udpate

2024/documents/by-course/phil-1/paper-2/main.typ

@@ -32,10 +32,6 @@
       ],
       [
         Word Count: #total-words
-        #footnote[
-          Figure computed programmatically during document compilation. Discounts
-          content in tables and the AI contribution statement.
-        ]
       ],
     ),
   ),
@@ -43,12 +39,12 @@
 
 = Introduction
 
-The argument Against Fearing Death says that you should not fear being dead
+In _Against Fearing Death_, the author argues you should not fear being dead
 because it is not bad for you. In this paper, I reject this thesis by showing
-that the argument from hedonism with which the author supports their premises
+that the argument from hedonism the author relies on forces us to draw the
-forces us to draw incorrect conclusions.
+absurd conclusion that being alive is worse than being dead.
 
-The author states the argument against fearing death as follows on
+The author states the argument Against Fearing Death as follows on
 #cite(<Korman2022-KORLFA>, supplement: [p. 74]):
 
 #indented-argument(
@@ -70,31 +66,35 @@ under anesthesia during a risky surgery, you should not fear that either, as
 you will painlessly transition from being alive to being dead, and there is
 nothing to fear about being dead.
 
-The author justifies FD1 by denying the only possibility of rejecting it, which
+The author justifies FD1 on #cite(<Korman2022-KORLFA>, supplement: [pp. 79-82])
-is the notion that your consciousness continues after the death of your
+by denying the only possibility of rejecting it, which is the notion that your
-physical body, perhaps in an afterlife. He rejects this idea by arguing that
+consciousness continues after the death of your physical body (perhaps in an
-you, the consciousness reading this, and your physical human body, are one and
+afterlife). He rejects this idea by arguing that you, the consciousness reading
-the same (i.e. they are _numerically equivalent_). So, if your physical human
+this, and your physical human body, are one and the same (i.e. they are
-body biologically ceases to be conscious after death (which it does), then you
+_numerically equivalent_). So, if your physical human body ceases to be
-also cease to be conscious when you die.
+conscious after death (which it does), then you (the conscious being reading
+this) also cease to be conscious when you die.
 
-Premise FD3 is the direct result of FD1 and FD2, so its validity is dependent
+Premise FD3 is the direct conclusion of FD1 and FD2, so it depends on the
-entirely on FD1 and FD2. We've already shown why FD1 should be accepted. Let us
+validity of those premises. We've already shown why FD1 should be accepted. Let
-first examine FD4 before returning to FD2.
+us first examine FD4 before taking a closer look at FD2.
 
-FD4 makes a lot of intuitive sense. Indeed, upon close inspection, there are no
+FD4 makes a lot of intuitive sense, and upon closer inspection, there are
-glaring issues. It is rational to fear something if and only if it is bad for
+indeed no issues which arise. It is irrational to fear something if it is not
-you. Sure, you may fear things that are not bad for you, but these fears are
+bad for you (this does not imply everything bad for you should necessarily be
-_irrational_, you should try to resist them. The only things that it is
+feared). Sure, you _could_ fear things that are not bad for you, but as the
-rational to fear are things that are actually bad for you, such as burning your
+author states on #cite(<Korman2022-KORLFA>, supplement: [pp. 82-83]), these
-hand on a hot stove, or stubbing your toe.
+fears are _irrational_, and you should try to resist them. The only things you
+should fear are things that are actually bad for you, such as burning your hand
+on a hot stove, or stubbing your toe.
 
-Finally, we return to premise FD2. This is perhaps the most questionable
+Finally, we return to premise FD2. This is arguably the most dubious premise as
-premise as it is not immediately clear why being unconscious implies that being
+it is not immediately clear why being unconscious implies that being dead is
-dead is not bad.
+not bad.
 
