Phil 1.5.19

It seems to me that this might also be important for validating machine learning models. Getting a critical level for false classification might really help

  • The quest for an optimal alpha
    • Researchers who analyze data within the framework of null hypothesis significance testing must choose a critical “alpha” level, α, to use as a cutoff for deciding whether a given set of data demonstrates the presence of a particular effect. In most fields, α = 0.05 has traditionally been used as the standard cutoff. Many researchers have recently argued for a change to a more stringent evidence cutoff such as α = 0.01, 0.005, or 0.001, noting that this change would tend to reduce the rate of false positives, which are of growing concern in many research areas. Other researchers oppose this proposed change, however, because it would correspondingly tend to increase the rate of false negatives. We show how a simple statistical model can be used to explore the quantitative tradeoff between reducing false positives and increasing false negatives. In particular, the model shows how the optimal α level depends on numerous characteristics of the research area, and it reveals that although α = 0.05 would indeed be approximately the optimal value in some realistic situations, the optimal α could actually be substantially larger or smaller in other situations. The importance of the model lies in making it clear what characteristics of the research area have to be specified to make a principled argument for using one α level rather than another, and the model thereby provides a blueprint for researchers seeking to justify a particular α level.

Working more on A guided tour through a dirt-simple “deep” neural network

jaybookman

femexplore

Phil 1.4.19

7:00 – 5:30 ASRC NASA

  • Ping Shimei – Tuesday at 4:00
  • Ping Don – Wednesday at 4:00
  • Hammerhead – print shipping label. Use Karoo box on bookshelf
  • Antibubbles is coming along really well. If Saturday is really going to be a rainy day, maybe get started on the PHP story code? Note: Check in the html source how pictures are referenced
  • Try changing the error chart so that each sample is a seperate line (along with the average?) Done. I like this a lot! outputerror
  • Walk through SimpleLayer in the order that it’s used
    • Creation
    • Training
    • Learning
    • Graphing
  • Beat on the prediction plumbing with Aaron. The parts that collect the error and produce a forecast are there, but not working right?

Phil 1.3.19

7:00 – 5:30 ASRC NASA

  • Realized that error calculation for Holt can simply be error from the horizontal line from each prediction. There would be a distribution for T-1, T-2, T-3 … T-n. Later, when we get fancy, we can use the phi curve. So dumb.
  • Continuing my deep neural network writeup
  • Continuing Holt-Winters work with Aaron – probability distributions!
    • Ok, I think I’ve got this stupid thing figured out. Below is a screenshot of the table of predictions. These predictions are based on applying exponential smoothing to a history of sine waves:
      • sinewaves
    • The table consists of a set of predictions and their observed values (not sure why the time steps in the column on the left are duplicated. Need to fix that:
      • predictions
    • I can then make a table that contains each prediction as a line stretching into the future:
      • populations
    • This “population of prediction errors” can then be used to calculate the amount of error in our forecast:
      • charts
    • This will work for any of the prediction schemes. We just have to store all predictions and observed.
    • Here’s the spreadsheet: ExponentialSmoothing2
  • Ping Shimei – campus closed
  • Ping Don – campus closed
  • Hammerhead 

 

Phil 1.2.19

Gotta get used to writing dates

7:00 – 5:00 ASRC PhD NASA

  • Continuing my deep neural network writeup
  • Continuing Holt-Winters work with Aaron
  • Continuing to read Clockwork Muse
    • Martindale spends the book talking about poetry, but I’m listening to Kind of Blue right now and I realize that Jazz is similar. The thought leaders are in some state where they are paying attention to each other and not much else. That’s how we get a trajectory that leads to Bitches Brew.
    • I think this is probably a generally applicable pattern. The thing that I need to think through is how a small group of highly creative people in what could be described as an echo-ish chamber differs from mass activity in a large attractor like authoritarianism.
    • We want to know how many dimensions are needed to account for the similarities among poets. Fortunately, once we have correlated all of the poets with one another, a procedure called multidimensional scaling will tell us just this.* Multidimensional scaling tells us that the twenty-one French poets differ along three· main dimensions. These three dimensions account for 94 percent of the similarity matrix. (Page 114)
  • I have spent the day doing PHENOMENALLY STUPID MATH (Holt exponential smoothing with drecksnest damping). Excel file: ExponentialSmoothing2

Phil 12.31.18

7:00 – 4:30 ASRC NASA

  • Set up appt for physical
  • This is fabulous! Seeing Theory
    • Seeing Theory was created by Daniel Kunin while an undergraduate at Brown University. The goal of this website is to make statistics more accessible through interactive visualizations (designed using Mike Bostock’s JavaScript library D3.js).
  • Working on cleaning up and validating my Very Simple Perceptron class. I think I’m going to write the whole thing up as its own blog post
  • Why Trump Reigns as King Cyrus
    • This isn’t the religious right we thought we knew. The Christian nationalist movement today is authoritarian, paranoid and patriarchal at its core. They aren’t fighting a culture war. They’re making a direct attack on democracy itself.

