires a sophisticated study design and the successful implementation ...
5.
MH002899 (2017) Neurophysiology Imaging Facility Core: Functional and Structural MRI
David A Leopold, PhD
There are multiple uses of magnetic resonance imaging (MRI) in the study of nonhuman primate neuroscience, each of which aims to make the research as precise and efficient, and as relevant to humans, as possible. The NIF facility at the NIH strives to make both structural and functional imaging fea ...
6.
MH002901 (2017) Transgenic Resources for Neuroscience Research
James M Pickel, AB, PhD
The NIMH transgenic core facility has several major functions: 1) to produce transgenic research animals for neuroscience research, 2) to support research with associated techniques in genetic research in neuroscience, 3) to develop new transgenic techniques and model systems and 4) to engage in col ...
7.
MH002903 (2017) Procurement and Characterization of Postmortem Brain Tissue
Barbara K Lipska, PhD
Currently, the HBCC resources include brains from individuals with the following diagnoses (numbers of subjects listed in parentheses): Bipolar disorder (161), Non-psychiatric controls (358), Schizophrenia and schizoaffective disorder (202), Substance Abuse (48), Major depressive disorder (228), Oth ...
8.
MH002911 (2017) NIMH Instrumentation Core Facility
George Raphael Dold, MME, BSEE
This past year, our Section had the unique opportunity to support the research of various Labs & Sections within NIMH, NINDS, NICHD, and NCCIH. During the past twelve months, investigators from these labs and branches requested 465 formal projects from our staff. Each of these requests was documente ...
9.
MH002952 (2017) Rodent Behavioral Core: The Analysis of Behavior
Yogita Chudasama, PhD
Behavioral neuroscience links systems-level circuitry to behavior, cognition and emotion and is thus critical for understanding the afflictions that affect neuropsychiatric patients. Linking cognitive changes in a behaving rat or mouse to targeted manipulations of neural circuitry requires the conve ...
10.
MH002960 (2017) Data Science and Sharing Team
Adam G Thomas, PhD
This is the first annual report for the Data Science and Sharing Team (DSST). In this past year, the team has been busy hiring staff and establishing infrastructure. Significant progress has also been made towards the goals and mission of the group.
The first data scientist for the group, John L ...
11.
MH002961 (2017) Neurodevelopmental and Behavioral Phenotyping
Audrey E Thurm, PhD
This past year the Neurodevelopmental and Behavioral Phenotyping Service conducted over 200 neurodevelopmental assessments and focused on collection and analyses of data from a variety of rare genetic disorders associated with intellectual disability and autism spectrum disorder. Much of the data an ...
12.
MH002962 (2017) Sleep and Neurodevelopment Service
Ashura Buckley
This past year the Sleep and Neurodevelopmental Service conducted over 120 overnight sleep studies ordered by nine different institutes. Seventy-three of those studies were performed in pediatric patients. We have collected sleep data on several distinct cohorts including children with childhood on ...
13.
MH002963 (2017) Systems Neuroscience Imaging Resource
Theodore B Usdin, MD, PhD
This is the first year of operation of the Systems Neuroscience Imaging Resource (SNIR). Much of the focus has been on equipment acquisition and procedure development.
The first piece of equipment specifically purchased for the SNIR is a Zeiss AxioscanZ1 slide scanning microscope. This is a high ...
Clinical Support Services
1.
MH002912 (2017) Office of the Clinical Director
Maryland Pao, MD
CLINICAL RESEARCH SUPPORT SERVICES: A central mission of the OCD is to ensure that research participants receive the highest quality clinical care. The OCD is responsible for oversight of all clinical aspects of the NIMH Intramural Research Program (NIMH IRP).
HUMAN SUBJECT PROTECTIONS: The mis ...
Clinical Training
1.
MH002939 (2017) Office of the Clinical Director-Psychiatry Clinical Research Training
Maryland Pao, MD
Clinical Fellowship Programs:
During FY17, there were seven NIMH IRP residents/fellows. The academic year generally starts in July so this number could include new, current and departing trainees in our programs. Of the seven trainees, three were in our PGY-4 ACGME accredited psychiatry residency ...
Animal Research Infrastructure
1.
MH002924 (2017) Veterinary Medicine Research Branch
James Raber, DVM, PhD
The NIMH Veterinary Medicine Research Branch (VMRB) has successfully provided veterinary medical, husbandry and research support to over twenty-six (26) principal investigators in the NIMH IRP with over fifty-four (54) active animal study protocols. In addition, the Branch has provided training and ...
New Space Activation & Commissioning
1.
MH002947 (2017) NIMH Space Activation and Commissioning
Susan G Amara, PhD
NIMH intramural scientists conduct research ranging from studies into the mechanisms of normal brain function at the behavioral, systems, cellular, and molecular levels, to clinical investigations into the diagnosis, treatment and prevention of mental illness. Researchers study conditions in adults ...
Education and Outreach
1.
MH002916 (2017) Office of Fellowship Training
Janet Elizabeth Clark
In fiscal year 2017 (FY17) OFT administered several ongoing educational events and programs for the IRP and established new training opportunities. The OFT processed personnel cases for approximately 359 fellows including 41 Visiting Fellows and 44 Special Volunteers. The OFT provided training to al ...
Technology Transfer
1.
MH002925 (2017) Office of Technology Transfer
Jennifer S Wong
The Office of Technology Transfer Office (OTT) negotiates Material Transfer Agreements, Cooperative Research Development Agreements (CRADA), Licenses, Collaboration Agreements, Confidential Disclosure Agreements, and Clinical Trial Agreements. OTT also provides guidance and assistance to NIMH resear ...


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{{ _('Send service updates and messages over email and SMS') }}
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{% extends "fullwidth_template.html" %} {% from "components/textbox.html" import textbox %} {% from "components/honeypot-field.html" import honeypotfield %} {% from "components/page-footer.html" import page_footer %} {% from "components/form.html" import form_wrapper %} {% from "components/radios.html" import radio %} {% from "components/select-input.html" import select, select_list, select_nested, select_wrapper, select_input %} {% block meta %} {% set description = _('Notify lets you send emails and text messages to your users') %} {% set keywords = _('gc notify, notify, canada notify, canada email, email notifications, Government of Canada, federal government, Canada SMS, Canada Notification service') %} {% set meta_title = _('GC Notify | GC Notification') %} {% endblock %} {% block page_title %} {{ _('Notification') }} {% endblock %} {% block maincolumn_content %}
{{ _('Try Notify if you work in the federal government.') }}
{{ _('The Notify dashboard lets you track the status of all your notifications and mailings. See at a glance the number of messages that have been received and opened.') }}
{{ _('Upload a spreadsheet containing the recipients email addresses or phone numbers, and Notify will send your message to them all in a batch.') }}
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We have a small number of high quality show goats available every show season.
See the statusNothing brings happiness like a baby goat pic or two. Follow us on social media to see the herd in their day to day life.
check us outRaising goats? You're not alone! Listen along as we document our ups and downs of goat breeding, and how we balance it with our every day life.
listen nowShow off your goat love with offical JB Racing Goat shirts, stickers, hats and more. Our Etsy store is now open to the public :)
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Average New Car Price Surpasses $34,000 For The First Time Ever
It's not exactly the car's fault.
by Chris OkulaThe average price of a new car reached a record-breaking $34,217 for October 2019. This incredible climb over the previous record of $33,924, which was set in December 2018, shows a steady increase in new car transaction prices even as sales volume continues to fall year over year. This begs the question of what’s driving these increased vehicle prices?
According to J.D. Power and LMC Automotive, the increased average transaction price has a lot to do with the vehicles customers prefer. In October 2019 an astonishing 72% of new car sales were actually SUVs and Trucks. The market share of SUVs and trucks is up 2.4% from October 2018 and shows the continued decline in actual car purchases.
Gallery: Volvo XC90 facelift
Another interesting data point uncovered by J.D. Power and LMC Automotive lies in the spread of purchases for different price points. “Sales of vehicles under $30,000, which accounts for 46% of the market, are down 7%, while sales of vehicles priced higher than $30,000 are up 6%.” Now, this does not mean that more people are buying luxury vehicles but instead further demonstrates the dominance of SUV sales. J.D. Power and LMC go on to further explain, “The average transaction price for cars (up 4% to $27,739) is rising at a faster rate than trucks/SUVs (up 3% to $36,474).”
With the average price of a car well under $30,000, and about $10,000 less than the average price of an SUV or truck, it’s clear new cars are not driving the average vehicle price up. Instead, the growing consumer demand for SUVs and Trucks is skewing the data toward that higher-end simply because an SUV usually has a higher starting price than a comparable car.
Top Truck and SUV News
When we see the average price of a new car in October 2019 was over $34,000 you may think everyone is buying a new BMW or Mercedes. Instead, they’re trading in the mid-sized sedans for an SUV and, for example, spending a bit more for an X5 over a 5-series.
Source: Cars Direct