 We first need a rigorous account of what exactly characterizes something as
-"bad." The author defines the following hedonist principle:
+"bad." The author defines the following _hedonist principle_ on
+#cite(<Korman2022-KORLFA>, supplement: [p. 76]):
 
 #pad(
   left: 16pt,
@@ -118,10 +118,9 @@ FD2.
     $<==>$ (FD2) So, if you cease to be conscious when you die, then being dead isn't bad for you
   ],
 )
-
 I've slightly modified the author's numberings to emphasize that the conclusion
 AH3 is equivalent to the premise FD2. That is, if the #smallcaps[Argument from
-Hedonism] holds, then FD2 must be true.
+Hedonism] holds, then FD2 must also be true.
 
 We've shown that the author's argument for why you should not fear death is
 substantiated by what appear to be valid premises. I object in section 2 by
@@ -132,15 +131,17 @@ responses to my objection.
 
 = Don't kill yourself
 
-I will advance the argument that we should reject HD\* because its logical
+I advance the argument that we should reject HD\* because its logical
-conclusion is that you should kill yourself. HD\* implies that being dead is
+conclusion is not only that being dead is not bad for you, but _being alive_ is
-not bad for you, but it also implies that _being alive_ is bad for you.
+actually bad for you. Accordingly, this implies that you should kill yourself
+to stop being alive because it is bad for you. Of course, this is wrong and we
+should not accept HD\*. Consider the following argument:
 
 #indented-argument(
   title: "Argument for Killing Yourself",
   abbreviation: "KYS",
   [You are occasionally conscious when you are alive],
-  [If you are conscious, you experience more pain than you otherwise would have if you were unconscious],
+  [If you are occasionally conscious, you will experience more pain than you otherwise would have if you were unconscious],
   [$<==>$ (HD\*) Something is bad for you if and only if it results in more pain than you otherwise would have had],
   [So, being alive is bad for you],
   [If you are unconscious when you are dead, then being dead isn't bad for you],
@@ -148,37 +149,90 @@ not bad for you, but it also implies that _being alive_ is bad for you.
   [So, you should stop being alive],
 )
 
-In other words, KYS7 plainly states that you should kill yourself to stop being
+In other words, KYS7 states that you should find a way to kill yourself in
-alive. This is clearly a ridiculous conclusion and one we should not accept. In
+order to stop being alive. This is an absurd conclusion we should not accept,
-order to reject this conclusion, we must reject KYS3, which is HD\*. All of the
+and it indicates a serious error with one of our premise. Let us identify
-other premises can be substantiated easily, as follows.
+exactly which one went wrong.
 
-KYS1 is trivial. KYS2 is true because you experience no pain when unconscious,
+KYS1 is trivial (unless you are unconscious for the rest of your life, which
-and we certainly experience pain at some point while conscious. So, we must
+for our purposes is essentially the same as death).
-experience more pain while conscious than we otherwise would have (while
+
-unconscious).
+KYS2 is true because you experience no pain when unconscious, and we certainly
+experience pain at some point while conscious. So, we must experience more pain
+while conscious than we otherwise would have (while unconscious).
+
+KYS5 is equivalent to our conclusion AH3 in the #smallcaps[Argument from
+Hedonism].
 
 KYS6 essentially just says that if being alive is bad and being dead is good,
 then you should take action to stop being alive and start being dead. After
-all, why continue doing something that is bad for you when the alternative not
+all, why continue doing something that is bad for you when the alternative is
-bad for you?
+not bad for you?
 