 

Phil 12.29.18

Credibility in Online Social Networks: A Survey

  • The importance of information credibility in society cannot be underestimated given that it is at the heart of all decision-making. Generally, more information is better; however, knowing the value of this information is essential for decision-making processes. Information credibility defines a measure of the fitness of information for consumption. It can also be defined in terms of reliability, which denotes the probability that a data source will appear credible to the users. A challenge in this topic is that there is a great deal of literature that has developed different credibility dimensions. Additionally, information science dealing with online social networks has grown in complexity, attracting interest from researchers in information science, psychology, human-computer interaction, communication studies, and management studies, all of whom have studied the topic from different perspectives. This work will attempt to provide an overall review of the credibility assessment literature over the period 2006–2017 as applied to the context of the microblogging platform, Twitter. Known interpretations of credibility will be examined, particularly as they relate to the Twitter environment. In addition, we investigate levels of credibility assessment features. We then discuss recent works, addressing a new taxonomy of credibility analysis and assessment techniques. At last, a cross-referencing of literature is performed while suggesting new topics for future studies of credibility assessment in social media context.

Phil 12.28.18

7:00 – 4:30 ASRC NASA

  • Human mind excels at quantum-physics computer game 3o6ozkvdtdarNDhGEw
  • Continuing on the proposal:
    • [Optional] What are your success metrics for the AI system (i.e., how will you know whether the system has succeeded or failed)?
      • Discuss the spectrum of success, from classification of behavior type by syntax patterns (LSTM) to human-based manifold learning (t-sne, xxx2vec, etc) for map generation, to development of new spatial neural frameworks, potentially based on grid neurons.
    • [Optional] What else we should know?
      • I want to say something about how this is based on animal studies, and how the idea of intelligence being expensive computation has to affect any kind of collective system. Still thinking about that.
      • Also, the economic power of maps, as discussed here
    • How will you sustain and grow the impact of this work beyond this grant? How could your project and its impact grow beyond what you’ve proposed in this application?
    • Need to add a brief description of each paper and include the venue and a link

Phil 12.27.18

7:00 – 11:00 PhD

  • How Much of the Internet Is Fake? Turns Out, a Lot of It, Actually.
    • Fake people with fake cookies and fake social-media accounts, fake-moving their fake cursors, fake-clicking on fake websites — the fraudsters had essentially created a simulacrum of the internet, where the only real things were the ads.
  • More proposal. With respect to bot traffic, there is standalone, monolithic and complex behaviors that can also be tracked and used to assess the underlying information. Adversarial herding is an example.
  • Ran out of steam. Hung up on these questions:
    • [Optional] What are your success metrics for the AI system (i.e., how will you know whether the system has succeeded or failed)?
      • Discuss the spectrum of success, from classification of behavior type by syntax patterns (LSTM) to human-based manifold learning (t-sne, xxx2vec, etc) for map generation, to development of new spatial neural frameworks, potentially based on grid neurons.
    • [Optional] What else we should know?
      • I want to say something about how this is based on animal studies, and how the idea of intelligence being expensive computation has to affect any kind of collective system. Still thinking about that.
      • Also, the economic power of maps, as discussed here
    • How will you sustain and grow the impact of this work beyond this grant? How could your project and its impact grow beyond what you’ve proposed in this application?
    • Need to add a brief description of each paper and include the venue and a link