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class PageVersionSource implements Source
Purpose of this class is to source out events that are page versions in disguise.
It also handles updating a page version event if the user has changed it on the front end calendar ...
Methods
|
__construct(DvsPageVersion $PageVersion, Framework $Framework)
Create a new PageVersionSource |
||
| array(StdClass) |
fetchEvents(string $start, string $end)
A Source must be able to fetch events. |
|
| StdClass |
updatePageVersion(int $id, string $start, string $end, boolean $published)
This is here so that we can update a page version event |
Details
at line 18
public
__construct(DvsPageVersion $PageVersion, Framework $Framework)
Create a new PageVersionSource
at line 32
public array(StdClass)
fetchEvents(string $start, string $end)
A Source must be able to fetch events.
On this source we treat page versions as events with start and end dates.
at line 62
public StdClass
updatePageVersion(int $id, string $start, string $end, boolean $published)
This is here so that we can update a page version event


{% load static %}
Lafayette Soccer Project - Upload File
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Source code for greengraph.test.test_mapClass
from mock import patch
from mock import MagicMock
import requests
import numpy as np
import os
from nose.tools import assert_almost_equal
from greengraph.map import *
defaultLat = 51.5073509
defaultLong = -0.1277583
[docs]def setup_module(module):
'''
This function is the first one to be executed in this module. It creates a synthetic image with no green pixels and saves it in a csv file to be accessed as a fixture in the functions.
'''
print('Setup module')
# create file with an image with no green pixels
mapImgR=np.ones((300,300))
mapImgG=np.zeros((300,300))
mapImgB=np.ones((300,300))
# savetxt only works with <2dim arrays
np.savetxt("greengraph/fixtDataRed.csv", mapImgR, delimiter=",")
np.savetxt("greengraph/fixtDataGreen.csv", mapImgG, delimiter=",")
np.savetxt("greengraph/fixtDataBlue.csv", mapImgB, delimiter=",")
[docs]def teardown_module(module):
'''
This function is called after everything else in this module. Its purpose is to close any connections to the internet or a database. In this case, it deletes the .csv files created in the setup_module() function.
'''
print('Teardown module')
########################
# # uncomment to delete the fixture data
# os.remove('greengraph/fixtDataRed.csv')
# os.remove('greengraph/fixtDataGreen.csv')
# os.remove('greengraph/fixtDataBlue.csv')
[docs]def test_defaultRequestParam():
'''
Test function to check that the http get request is called with the right parameters in the default case.
'''
with patch.object(requests,'get') as mock_getRequest:
# print('mock requests was called')
defaultMap= getMapAt(defaultLat,defaultLong)
params=dict(
sensor='false',
zoom=10,
size='400x400',
center=",".join(map(str, (defaultLat,defaultLong) )),
style='feature:all|element:labels|visibility:off'
)
params["maptype"]="satellite"
mock_getRequest.assert_called_with(
"http://maps.googleapis.com/maps/api/staticmap?",params=params)
[docs]def test_countGreen_nonGreenImg():
'''
Test to count the number of green pixels in a photo with no green areas. The image is read from the fixture .csv file created in the setup_module() function.
'''
myDataRed = np.genfromtxt('greengraph/fixtDataRed.csv', delimiter=',')
myDataGreen = np.genfromtxt('greengraph/fixtDataGreen.csv', delimiter=',')
myDataBlue = np.genfromtxt('greengraph/fixtDataBlue.csv', delimiter=',')
mapImg=np.dstack((myDataRed, myDataGreen, myDataBlue))
# create Map object with the test image
testMap=Map(defaultLat,defaultLong)
testMap.setDummyImage(mapImg)
print(str(testMap.count_green()))
assert_almost_equal (testMap.count_green(),0,delta=1e-5)
# @patch('greengraph.map.Map')
# def getMapMock(mockMap):
# print('patch decorator')
# myDataRed = np.genfromtxt('fixtDataRed.csv', delimiter=',')
# myDataGreen = np.genfromtxt('fixtDataGreen.csv', delimiter=',')
# myDataBlue = np.genfromtxt('fixtDataBlue.csv', delimiter=',')
# mapImg=np.dstack((myDataRed, myDataGreen, myDataBlue))
#
# mockMap.pixels=mapImg
#
# numGreenPix=mockMap.count_green()
# print('green pix = '+ str(numGreenPix))
# define fixture with a numpy array of 3 green pixels -> test count_green() test that it works with no green pixels and only green pixels
# test save_green with my fixture


Macron to meet Merkel for anniversary weekend of the Berlin Wall fall
by EuronewsFrench President Emmanuel Macron will meet with German Chancellor Angela Merkel in Berlin on Sunday, Macron's office has said.
Macron will talk with German President Frank-Walter Steinmeier, then with the chancellor, ahead of a dinner attended by players and witnesses of the fall of the Berlin Wall in 1989, the statement released on Thursday said.
The meeting will be an occasion to "reflect on the legacy of 1989 and the meaning of the European project, highlighting the importance of the Franco-German couple," it said.
Macron warned fellow European countries on Thursday that NATO is dying because of American unpredictability under President Donald Trump, a view quickly rejected by Germany.
Macron used "drastic words" when he described NATO as experiencing a "brain death", Merkel said on Thursday, saying that was not her view.