-Clearly, our own avenue forward is to reject HD\* as our principle of hedonism.
+Clearly, our only option is to reject KYS3 (which is just HD\*) as our
-Without HD\*, the #smallcaps[Argument from Hedonism] no longer stands, and
+principle of hedonism. Without HD\*, the #smallcaps[Argument from Hedonism] no
-therefore the argument for FD2 fails.
+longer stands, and therefore the argument for FD2 fails.
+
+Note that we do not make a claim as to whether or not death is bad for you. We
+simply show the absurdity of an argument that relies on HD\*, which means the
+#smallcaps[Argument from Hedonism] fails to justify the premise FD2. Without a
+clear justification for why we should accept the dubious claim in FD2, we can
+no longer claim that death is definitely not bad and should not be feared.
+
+// #let hdp = [HD$'$]
+//
+// Let us formulate a new hedonist principle, denoted #hdp.
+//
+// #pad(
+//   left: 16pt,
+//   [
+//     (#hdp) Something is bad for you if and only if it prevents or hinders the achievement of your goals
+//   ],
+// )
+//
+// We need to clarify what is meant by "goals". In this case, goals refers broadly
+// to all of the things someone needs to feel fulfilled. Someone may have a few
+// _fundamental goals_, such as to be fulfilled or to feel happy.
+//
+// Anything that works against these goals is bad. If someone seeks to be happy,
+// then feeling pain is bad for them because they no longer .
+//
+// The author's hypothetical case of #smallcaps[Unread Mail] shows that this
+// HD\*\* wrongfully characterizes some situations as bad, so we should prefer
+// HD\*.
+//
+// However, in my formulation #hdp, we do correctly identify that the situation in
+// #smallcaps[Unread Mail] is not bad. #hdp is essentially equivalent to HD\* is
+// most cases, but it successfully identifies that being alive is not bad for you.
+// Since being
 
 = Possible objections
 
 One might attempt to object to KYS without rejecting HD\*. The only other
-premises to object against are KYS1 and KYS2. In particular, one might raise
+premises to reasonably object against are KYS1 and KYS2. In particular, one
-the concern that someone could be alive without ever being conscious (as an
+might raise the following concerns
-objection to KYS1), and that someone may not experience any pain while
-conscious (an objection to KYS2).
 
-These criticisms are not substantial enough for us to reject KYS. KYS1 brings
++ An objection to KYS1: someone could be alive without ever being conscious.
-up a valid point, but being unconscious for the rest of your life is not really
++ An objection to KYS2: someone may not ever experience any pain while conscious.
-substantially different from being dead. Regardless, we could modify our
+
-argument to
+These criticisms are not really substantial enough for us to reject KYS. KYS1
+brings up a valid point, but being unconscious for the rest of your life is not
+really a better situation than being dead. Regardless, HD\* still implies that
+being conscious is bad while being in a _death-like state_ of permanent
+unconsciousness is not. The conclusion then becomes that you should either kill
+yourself or place yourself into a death-like state (perhaps a coma), which is
+just as absurd as before.
+
+One may argue from a hypothetical situation in which a human is somehow
+modified to be incapable of feeling pain (of any sort). In this situation, HD\*
+does not fail, as it does not imply being alive and conscious is worse than
+being dead, since being conscious and being dead both result in absolutely no
+pain.
+
+This case fails to present any challenge to our argument. Even though HD\* does
+not fail in the hypothetical, it clearly still fails _now_, as it still implies
+that being alive is bad for you, the person reading, who almost certainly does
+feel pain.
 
 #pagebreak()
 

2024/documents/by-name/vision-whitepaper/appendix.typ

@@ -0,0 +1,5 @@
+= Appendix / supplemental material
+
+Optionally include supplemental material (complete proofs, additional
+experiments and plots) in appendix. All such materials *SHOULD be included in
+the main submission*.