Phil 12.24.18

PhD 7:00 – 3:00

Phil 12.21.18

7:00 – 4:30 ASRC PhD/NASA/NOAA

  • Spatial Representations in the Human Brain
    • While extensive research on the neurophysiology of spatial memory has been carried out in rodents, memory research in humans had traditionally focused on more abstract, language-based tasks. Recent studies have begun to address this gap using virtual navigation tasks in combination with electrophysiological recordings in humans. These studies suggest that the human medial temporal lobe (MTL) is equipped with a population of place and grid cells similar to that previously observed in the rodent brain. Furthermore, theta oscillations have been linked to spatial navigation and, more specifically, to the encoding and retrieval of spatial information. While some studies suggest a single navigational theta rhythm which is of lower frequency in humans than rodents, other studies advocate for the existence of two functionally distinct delta–theta frequency bands involved in both spatial and episodic memory. Despite the general consensus between rodent and human electrophysiology, behavioral work in humans does not unequivocally support the use of a metric Euclidean map for navigation. Formal models of navigational behavior, which specifically consider the spatial scale of the environment and complementary learning mechanisms, may help to better understand different navigational strategies and their neurophysiological mechanisms. Finally, the functional overlap of spatial and declarative memory in the MTL calls for a unified theory of MTL function. Such a theory will critically rely upon linking task-related phenomena at multiple temporal and spatial scales. Understanding how single cell responses relate to ongoing theta oscillations during both the encoding and retrieval of spatial and non-spatial associations appears to be key toward developing a more mechanistic understanding of memory processes in the MTL.
  • Three Kinds of Spatial Cognition
    • Nora S. Newcombe (Scholar)
    • Spatial cognition is often (but wrongly) conceptualized as a single domain of cognition. However, humans function in more than one way in the spatial world. We navigate, as do all mobile animals, but we also manipulate objects using distinctive hands with opposable thumbs, unlike other species. In fact, an important characteristic of human adaptation is the ability to invent tools. Of course, another central asset is human symbolic ability, which includes the ability to spatialize thought in abstractions such as maps, graphs, and analogies. Thus, there are at least three kinds of spatial cognition with three separable functions. Navigation involves moving around the environment to find food and shelter, and to avoid danger. It draws on several interconnected neural subsystems that track movement and encode the location of external entities with respect to each other and the moving self (i.e., extrinsic coding), and it integrates these inputs to achieve best‐possible estimates. Human navigation is characterized by a great deal of individual variation. Tool use and invention involves the mental representation and transformation of the shapes of objects (i.e., intrinsic coding). It relies on substantially different neural subsystems than navigation. Like navigation, it shows marked individual differences, which are related to variations in learning in science, technology, engineering, and mathematics (STEM). Spatialization is an aspect of human symbolic skill that cuts across multiple cognitive domains and involves many kinds of spatial symbol systems, including language, metaphor, analogy, gesture, sketches, diagrams, graphs, maps, and mental images. These spatial symbol systems are vital to many kinds of learning, including in STEM. Future research on human spatial cognition needs to further delineate the origins, development, neural substrates, variability, and malleability of navigation, tool use, and abstract spatial thinking, as well as their interconnections to each other and to other cognitive skills.
  • A little bit adding to Normal Accidents notes
  • Working on saving out to history and item table
  • Turns out that if you want to retrieve floats from a postgres table using psycopg2, you have to register a custom handler:
    DEC2FLOAT = psycopg2.extensions.new_type(
        psycopg2.extensions.DECIMAL.values,
        'DEC2FLOAT',
        lambda value, curs: float(value) if value is not None else None)
    psycopg2.extensions.register_type(DEC2FLOAT)
  • Learned about dollar-quoting as per https://www.postgresql.org/docs/current/sql-syntax-lexical.html#SQL-SYNTAX-DOLLAR-QUOTING
  • To execute big inserts with psycopg2, you need to set autocommit = True
    self.conn = psycopg2.connect(config_str)
    self.conn.autocommit = True
  • And you should use try/catch
    def query_no_result(self, sql: str) -> bool:
        try:
            self.cursor.execute(sql)
            desc = self.cursor.statusmessage
            return True
        except:
            print("unable to execute: {}".format(sql))
            return False

 