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--- layout: page title: Teaching ---
Control Engineering plays a fundamental role in modern technological systems. The aim of this course is to serve as an introduction to control system analysis and design. The objectives include equipping students with:
| Lecture | Topic | Handouts | Slides |
|---|---|---|---|
| 1 | Frequency Response | Handouts1 | Slides1 |
| 2 | Bode Plots for Simple Factors | Handouts2 | Slides2 |
| 3 | Bode Plots of Complex Transfer Function | Handouts3 | Slides3 |
| 4 | Frequency Domain Modelling | Handouts4 | Slides4 |
| 5&6 | Nyquist Stability Criterion & Nyquist Plots | Handouts5 | Slides5 |
| 7 | Relative Stability Analysis | Handouts6 | Slides6 |
| 8 | Loop Shaping, Lead Compensator | Handouts7 | Slides7 |
| 9 | Lag Compensator | Handouts8 | Slides8 |
| 10 | PID Tuning | Handouts9 | Slides9 |
| 11 | Examples on Controller Design | Handouts10 | Slides10 |
| 12 | Review | Handouts11 | Slides11 |
Networked control systems are spatially distributed systems for which the communication between sensors, actuators and controllers is supported by communication networks. Recent advances in sensing, communication technologies and computer architecture have led to the rapid growth of cost effective and low power devices, which dramatically increases the adaptability, efficiency and functionality of the control systems. However, networked control systems also introduce new challenges, as the information becomes local to each node and the information sharing between nodes may subject to network effects such as packet drop or delay.
In this course, we will review several recent advancements in networked control theory. We first consider a centralized control scheme, where the communication between the sensor, the controller and the actuator is unreliable. We then move to distributed control schemes and analyze the consensus algorithm, as it is key for many distributed control applications. Next, we study the performance of a consensus-based distributed inference algorithm. Finally, we discuss the consensus algorithm in adversarial environment.
Undergraduate linear algebra, probability and signal processing, understanding of modern (state space) control theory
| Course | Topic | Reading |
|---|---|---|
| 1 | Course Overview | Control in an Information Rich World, Notes |
| 2 | State Estimation, Kalman Filtering | Kalman Filtering, Notes |
| 3 | Functions of Symmetric Matrices | Notes |
| 4 | Estimation over Lossy Networks | Notes |
| 5 | Control Over Lossy Networks, Witsenhausen's counterexample | Witsenhausen's paper, Control over Lossy Networks, Notes |
| 6 | Sensor Selection | Convex opt, Submodularity, Notes |
| 7 | Event-based Estimation | Event-triggered Estimation, Event-based Control, Notes |
| 8 | Average Consensus | Good Topology, Notes |
| 9 | Variants of Average Consensus | Consensus with Switching Topology and Channel Noise, Finite Time Consensus, Finite Time Consensus(continuous time), Notes |
| 10 | Gossip Algorithm | Gossip paper, Products of Random Matrices, Notes |
| 11 | Large Deviation | Cramer's Theorem |
| 12 | Distributed Hypothesis Testing | Reference, Notes |
| 13 | Distributed Estimation | References, Constant Gain Strategy, Notes |
| 14 | Nonnegative Matrices and Distributed Control | Perron-Frobenius theorem, Control of Positive System, Notes |
| 15 | Generic Properties of Linear Structured Systems | A Survey Paper, Notes |
| 16 | Secure Control: Intrusion Detection and Identification | Secure Consensus, Fault Detection and Identification, Notes |


{% extends "layout.html" %} {% block title %}Use a Barcode{% endblock %} {% block head %} {{ super() }} {% endblock %} {% block body %}
You're using {{ use.qr_code.registrant }}'s barcode. We just logged a use of it; if you don't actually use the card, cancel it below.
Go here to use the code. The phone number is {{ use.qr_code.phone }} if they want that instead.
Once you've used it, or changed your mind, come back here and confirm:
{% endblock %}


Learning code is challenging. There are many concepts to learn, and as you probably know already, practice is essential. Common advice for practicing what you're learning is "build projects!"
Yeah, but I want to practice most if not everything I learn - am I supposed to build a million projects to practice and demonstrate what I'm learning? No. Because another common piece of advice is to have fewer, more high quality projects, at least in your portfolio.
Well, okay. That's confusing.
First, you don't have put all the projects you create in your portfolio.
Second, in addition to creating larger projects, why not have a place that contains several smaller projects? These smaller projects can be accomplished in 30 minutes, an hour, or half a day.
That's where Code Playground comes in. Each section below is a self-contained project - a sandbox.
I only include things here that delight me.
Each sandbox has a link to resources where you can learn more about this thing.
Happy coding! 🎉
Because learning should be fun.
With so much to explore and discover in the world, why not have fun during that process?
The goals of Code Playground:
To make coding fun 🤸
To create a joyful experience and inspirational learning environment 💙
Heck, yeah!
All this project code is available on my Github.
Plus, it was one of my first projects to use a front-end framework, so...
If I can do it, so can you.