2024/documents/by-name/vision-whitepaper/checklist.typ

@@ -0,0 +1,524 @@
+#import "@preview/bloated-neurips:0.5.1": url
+
+#let TODO = text(fill: red, [*[TODO]*])
+#let answerNA = text(fill: gray, "[NA]")
+#let answerNo = text(fill: red, "[NO]")
+#let answerYes = text(fill: blue, "[YES]")
+
+#pagebreak(weak: true)
+
+#set heading(numbering: none)
+
+= NeurIPS Paper Checklist
+
+#let claim(
+  name: [],
+  question: [],
+  answer: [],
+  justification: [],
+  guidelines: [],
+) = {
+  set list(indent: 1em, tight: false)
+  show list: set block(spacing: 10pt)
+  set par(spacing: 5.8pt)
+  [
+    *#name*
+
+    Question: #question
+
+    Answer: #answer
+
+    Justification: #justification
+
+    Guidelines:
+
+    #guidelines
+  ]
+}
+
++ #claim(
+  name: [Claims],
+  question: [
+    Do the main claims made in the abstract and introduction accurately reflect
+    the paper's contributions and scope?],
+  answer: TODO,  // Replace by answerYes, answerNo, or answerNA.
+  justification: TODO,
+  guidelines: [
+  - The answer NA means that the abstract and introduction do not include the
+    claims made in the paper.
+
+  - The abstract and/or introduction should clearly state the claims made,
+    including the contributions made in the paper and important assumptions and
+    limitations. A No or NA answer to this question will not be perceived well
+    by the reviewers.
+
+  - The claims made should match theoretical and experimental results, and
+    reflect how much the results can be expected to generalize to other settings.
+
+  - It is fine to include aspirational goals as motivation as long as it is
+    clear that these goals are not attained by the paper.
+  ])
+
++ #claim(
+  name: [Limitations],
+  question: [
+    Does the paper discuss the limitations of the work performed by the
+    authors?
+  ],
+  answer: TODO,  // Replace by answerYes, answerNo, or answerNA.
+  justification: TODO,
+  guidelines: [
+    - The answer NA means that the paper has no limitation while the answer No
+      means that the paper has limitations, but those are not discussed in the
+      paper.
+
+    - The authors are encouraged to create a separate "Limitations" section in
+      their paper.
+
+    - The paper should point out any strong assumptions and how robust the
+      results are to violations of these assumptions (e.g., independence
+      assumptions, noiseless settings, model well-specification, asymptotic
+      approximations only holding locally). The authors should reflect on how
+      these assumptions might be violated in practice and what the implications
+      would be.
+
+    - The authors should reflect on the scope of the claims made, e.g., if the
+      approach was only tested on a few datasets or with a few runs. In
+      general, empirical results often depend on implicit assumptions, which
+      should be articulated.
+
+    - The authors should reflect on the factors that influence the performance
+      of the approach. For example, a facial recognition algorithm may perform
+      poorly when image resolution is low or images are taken in low lighting.
+      Or a speech-to-text system might not be used reliably to provide closed
+      captions for online lectures because it fails to handle technical jargon.
+
+    - The authors should discuss the computational efficiency of the proposed
+      algorithms and how they scale with dataset size.
+
+    - If applicable, the authors should discuss possible limitations of their
+      approach to address problems of privacy and fairness.
+
+    - While the authors might fear that complete honesty about limitations
+      might be used by reviewers as grounds for rejection, a worse outcome
+      might be that reviewers discover limitations that aren't acknowledged in
+      the paper. The authors should use their best judgment and recognize that
+      individual actions in favor of transparency play an important role in
+      developing norms that preserve the integrity of the community. Reviewers
+      will be specifically instructed to not penalize honesty concerning
+      limitations.
+  ])
+
++ #claim(
+  name: [Theory Assumptions and Proofs],
+  question: [
+    For each theoretical result, does the paper provide the full set of
+    assumptions and a complete (and correct) proof?
+  ],
+  answer: TODO,  // Replace by answerYes, answerNo, or answerNA.
+  justification: TODO,
+  guidelines: [
+    - The answer NA means that the paper does not include theoretical results.
+
+    - All the theorems, formulas, and proofs in the paper should be numbered
+      and cross-referenced.
+
+    - All assumptions should be clearly stated or referenced in the statement
+      of any theorems.
+
+    - The proofs can either appear in the main paper or the supplemental