Phil 12.20.18

7:00 – 4:00 ASRC NASA/PhD

  • Goal-directed navigation based on path integration and decoding of grid cells in an artificial neural network
    • As neuroscience gradually uncovers how the brain represents and computes with high-level spatial information, the endeavor of constructing biologically-inspired robot controllers using these spatial representations has become viable. Grid cells are particularly interesting in this regard, as they are thought to provide a general coordinate system of space. Artificial neural network models of grid cells show the ability to perform path integration, but important for a robot is also the ability to calculate the direction from the current location, as indicated by the path integrator, to a remembered goal. This paper presents a neural system that integrates networks of path integrating grid cells with a grid cell decoding mechanism. The decoding mechanism detects differences between multi-scale grid cell representations of the present location and the goal, in order to calculate a goal-direction signal for the robot. The model successfully guides a simulated agent to its goal, showing promise for implementing the system on a real robot in the future.
  • Path integration and the neural basis of the ‘cognitive map’
    • Accumulating evidence indicates that the foundation of mammalian spatial orientation and learning is based on an internal network that can keep track of relative position and orientation (from an arbitrary starting point) on the basis of integration of self-motion cues derived from locomotion, vestibular activation and optic flow (path integration).
    • Place cells in the hippocampal formation exhibit elevated activity at discrete spots in a given environment, and this spatial representation is determined primarily on the basis of which cells were active at the starting point and how far and in what direction the animal has moved since then. Environmental features become associatively bound to this intrinsic spatial framework and can serve to correct for cumulative error in the path integration process.
    • Theoretical studies suggested that a path integration system could involve cooperative interactions (attractor dynamics) among a population of place coding neurons, the synaptic coupling of which defines a two-dimensional attractor map. These cells would communicate with an additional group of neurons, the activity of which depends on the conjunction of movement speed, location and orientation (head direction) information, allowing position on the attractor map to be updated by self-motion information.
    • The attractor map hypothesis contains an inherent boundary problem: what happens when the animal’s movements carry it beyond the boundary of the map? One solution to this problem is to make the boundaries of the map periodic by coupling neurons at each edge to those on the opposite edge, resulting in a toroidal synaptic matrix. This solution predicts that, in a sufficiently large space, place cells would exhibit a regularly spaced grid of place fields, something that has never been observed in the hippocampus proper.
    • Recent discoveries in layer II of the medial entorhinal cortex (MEC), the main source of hippocampal afferents, indicate that these cells do have regularly spaced place fields (grid cells). In addition, cells in the deeper layers of this structure exhibit grid fields that are conjunctive for head orientation and movement speed. Pure head direction neurons are also found there. Therefore, all of the components of previous theoretical models for path integration appear in the MEC, suggesting that this network is the core of the path integration system.
    • The scale of MEC spatial firing grids increases systematically from the dorsal to the ventral poles of this structure, in much the same way as is observed for hippocampal place cells, and we show how non-periodic hippocampal place fields could arise from the combination of inputs from entorhinal grid cells, if the inputs cover a range of spatial scales rather than a single scale. This phenomenon, in the spatial domain, is analogous to the low frequency ‘beats’ heard when two pure tones of slightly different frequencies are combined.
    • The problem of how a two-dimensional synaptic matrix with periodic boundary conditions, postulated to underlie grid cell behaviour, could be self-organized in early development is addressed. Based on principles derived from Alan Turing’s theory of spontaneous symmetry breaking in chemical systems, we suggest that topographically organized, grid-like patterns of neural activity might be present in the immature cortex, and that these activity patterns guide the development of the proposed periodic synaptic matrix through a mechanism involving competitive synaptic plasticity.
  • Wormholes in virtual space: From cognitive maps to cognitive graphs
    • Cognitive maps are thought to have a metric Euclidean geometry.
    • Participants learned a non-Euclidean virtual environment with two ‘wormholes’.
    • Shortcuts reveal that spatial knowledge violates metric geometry.
    • Participants were completely unaware of the wormholes and geometric inconsistencies.
    • Results contradict a metric Euclidean map, but support a labelled ‘cognitive graph’.
  • Back to TimeSeriesML
    • Encryption class – done
      • Create a key and save it to file
      • Read a key in from a file into global variable
      • Encrypt a string if there is a key
      • Decrypt a string if there is a key
    • Postgres class – reading part is done
      • Open a global connection and cursor based on a config string
      • Run queries and return success
      • Fetch results of queries as lists of JSON objects

Phil 12.19.18

7:00 – 4:30 ASRC PhD/NASA

  • I think the IEEE paper with Antonio should be something on the math behind diversity/chaos <-> ensembles <-> hierarchies/stampedes
  • Continuing with Normal Accidents
  • Thinking about costly signalling (economics, and more generally) WRT stampede theory. It’s a form of friction. Proof-of-work could be adaptively added to a system to inhibit stampedes?
    • In contract theorysignalling is the idea that one party (termed the agent) credibly conveys some information about itself to another party (the principal). For example, in Michael Spence’s job-market signalling model, (potential) employees send a signal about their ability level to the employer by acquiring education credentials. The informational value of the credential comes from the fact that the employer believes the credential is positively correlated with having greater ability and difficult for low ability employees to obtain. Thus the credential enables the employer to reliably distinguish low ability workers from high ability workers.
  • Tweak optimizer. Change callback method to replace_with_your_method(), and add some documentation on how to use the classes
  • Move clustering paper to LaTex folder – done!
  • Pinged Antonio about the new paper ideas
  • More predictive analytics? Kind of – setting up to read and write out time series to db
  • Upgrading my postgreSQL, to read/write from a table
    • Installed drivers and python packages
    • Reading and writing in the IDE
      • Created and populated a dummy data table
    • Reading in python. Writing is next
  • I was thinking that if we run out of D&D data for other map configurations, that I can use the current data with some repurposing (replace orc with xxx) to create conversations about other environments using human speech. Kind of like PCA for DNA.