Table of Contents
Art of Combat, 1570
Book 1 Longsword
Chapter 3
Chapter 4
Chapter 5
Chapter 7
Chapter 9
Chapter 10
Chapter 11
Tag (Day)
Zornhut (Wrath Guard)
Ochs (Ox)
Einhorn (Unicorn)
Schlussel (Key)
Hangetort (Hanging Point)
Eisenport (Iron Gate)
Nebenhut (Side Guard)
Mittelhut (Middle Guard)
Langort (Long Point)
Wechsel (Changer)
Brechfenster (Break Window)
Part 3
Zornhut (Wrath Guard)
Krumphauw (Crooked Strike)
Zirckel (Circle)
Verkehren (Reversing)
Krumphauw (Crooked Strike cont.)
Zucken (Pulling)
Durchwinden (Winding Through)
Zeckrur (Tag Hit)
Durchschiessen (Shooting Through)
Krumphauw (Crooked Strike cont.)
Schneller (Flicking)
Brutes
Schielhauw (Squinter)
Pflug (Plow)
Langort (Longpoint)
Schielhauw (Squinter cont.)
Wechseln (Changing Through)
Zwerch (Thwart)
Fehlen (Feint or Failing)
Zwerch (Thwart cont.)
On Stepping
Schneiden (Slicing)
Nachreisen (Chasing)
Kron (Crown)
Streich (Slash)
Blendthauw (Blind Cut)
Grappling
Einlauffen (Running In)
Casting (Throwing)
Hengen (Hanging)
Oberhauw (High Cut)
Book 2 Dussack
Book 3 Sidesword (Rappier)
Book 4 Dagger and Wrestling
Art of Combat, 1570
Book 1 Longsword
Chapter 3
Ochs (Ox)
Pflug (Plow)
Tag (Day or Roof)
Olber (Fool)
Zornhut (Wrath Guard)
Langort (Longpoint)
Wechsel (Changer)
Nebenhut (Side Guard)
Eisenport (Iron Gate) 1
Eisenport (Iron Gate) 2
Hangetort (Hanging Point)
Schlüssel (Key)
Eynhorn (Unicorn)
Flügel (Winging)
Cutting Patterns
Chapter 4
Oberhauw (High Cut)
Zornhauw (Wrath Cut)
Mittelhauw (Middle Cut)
Underhauw (Low Cut)
Schielhauw (Squinting Cut)
Krumphauw (Crooked Cut)
Zwerch (Thwart)
Kurtzhauw (Short Cut)
Glützhauw (Glancing Cut)
Prellhauw (Rebound Cut) 1
Prellhauw (Rebound Cut) 2
Blendthauw (Blind Cut)
Windthauw (Winding Cut)
Kronhauw (Crown Cut)
Kniechelhauw (Wrist Cut)
Sturzhauw (Plunge Cut)
Wechselhauw (Change Cut)
Schneller oder Zeckrur (Flick or Tag-Hit)
Chapter 5
Nachreisen (Chasing or Following After) 1
Nachreisen (Chasing or Following After) 2
Schneiden (Slicing)
Umbschlagen (Striking Around)
Ablauffen (Running Off)
Verfuhren (Deceiving)
Verfliegen (Flitting)
Absetzen (Setting Off)
Schlaudern (Slinging)
Zucken (Pulling)
Doplieren (Doubling)
Verkehren (Reversing)
Umbschnappen (Snapping Around) 1
Umbschnappen (Snapping Around) 2
Fehlen (Failing)
Zirckel (Circle)
Rinde (Looping)
Winden (Winding)
Durchwinden (Winding Through)
Wechseln (Changing)
Abschneiden (Slicing Off)
Hendtrucken (Pressing Hands)
Verschieben (Sliding)
Hengen (Hanging)
Aussreissen (Wrenching)
Sperren (Barring)
Verstullen (Blocking)
Ubergreiffen (Gripping Over)
Einlauffen (Running In)
Chapter 7
Stepping
Chapter 9
Example Device 1
Example Device 2
Chapter 10
Pattern 1 (1.27v.1)
Pattern 1 with the Short Edge
Pattern 1 with the Flats
Pattern 2 (1.28v.1)
Pattern 3 (1.28v.2)
Pattern 4 (1.29r.1)
Pattern 5 (1.29r.2)
Pattern 6 (1.29r.3)
Pattern 7 (1.29v.1)
Pattern 8 (1.29v.2)
Pattern 9 (1.29v.3)
Pattern 10 (1.29v.4)
Pattern 11 (1.30r.1)
Pattern 12 (1.30r.2)
Chapter 11
Tag (Day)
Tag Device 1 (1.31v.1)
Tag Device 21 (1.31v.2)
Tag Device 31 (1.32r.1)
Tag Device 4 (1.32r.2)
Tag Device 5 Variant of 4 (1.33r.1)
Tag Device 6 Another (1.33r.2)
Tag Device 7 Counter (1.33v.1)
Tag Device 8 (1.33v.2)
Zornhut (Wrath Guard)
Zornhut Device 1
Zornhut Device 2
Zornhut Device 3
Zornhut Device 4
Zornhut Precept
Zornhut Left Side
Ochs (Ox)
Ochs Device 1
Ochs Device 2
Einhorn (Unicorn)
Eynhorn Device 1
Eynhorn Device 2
Eynhorn Device 3
Eynhorn Device 4
Schlussel (Key)
Schlüssel Device 1
Schlüssel Device 2
Hangetort (Hanging Point)
Hangetort Device 1
Hangetort Device 2
Hangetort Device 3
Eisenport (Iron Gate)
Eisenport (actually Schrankhut) Device 1
Eisenport (actually Schrankhut) Device 2
Eisenport (actually Schrankhut) Device 3
Eisenport (actually Schrankhut) Device 4
Nebenhut (Side Guard)
Nebenhut Device 1
Mittelhut (Middle Guard)
Mittelhut Device 1
Mittelhut Device 2 a.k.a. Rose 1
Mittelhut Device 3
Langort (Long Point)
Langort Device 1 (1.41r.1)
Langort Device 2 a.k.a. Rose 2 (1.41r.2)
Langort Device 3 a.k.a. Rose 3 (1.41r.3)
Langort Device 4 a.k.a. Rose 4 (1.41r.4)
Langort Device 5 (1.41r.5)
Langort Device 6 (1.42r.1)
Langort Device 7 a.k.a. Rose 5 (1.42r.2)
Langort Device 8 a.k.a. Rose 5 alternate ending (1.42r.3)
Wechsel (Changer)
Wechsel Device 1
Wechsel Device 2
Brechfenster (Break Window)
Brechfenster Device 1
Brechfenster Device 2
Brechfenster Device 3
Brechfenster Device 4
Brechfenster Device 5
Brechfenster Device 6
Part 3
Zornhut (Wrath Guard)
Zornhut 1
Krumphauw (Crooked Strike)
Krumphauw 1
Krumphauw 2
Zirckel (Circle)
Zirckel 1
Zirckel 2 (A Good Device with the Circle)
Verkehren (Reversing)
Verkehren 1 with Counter (A Fine Device with Reversing)
Krumphauw (Crooked Strike cont.)
Krumphauw 3 with Counter (1.48v.3)
Zucken (Pulling)
Zucken 1 (1.49v.2)
Zucken 2
Zucken 3
Zucken 4
Durchwinden (Winding Through)
Durchwinden 1 (1.50r.1)
Durchwinden 2
Zeckrur (Tag Hit)
Zeckrur 1 (1.50r.3)
Zeckrur 2
Durchschiessen (Shooting Through)
Durchschiessen 1
Durchschiessen 2
Krumphauw (Crooked Strike cont.)
Krumphauw 4
Schneller (Flicking)
Schneller 1
Schneller 2
Schneller 3
Brutes
Brute 1 (Another)
Brute 2
Schielhauw (Squinter)
Schielhauw 1 (Counter against the Squinter)
Schielhauw 2
Pflug (Plow)
Pflug 1 (Counter against Plow)
Langort (Longpoint)
Langort 1 (Counter against Longpoint)
Langort 2
Schielhauw (Squinter cont.)
Schielhauw 3 (Another Device with the Squinter)
Schielhauw 4 (Doubling the Squinter)
Schielhauw 5
Schielhauw 6 (The Third Squinter is a Deceit with the Face)
Schielhauw 7 (Note a Deft Device with the Squinter)
Wechseln (Changing Through)
Wechseln 1 (Concerning Changing Through)
Wechseln 2
Zwerch (Thwart)
Zwerch 1
Zwerch 2
Zwerch 3
Zwerch 4
Fehlen (Feint or Failing)
Fehlen 1
Fehlen 2
Fehlen 3 (A Failer with a False Step)
Fehlen 4 (Twofold or Double Failing)
Fehlen 5 (Another with the Double Failer)
Zwerch (Thwart cont.)
Zwerch 5 (Counter against the Thwart)
Zwerch 6
On Stepping
Stepping 1
Schneiden (Slicing)
Schneiden 1 (...through the mouth)
Schneiden 2
Schneiden 3
Nachreisen (Chasing)
Nachreisen 1
Nachreisen 2
Kron (Crown)
Kron 1
Streich (Slash)
Streich 1 (1.60v.2)
Streich 2
Blendthauw (Blind Cut)
Blendthauw 1
Grappling
Grappling 1 with Counter (Concerning this grappling and wrestling...)
Grappling 2 (Another)
Grappling 3
Grappling 4
Grappling 5 with Counter
Einlauffen (Running In)
Einlauffen 1 (Another devices against running in)
Casting (Throwing)
Casting 1
Casting 2
Casting 3
Casting 4 (Another)
Casting 5 (Another)
Hengen (Hanging)
Hengen 1 (How you shall wind in and out of the low hanging)
Hengen 2
Hengen 3
Hengen 4 (How you shall carry out the inward hangings and windings from above in the work)
Hengen 5
Oberhauw (High Cut)
Oberhauw 1 (A Good Counter Against all High Cuts)
Book 2 Dussack
Book 3 Sidesword (Rappier)
Book 4 Dagger and Wrestling


Input the name of the song
Input the name of the artist name
{% for song in song_list %}
{{ song }}
{% endfor %}


Account linking has failed because you were not logged in. This has caused the process to fail, so you must go back to the Accounts page and try again.
Account linking has failed because no Identity Provider for accounts of type {{ accountType }} has been configured. Please contact your system administrator to resolve this configuration problem.
An unexpected error occurred while trying to create a linked account. The process has failed, so you must go back and try again.
You've successfully authorized Apicurio Studio with GitHub GitLab Bitbucket and we are now finalizing the link. This should only take a moment, after which you will be redirected back to the Settings >> Accounts page.
Finalizing account link, please wait...
This account has already been successfully linked. Click the button below to return to the Settings page.