+      material, but if they appear in the supplemental material, the authors
+      are encouraged to provide a short proof sketch to provide intuition.
+
+    - Inversely, any informal proof provided in the core of the paper should be
+      complemented by formal proofs provided in appendix or supplemental
+      material.
+
+    - Theorems and Lemmas that the proof relies upon should be properly
+      referenced.
+  ])
+
++ #claim(
+  name: [Experimental Result Reproducibility],
+  question: [
+    Does the paper fully disclose all the information needed to reproduce the
+    main experimental results of the paper to the extent that it affects the
+    main claims and/or conclusions of the paper (regardless of whether the code
+    and data are provided or not)?
+  ],
+  answer: TODO,  // Replace by answerYes, answerNo, or answerNA.
+  justification: TODO,
+  guidelines: [
+    - The answer NA means that the paper does not include experiments.
+
+    - If the paper includes experiments, a No answer to this question will not
+      be perceived well by the reviewers: Making the paper reproducible is
+      important, regardless of whether the code and data are provided or not.
+
+    - If the contribution is a dataset and/or model, the authors should
+      describe the steps taken to make their results reproducible or verifiable.
+
+    - Depending on the contribution, reproducibility can be accomplished in
+      various ways. For example, if the contribution is a novel architecture,
+      describing the architecture fully might suffice, or if the contribution
+      is a specific model and empirical evaluation, it may be necessary to
+      either make it possible for others to replicate the model with the same
+      dataset, or provide access to the model. In general. releasing code and
+      data is often one good way to accomplish this, but reproducibility can
+      also be provided via detailed instructions for how to replicate the
+      results, access to a hosted model (e.g., in the case of a large language
+      model), releasing of a model checkpoint, or other means that are
+      appropriate to the research performed.
+
+    - While NeurIPS does not require releasing code, the conference does
+      require all submissions to provide some reasonable avenue for
+      reproducibility, which may depend on the nature of the contribution. For
+      example
+
+      #set enum(numbering: "(a)")
+
+      + If the contribution is primarily a new algorithm, the paper should make
+        it clear how to reproduce that algorithm.
+      + If the contribution is primarily a new model architecture, the paper
+        should describe the architecture clearly and fully.
+      + If the contribution is a new model (e.g., a large language model), then
+        there should either be a way to access this model for reproducing the
+        results or a way to reproduce the model (e.g., with an open-source
+        dataset or instructions for how to construct the dataset).
+      + We recognize that reproducibility may be tricky in some cases, in which
+        case authors are welcome to describe the particular way they provide
+        for reproducibility. In the case of closed-source models, it may be
+        that access to the model is limited in some way (e.g., to registered
+        users), but it should be possible for other researchers to have some
+        path to reproducing or verifying the results.
+  ])
+
++ #claim(
+  name: [Open access to data and code],
+  question: [
+    Does the paper provide open access to the data and code, with sufficient
+    instructions to faithfully reproduce the main experimental results, as
+    described in supplemental material?
+  ],
+  answer: TODO,  // Replace by answerYes, answerNo, or answerNA.
+  justification: TODO,
+  guidelines: [
+    - The answer NA means that paper does not include experiments requiring
+      code.
+
+    - Please see the NeurIPS code and data submission guidelines
+      (#url("https://nips.cc/public/guides/CodeSubmissionPolicy")) for more
+      details.
+
+    - While we encourage the release of code and data, we understand that this
+      might not be possible, so "No" is an acceptable answer. Papers cannot be
+      rejected simply for not including code, unless this is central to the
+      contribution (e.g., for a new open-source benchmark).
+
+    - The instructions should contain the exact command and environment needed
+      to run to reproduce the results. See the NeurIPS code and data submission
+      guidelines (#url("https://nips.cc/public/guides/CodeSubmissionPolicy"))
+      for more details.
+
+    - The authors should provide instructions on data access and preparation,
+      including how to access the raw data, preprocessed data, intermediate