fontimage
generate a font thumbnail image
fontimage [--o outfile] [--width num] [--height num] [--pixelsize num]
[--fontname] [--text string] [--version] [--help] fontfile
Fontimage produces a thumbnail image of a font (which may be in
any format fontforge can read).
You may either explicitly specify text to be displayed in the font, or rely
on fontimage's default behavior, which is to check for various scripts it
knows about and display something appropriate for each one present.
-
--help
-
Displays a list of arguments and descriptions of each.
-
--width num
-
Specifies the width (in pixels) of the output image. If omitted the image
will be as wide as it needs to be to display the text.
-
--height num
-
Specifies the height (in pixels) of the output image. If omitted the image
will be as high as it needs to be to display the text.
-
--o outputfile
-
Specifies the name of the output image file. The format is determined by
the extension. Currently only ".bmp" and ".png" are supported. If omitted,
fontimage will choose a filename based on the fontname of the font.
-
--pixelsize num
-
Specifies the pixelsize at which to display text. This may be specified multiple
times. Each specification applies to any --text arguments which occur after
it.
-
--fontname
-
Include the fontname as a line of text.
-
--text string
-
Specifies a string to be displayed. The string must be in UTF-8.
This option may be provided multiple times to supply more than one line of
text. If omitted entirely, fontimage will examine the scripts it knows about
in the font and will display text appropriate to each one present.
-
--version
-
Displays the fontimage version.
See Also
FontForge
Example


Greetings! in the world of TechnoUniverse
We are a new gen startup
Mainly working in 2 verticals viz Skills and Software Development
Software Development
For us Quality is a responsibility.
Our software development house have some innovative products in pipeline which are aimed to create a stir in market and be addictive at the same time be user friendly and pleasing to eyes.
On course we plan to be working as SaaS company as well for developing tools and services to enable businesses scale and grow faster.
Our team of dedicated software professionals focus to build quality products and services which are highly affordable and helpful to masses to bring about a positive change.
Skills Development
In today's era lot of graduates are available and ready to work in Industry but are they really ready?
Lot of researches show software industry is on track to new horizons with lot of new startups offering unbelievable salaries to skilled professionals. Same segment of industry has huge demand of fresh talent but graduates are lacking on basic skills and loosing out
Our team which consists of experts both from academics and industry is poised exceptionally well to train upcoming graduates become best professionals industry needs today.
Know a little more about us
But I must explain to you how all this mistaken idea of denouncing pleasure and praising pain was born and I will give you a complete account of the system, and expound the actual teachings of the great explorer of the truth, the master-builder of human happiness.
No one rejects, dislikes, or avoids pleasure itself, because it is pleasure, but because those who do not know how to pursue pleasure


Recognize Emotions in Images
Emotion Detection is part of the Face Detection API and provides an analysis of the predicted emotions expressed in each
face within the frame. The emotions currently enabled are: happiness, surprise, neutral, anger, contempt, disgust,
fear, and sadness.
To try out the Emotion Detection API, please click the image samples below to generate example results.
{{errorMessage}}
{{topEmotion(i)}}
Number of faces: {{ emotions ? emotions.length : 0 }}
{{ emotions | json }}
Want to build this?


The only effect of a declaration
:- public :PredSpec, ..., :PredSpec.
where each PredSpec is a predicate spec, is to give the SICStus cross-referencer (see The Cross-Referencer) a starting point for tracing reachable code. In some Prologs, this declaration is necessary for making compiled predicates visible. In SICStus Prolog, predicate visibility is handled by the module system. See ref-mod.


You are viewing an historical archive of past issues. Please report new issues to the appropriate project issue tracker on GitHub.
Comments
-
Greg Hurrell
This is a bug in the version of Vim that ships with Ubuntu 11.04. See this forum topic for more info.
-
Greg Hurrell
Status changed:
- From: new
- To: closed
-
anonymous
Although vim works fine (except of I couldn't close finder window hitting "q", like it was using gvim on Ubuntu 10.10), gvim segfaults on Ubuntu 11.04 and latest vim build:
VIM - Vi IMproved 7.3 (2010 Aug 15, compiled Jun 15 2011 10:58:16) Included patches: 1-154 Compiled by gmile@gmile-K52Jr Huge version without GUI. Features included (+) or not (-): +arabic +autocmd -balloon_eval -browse ++builtin_terms +byte_offset +cindent -clientserver -clipboard +cmdline_compl +cmdline_hist +cmdline_info +comments +conceal +cryptv +cscope +cursorbind +cursorshape +dialog_con +diff +digraphs -dnd -ebcdic +emacs_tags +eval +ex_extra +extra_search +farsi +file_in_path +find_in_path +float +folding -footer +fork() +gettext -hangul_input +iconv +insert_expand +jumplist +keymap +langmap +libcall +linebreak +lispindent +listcmds +localmap -lua +menu +mksession +modify_fname +mouse -mouseshape +mouse_dec -mouse_gpm -mouse_jsbterm +mouse_netterm -mouse_sysmouse +mouse_xterm +multi_byte +multi_lang -mzscheme +netbeans_intg -osfiletype +path_extra -perl +persistent_undo +postscript +printer +profile -python -python3 +quickfix +reltime +rightleft +ruby +scrollbind +signs +smartindent -sniff +startuptime +statusline -sun_workshop +syntax +tag_binary +tag_old_static -tag_any_white -tcl +terminfo +termresponse +textobjects +title -toolbar +user_commands +vertsplit +virtualedit +visual +visualextra +viminfo +vreplace +wildignore +wildmenu +windows +writebackup -X11 -xfontset -xim -xsmp -xterm_clipboard -xterm_save system vimrc file: "$VIM/vimrc" user vimrc file: "$HOME/.vimrc" user exrc file: "$HOME/.exrc" fall-back for $VIM: "/usr/local/share/vim" Compilation: gcc -c -I. -Iproto -DHAVE_CONFIG_H -g -O2 -D_FORTIFY_SOURCE=1 Linking: gcc -L. -rdynamic -Wl,-export-dynamic -L/usr/local/lib -Wl,--as-needed -o vim -lm -lncurses -lnsl -Wl,-R -Wl,/home/gmile/.rvm/rubies/ruby-1.9.2-p180/lib -L/home/gmile/.rvm/rubies/ruby-1.9.2-p180/lib -lruby -lpthread -lrt -ldl -lcrypt -lm
Ruby:
ruby 1.9.2p180 (2011-02-18 revision 30909) [i686-linux]
-
Greg Hurrell
This is caused by a buggy Vim shipped with Ubuntu 11.04. Fix is installing a newer version of Vim.
Plenty of tickets and forum threads on the subject — for example, this one — so will mark this one closed as a duplicate.
Add a comment
Comments are now closed for this issue.


| Package | org.as3commons.collections |
| Class | public class Treap |
| Implements | IBinarySearchTree |
Description
A treap is a randomised binary search tree where degeneration can be minimised by assigning a random priority value to each tree node. A treap is comparatively easy to implement and is at the same time highly performant. "To summarize, in the mixed workload environment, the treap data structure outperformed the other 4 implementations by a rather significant margin." [1] The implementation of this class is taken from a Java template. [2]
The differences between SortedSet
and Treap
are:
Runtime of operations
first, last, removeFirst(), removeLast()
Notes
The iterator()
method accepts an item as the cursor parameter. If the given
item is not contained by the treap, it will be ignored.
The iterator()
method retuns an ISortedSetIterator
.
null
is allowed if the comparator allows null
.
Treap example
This example shows the specific behaviour of a Treap
.
The general work with collections and iterators is more detailed illustrated
in the ArrayList
examples below.
ArrayList example
Show exampleArrayListIterator example
Show exampleSee also
| Property | Defined by | ||
|---|---|---|---|
| comparator : IComparator
Returns or sets the initial comparator of the collection.
| Treap | ||
| first : * [read-only]
The first item or
undefined if the collection is empty. | Treap | ||
| last : * [read-only]
The last item or
undefined if the collection is empty. | Treap | ||
| size : uint [read-only]
The number of items contained by the collection.
| Treap |
| Property | Defined by | ||
|---|---|---|---|
| _comparator : IComparator
The sort criterion.
| Treap | ||
| _root : TreapNode
The root node.
| Treap | ||
| _size : uint = 0
The size of the treap.
| Treap |
| Method | Defined by | ||
|---|---|---|---|
|
Treap(comparator:IComparator = null)
Treap constructor.
| Treap | ||
|
add(item:*):Boolean
Adds an item to the tree.
| Treap | ||
|
clear():Boolean
Removes all items from the collection.
| Treap | ||
|
equalItem(item:*):*
Returns the item that is equal to the given one.
| Treap | ||
|
has(item:*):Boolean
Tests if an item is contained by the collection.
| Treap | ||
|
hasEqual(item:*):Boolean
Tests if the collection contains an item that is equal to the given item.
| Treap | ||
|
higher(item:*):*
Returns the first item that is higher than the given item.
| Treap | ||
|
Returns an iterator over the iterable data structure.
| Treap | ||
|
lesser(item:*):*
Returns the last item that is lesser than the given item.
| Treap | ||
|
remove(item:*):Boolean
Removes an item from the collection.
| Treap | ||
|
removeFirst():*
Removes the first item of the ordered collection.
| Treap | ||
|
removeLast():*
Removes the last item of the ordered collection.
| Treap | ||
|
toArray():Array
Returns an array of all items.
| Treap |
| Method | Defined by | ||
|---|---|---|---|
|
add_protected(item:*):TreapNode
Adds an item to the treap.
| Treap | ||
|
Returns the node of the given item.
| Treap | ||
|
higherNode(item:*):TreapNode
Returns the first node whose item is higher than the given item.
| Treap | ||
|
lesserNode(item:*):TreapNode
Returns the last node whose item is lesser than the given item.
| Treap | ||
|
removeNode(node:TreapNode):void
Removes a node from the tree.
| Treap | ||
|
Rotates a parent with its child node.
| Treap |
| _comparator | property |
protected var _comparator:IComparator
The sort criterion.
| comparator | property |
comparator:IComparator
[read-write]Returns or sets the initial comparator of the collection.
You cannot reset a comparator of a collection that contains items.
In that case an ArgumentError
is thrown.
public function get comparator():IComparator
public function set comparator(value:IComparator):void
| first | property |
first:*
[read-only]
The first item or undefined
if the collection is empty.
public function get first():*
| last | property |
last:*
[read-only]
The last item or undefined
if the collection is empty.
public function get last():*
| _root | property |
protected var _root:TreapNode
The root node.
| _size | property |
protected var _size:uint = 0
The size of the treap.
| size | property |
size:uint
[read-only]The number of items contained by the collection.
Implementation public function get size():uint
| Treap | () | constructor |
public function Treap(comparator:IComparator = null)
Treap constructor.
Parameterscomparator:IComparator (default = null ) — The sort criterion.
|
| add | () | method |
public function add(item:*):Boolean
Adds an item to the tree.
If the tree already contains an item that is equal to the given one,
the method abords with false
.
Hence, if the tree already contains the given item, the method abords with false
.
item:* — The item to add.
|
Boolean — true if the item has been added.
|
| add_protected | () | method |
protected function add_protected(item:*):TreapNode
Adds an item to the treap.
The method creates a new treap node, inserts the node as the leaf of its successor/predecessor and rotates then the node upwards until the tree becomes consistent again regarding the priority constraint.
If the treap is empty, the method simply creates a new root node.
Parametersitem:* — The data to insert into the tree.
|
TreapNode —
true, if the item has been inserted, false if an equal item already exists.
|
— If the item cannot be compared.
|
| clear | () | method |
public function clear():Boolean
Removes all items from the collection.
If the collection is already empty, the method aborts with false
.
Boolean — true if items have been removed.
|
| equalItem | () | method |
public function equalItem(item:*):*
Returns the item that is equal to the given one.
Returns of course the given item, if the item is contained.
Parametersitem:* — The item to test.
|
* — The equal item or undefined if there is no equal item.
|
| getNode | () | method |
protected function getNode(item:*):TreapNode
Returns the node of the given item.
Parametersitem:* — The item to get the node for.
|
TreapNode —
The node or null , if the item is not contained.
|
| has | () | method |
public function has(item:*):Boolean
Tests if an item is contained by the collection.
Parametersitem:* — The item to test.
|
Boolean — true if the item is contained.
|
| hasEqual | () | method |
public function hasEqual(item:*):Boolean
Tests if the collection contains an item that is equal to the given item.
Returns of course true
if the given item is contained.
item:* — The item to test.
|
Boolean — true if there is an equal item contained.
|
| higher | () | method |
public function higher(item:*):*
Returns the first item that is higher than the given item.
Parametersitem:* — The item to test.
|
* — The next higher item or undefined if there is no higher item.
|
| higherNode | () | method |
protected function higherNode(item:*):TreapNode
Returns the first node whose item is higher than the given item.
Parametersitem:* — The item to test.
|
TreapNode —
The next higher node or null if there is no higher item.
|
| iterator | () | method |
public function iterator(cursor:*):IIterator
Returns an iterator over the iterable data structure.
The cursor
parameter is optional and not considered by all iterable
data structures. E.g. the iterator of an unordered collection such as Map
or Set
starts always with the first item found. Still passing a parameter to
such a data structure does not throw an error and will simply show no effect.
The type of the cursor is unspecified and depends on the particular data structure. E.g. lists allow to specify an index, sets to specify an item and maps to specify a key.
If cursor
is specified, the iterator returns the item that is related
to that cursor with the first call to next()
(and its predecessor with
the first call to previous()
if the iterator allows bidirectional
navigation.
cursor:* — The initial cursor position.
|
IIterator —
An iterator.
|
| lesser | () | method |
public function lesser(item:*):*
Returns the last item that is lesser than the given item.
Parametersitem:* — The item to test.
|
* — The last lesser item or undefined if there is no lesser item.
|
| lesserNode | () | method |
protected function lesserNode(item:*):TreapNode
Returns the last node whose item is lesser than the given item.
Parametersitem:* — The item to test.
|
TreapNode —
The last lesser node or null if there is no lesser item.
|
| mostLeftNode_internal | () | method |
as3commons_collections function mostLeftNode_internal(node:TreapNode = null):TreapNode
Framework internal method to retrieve the lowest node of the given subtree (node).
Parametersnode:TreapNode (default = null ) — The root node of the subtree.
|
TreapNode —
The lowest node or null , if the treap is empty.
|
| mostRightNode_internal | () | method |
as3commons_collections function mostRightNode_internal(node:TreapNode = null):TreapNode
Framework internal method to retrieve the highest node of the given subtree (node).
Parametersnode:TreapNode (default = null ) — The root node of the subtree.
|
TreapNode —
The highest node or null , if the treap is empty.
|
| nextNode_internal | () | method |
as3commons_collections function nextNode_internal(node:TreapNode):TreapNode
Framework internal method to retrieve the successor of a given node.
Parametersnode:TreapNode — The node to get the successor.
|
TreapNode —
The next node or null if the node is the first item or the treap is empty.
|
| previousNode_internal | () | method |
as3commons_collections function previousNode_internal(node:TreapNode):TreapNode
Framework internal method to retrieve the predecessor of a given node.
Parametersnode:TreapNode — The node to get the predecessor.
|
TreapNode —
The previous node or null if the node is the last node or the treap is empty.
|
| remove | () | method |
public function remove(item:*):Boolean
Removes an item from the collection.
Trying to remove an item which is not contained by the collection
leaves the collection untouched and returns false
.
If the collection contains multiple entries of the given item the first occurrence of the item will be removed.
Parametersitem:* — The item to remove.
|
Boolean — true if the item has been removed.
|
| removeFirst | () | method |
public function removeFirst():*
Removes the first item of the ordered collection.
Returns* — The formerly first item or undefined if the collection is empty.
|
| removeLast | () | method |
public function removeLast():*
Removes the last item of the ordered collection.
Returns* — The formerly last item or undefined if the collection is empty.
|
| removeNode | () | method |
protected function removeNode(node:TreapNode):void
Removes a node from the tree.
The method rotates the given node downwards until it becomes a leaf node and gets removed from the tree.
Parametersnode:TreapNode — The node to remove.
|
| removeNode_internal | () | method |
as3commons_collections function removeNode_internal(node:TreapNode):void
Framework internal method to remove a node from the treap.
Parametersnode:TreapNode — The node to remove.
|
| rotate | () | method |
protected function rotate(parent:TreapNode, child:TreapNode):void
Rotates a parent with its child node.
Parametersparent:TreapNode — The parent node.
|
|
child:TreapNode — The child node.
|
| toArray | () | method |
public function toArray():Array
Returns an array of all items.
The order of items in the array is the same as in the particular collection.
Certain collections do not have an explicit order (Map
, Set
).
The order of the items in the array is then not predictable.
If the collection maintains an array as its data source, a copy of the internal array is returned rather than the original array.
ReturnsArray — An array of the items of the collection.
|


node-liveosc
node.js integration with Ableton Live via LiveOSC
Prerequisites
You will need to have LiveOSC installed and running. For best results, please use this version:
https://github.com/dinchak/LiveOSC
Usage
Basic usage is as follows:
// load LiveOSC class
var LiveOSC = require('liveosc');
// create new instance of LiveOSC, starts OSC listener
var liveosc = new LiveOSC({debug: true});
// triggered when Live is loaded and ready
liveosc.song.on('ready', function () {
// start the song playing
liveosc.song.play();
});
Reference
See the API docs for full usage information.
Song Structure
The LiveOSC object has a song property that contains a full object map of the current Live set. The organization of the object is as follows:
Song
{
tempo: 120.0,
tracks: [{Track}, {Track}, ...],
returns: [{Return}, {Return}, ...],
devices: [{Device}, {Device}, ...],
volume: 0.0,
pan: 0.0,
scene: 0,
beat: 0,
playing: 1
}
Track
{
id: 0,
name: '1-MIDI',
clips: [{Clip}, {Clip}, ...],
sends: [{id: 0, value: 1.0}, {id: 1, value: 1.0}, ...],
devices: [{Device}, {Device}],
audio: 0,
solo: 0,
mute: 0,
arm: 0,
volume: 0,
pan: 0,
numScenes: 0
}
Clip
{
id: 0,
name: '',
track: {Track},
state: 0,
coarse: 0,
fine: 0,
loopstart: 0,
loopend: 0,
loopstate: 0,
warping: 0,
length: 0
}
Return
{
id: 0,
name: 'A-Reverb',
sends: [{id: 0, value: 1.0}, {id: 1, value: 1.0}, ...],
devices: [{Device}, {Device}],
solo: 0,
mute: 0,
volume: 0,
pan: 0
}
Device
{
id: 0,
name: 'Reverb',
track: {Track},
type: 'return',
params: [
{
id: 0,
value: 1,
name: 'Device On',
min: 0,
max: 1
},
{
id: 1,
value: 0.5555555820465088,
name: 'PreDelay',
min: 0,
max: 1
},
...
]
}
Changing Parameters
Most parameters have a corresponding set function that can be called to change them. For example, to set a clip's pitch, use the setPitch function:
clip.setPitch(12);
There are also a number of events that can be triggered, such as playing a clip:
clip.play();
See the API docs for a full reference.
Events
Each object emits various events that can be listened for. For example, to listen for changes to a clip's playing state (ie. the clip was started or stopped):
clip.on('state', function (param) {
// do something with param
});
param
will contain the new and previous values of the parameter, for example:
{
value: 1,
prev: 0
}
Events can be listened at a global level as well. Each clip, track, device, and return will prefix its events with the type (clip:state for example) and broadcast them through the song event emitter. To listen for all clip state changes:
liveosc.song.on('clip:state', function (param) {
// do something with param
});
Additional parameters including the id of the object will be passed at the global level:
```javascript
{
id: 0, // the clip id
trackId: 0, // the track id the clip is on
value: 1,
prev: 0
}
See the API docs for a full reference.
Using the REPL
A REPL is included to help with exploring the object model and how LiveOSC behaves:
$ node repl
LiveOSC> song.tracks[2].name;
'3-Audio'
LiveOSC> song.tracks[2].clips[1].name;
'test'
LiveOSC> song.tracks[2].clips[1].on('name', function (param) { console.log(param) });
undefined
LiveOSC> song.tracks[2].clips[1].setName('fresh jams');
undefined
{ value: 'fresh jams', prev: 'test' }
LiveOSC> song.tracks[2].clips[1].name;
'fresh jams'
LiveOSC>


By pushing this button wee get random data from our awesome service.
The ransom result is:
{{randomResult}}
.
Get some people data from a place far, far away:
| First Name |
Last Name |
Age |
| {{p.firstName}} |
{{p.lastName}} |
{{p.age}} |
Stream this:
{{streamResult}}


Replication Manager automatically conducts elections when necessary, based on configuration information supplied to the DB_ENV->rep_set_priority() method, unless the application turns off automatic elections using the DB_ENV->rep_set_config() method.
It is the responsibility of a Base API application to initiate elections if desired. It is never dangerous to hold an election, as the Berkeley DB election process ensures there is never more than a single master database environment. Clients should initiate an election whenever they lose contact with the master environment, whenever they see a return of DB_REP_HOLDELECTION from the DB_ENV->rep_process_message() method, or when, for whatever reason, they do not know who the master is. It is not necessary for applications to immediately hold elections when they start, as any existing master will be discovered after calling DB_ENV->rep_start(). If no master has been found after a short wait period, then the application should call for an election.
For a client to win an election, the replication group must currently have no master, and the client must have the most recent log records. In the case of clients having equivalent log records, the priority of the database environments participating in the election will determine the winner. The application specifies the minimum number of replication group members that must participate in an election for a winner to be declared. We recommend at least ((N/2) + 1) members. If fewer than the simple majority are specified, a warning will be given.
If an application's policy for what site should win an election can be parameterized in terms of the database environment's information (that is, the number of sites, available log records and a relative priority are all that matter), then Berkeley DB can handle all elections transparently. However, there are cases where the application has more complete knowledge and needs to affect the outcome of elections. For example, applications may choose to handle master selection, explicitly designating master and client sites. Applications in these cases may never need to call for an election. Alternatively, applications may choose to use DB_ENV->rep_elect()'s arguments to force the correct outcome to an election. That is, if an application has three sites, A, B, and C, and after a failure of C determines that A must become the winner, the application can guarantee an election's outcome by specifying priorities appropriately after an election:
on A: priority 100, nsites 2 on B: priority 0, nsites 2
It is dangerous to configure more than one master environment using the DB_ENV->rep_start() method, and applications should be careful not to do so. Applications should only configure themselves as the master environment if they are the only possible master, or if they have won an election. An application knows it has won an election when it receives the DB_EVENT_REP_ELECTED event.
Normally, when a master failure is detected it is desired that an election finish quickly so the application can continue to service updates. Also, participating sites are already up and can participate. However, in the case of restarting a whole group after an administrative shutdown, it is possible that a slower booting site had later logs than any other site. To cover that case, an application would like to give the election more time to ensure all sites have a chance to participate. Since it is intractable for a starting site to determine which case the whole group is in, the use of a long timeout gives all sites a reasonable chance to participate. If an application wanting full participation sets the DB_ENV->rep_elect() method's nvotes argument to the number of sites in the group and one site does not reboot, a master can never be elected without manual intervention.
In those cases, the desired action at a group level is to hold a full election if all sites crashed and a majority election if a subset of sites crashed or rebooted. Since an individual site cannot know which number of votes to require, a mechanism is available to accomplish this using timeouts. By setting a long timeout (perhaps on the order of minutes) using the DB_REP_FULL_ELECTION_TIMEOUT flag to the DB_ENV->rep_set_timeout() method, an application can allow Berkeley DB to elect a master even without full participation. Sites may also want to set a normal election timeout for majority based elections using the DB_REP_ELECTION_TIMEOUT flag to the DB_ENV->rep_set_timeout() method.
Consider 3 sites, A, B, and C where A is the master. In the case where all three sites crash and all reboot, all sites will set a timeout for a full election, say 10 minutes, but only require a majority for nvotes to the DB_ENV->rep_elect() method. Once all three sites are booted the election will complete immediately if they reboot within 10 minutes of each other. Consider if all three sites crash and only two reboot. The two sites will enter the election, but after the 10 minute timeout they will elect with the majority of two sites. Using the full election timeout sets a threshold for allowing a site to reboot and rejoin the group.
To add a database environment to the replication group with the intent of it becoming the master, first add it as a client. Since it may be out-of-date with respect to the current master, allow it to update itself from the current master.  Presumably, the added client will win the subsequent election. If the client does not win the election, it is likely that it was not given sufficient time to update itself with respect to the current master.
If a client is unable to find a master or win an election, it means that the network has been partitioned and there are not enough environments participating in the election for one of the participants to win. In this case, the application should repeatedly call DB_ENV->rep_start() and DB_ENV->rep_elect(), alternating between attempting to discover an existing master, and holding an election to declare a new one. In desperate circumstances, an application could simply declare itself the master by calling DB_ENV->rep_start(), or by reducing the number of participants required to win an election until the election is won. Neither of these solutions is recommended: in the case of a network partition, either of these choices can result in there being two masters in one replication group, and the databases in the environment might irretrievably diverge as they are modified in different ways by the masters.
Note that this presents a special problem for a replication group consisting of only two environments. If a master site fails, the remaining client can never comprise a majority of sites in the group. If the client application can reach a remote network site, or some other external tie-breaker, it may be able to determine whether it is safe to declare itself master. Otherwise it must choose between providing availability of a writable master (at the risk of duplicate masters), or strict protection against duplicate masters (but no master when a failure occurs). Replication Manager offers this choice via the DB_ENV->rep_set_config() method DB_REPMGR_CONF_2SITE_STRICT flag. Base API applications can accomplish this by judicious setting of the nvotes and nsites parameters to the DB_ENV->rep_elect() method.
It is possible for a less-preferred database environment to win an election if a number of systems crash at the same time. Because an election winner is declared as soon as enough environments participate in the election, the environment on a slow booting but well-connected machine might lose to an environment on a badly connected but faster booting machine. In the case of a number of environments crashing at the same time (for example, a set of replicated servers in a single machine room), applications should bring the database environments on line as clients initially (which will allow them to process read queries immediately), and then hold an election after sufficient time has passed for the slower booting machines to catch up.
If, for any reason, a less-preferred database environment becomes the master, it is possible to switch masters in a replicated environment. For example, the preferred master crashes, and one of the replication group clients becomes the group master. In order to restore the preferred master to master status, take the following steps:


This func is used to relink a Graphs output by Unparse using the graph=true option. See the graph example to see how this works.
Function graphExpand
| @x:func graphExpand(t~xdom)~xdom | ||
|
Semantics
Result is the linked graph: a modified version of t with graph defs and uses replaced by links. Note the graph is modified in place, not copied.


Happy Founders Day to the women of Alpha Kappa Alpha Sorority Inc.
Music Video
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# Lean Coffee #### By Piotr Suwała --- ## Step 1 1. Division into groups of 5-8 people [5min] --- ## Step 2 1. Choosing topics [15min] 1. Each person proposes a topic as a sticky note 1. Similar topics are merged 1. Everyone has 3 dots to vote 1. 3 topics with the highest number of votes win --- ## Step 3 1. Each team shares topics of their choosings [2min/team] --- ## Step 4 1. Talks [60min] 1. Topic is being conducted for 20 minutes 2. (People may make notes on sticky notes, doodling, etc.) 3. After topic: People can move to other tables (but limit per station should be preserved) 4. The next topic gets addressed --- ## Step 5 1. Each person shares what was the most impactful for him during the lean coffee


INF.SYS
INF.SYS file (\FBB\SYSTEM).
This binary file holds the information on all users of the BBS. This file
is very important and must not be edited or changed otherwise the list of the
users could be lost. This file can only be opened for reading.
It is organized with records, each record containing information on a user.
Never modify this file while the BBS is running.
Structure of one record (C language) :
#define uchar unsigned char
typedef struct { /* Callsign structure used in info */
char callsign[7] ;
char ssid ;
} indicat ;
typedef struct {
indicat indic ; /* 8 Callsign */
indicat relai[8] ; /* 64 Digis path */
long lastmes ; /* 4 Last L number */
long nbcon ; /* 4 Number of connexions */
long hcon ; /* 4 Last connexion date */
long lastyap ; /* 4 Last YN date */
unsigned flags ; /* 2 Flags */
unsigned on_base ; /* 2 ON Base number */
uchar nbl ; /* 1 Lines paging */
uchar lang ; /* 1 Language */
long newbanner ; /* 4 Date of new banner (lang.NEW)*/
ushort download ; /* 2 Download size in KB */
char free[20] ; /* 20 Reserved */
char thema ; /* 1 Current thema selection */
char nom[18] ; /* 18 1st Name */
char prenom[13]; /* 13 Christian name */
char adres[61] ; /* 61 Address */
char ville[31] ; /* 31 City */
char teld[13] ; /* 13 home phone */
char telp[13] ; /* 13 job phone */
char home[41] ; /* 41 home BBS */
char qra[7] ; /* 7 Qth Locator */
char priv[13] ; /* 13 PRIV directory */
char filtre[7] ; /* 7 LC choice filter */
char pass[13] ; /* 13 Password */
char zip[9] ; /* 9 Zipcode */
} info ; /* 360 bytes = lenght of one record */
Dates are given as the number of seconds since january 1st, 1970 00:00.
In C language, all strings are ended with a NULL (00 hex) character.
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