+      data, and generated data, etc.
+
+    - The authors should provide scripts to reproduce all experimental results
+      for the new proposed method and baselines. If only a subset of
+      experiments are reproducible, they should state which ones are omitted
+      from the script and why.
+
+    - At submission time, to preserve anonymity, the authors should release
+      anonymized versions (if applicable).
+
+    - Providing as much information as possible in supplemental material
+      (appended to the paper) is recommended, but including URLs to data and
+      code is permitted.
+  ])
+
+
++ #claim(
+  name: [Experimental Setting/Details],
+  question: [
+    Does the paper specify all the training and test details (e.g., data
+    splits, hyperparameters, how they were chosen, type of optimizer, etc.)
+    necessary to understand the results?
+  ],
+  answer: TODO,  // Replace by answerYes, answerNo, or answerNA.
+  justification: TODO,
+  guidelines: [
+    - The answer NA means that the paper does not include experiments.
+
+    - The experimental setting should be presented in the core of the paper to
+      a level of detail that is necessary to appreciate the results and make
+      sense of them.
+
+    - The full details can be provided either with the code, in appendix, or as
+      supplemental material.
+  ])
+
++ #claim(
+  name: [Experiment Statistical Significance],
+  question: [
+    Does the paper report error bars suitably and correctly defined or other
+    appropriate information about the statistical significance of the
+    experiments?
+  ],
+  answer: TODO,  // Replace by answerYes, answerNo, or answerNA.
+  justification: TODO,
+  guidelines: [
+    - The answer NA means that the paper does not include experiments.
+
+    - The authors should answer "Yes" if the results are accompanied by error
+      bars, confidence intervals, or statistical significance tests, at least
+      for the experiments that support the main claims of the paper.
+
+    - The factors of variability that the error bars are capturing should be
+      clearly stated (for example, train/test split, initialization, random
+      drawing of some parameter, or overall run with given experimental
+      conditions).
+
+    - The method for calculating the error bars should be explained (closed
+      form formula, call to a library function, bootstrap, etc.)
+
+    - The assumptions made should be given (e.g., Normally distributed errors).
+
+    - It should be clear whether the error bar is the standard deviation or the
+      standard error of the mean.
+
+    - It is OK to report 1-sigma error bars, but one should state it. The
+      authors should preferably report a 2-sigma error bar than state that they
+      have a 96% CI, if the hypothesis of Normality of errors is not verified.
+
+    - For asymmetric distributions, the authors should be careful not to show
+      in tables or figures symmetric error bars that would yield results that
+      are out of range (e.g. negative error rates).
+
+    - If error bars are reported in tables or plots, The authors should explain
+      in the text how they were calculated and reference the corresponding
+      figures or tables in the text.
+  ])
+
++ #claim(
+  name: [Experiments Compute Resources],
+  question: [
+    For each experiment, does the paper provide sufficient information on the
+    computer resources (type of compute workers, memory, time of execution)
+    needed to reproduce the experiments?
+  ],
+  answer: TODO,  // Replace by answerYes, answerNo, or answerNA.
+  justification: TODO,
+  guidelines: [
+    - The answer NA means that the paper does not include experiments.
+
+    - The paper should indicate the type of compute workers CPU or GPU,
+      internal cluster, or cloud provider, including relevant memory and storage.
+
+    - The paper should provide the amount of compute required for each of the
+      individual experimental runs as well as estimate the total compute.
+
+    - The paper should disclose whether the full research project required more
+      compute than the experiments reported in the paper (e.g., preliminary or
+      failed experiments that didn't make it into the paper).
+  ])
+
++ #claim(
+  name: [Code Of Ethics],
+  question: [
+    Does the research conducted in the paper conform, in every respect, with
+    the NeurIPS Code of Ethics
+    #url("https://neurips.cc/public/EthicsGuidelines")
+  ],
+  answer: TODO,  // Replace by answerYes, answerNo, or answerNA.
+  justification: TODO,
+  guidelines: [
+    - The answer NA means that the authors have not reviewed the NeurIPS Code
+      of Ethics.
+
+    - If the authors answer No, they should explain the special circumstances
+      that require a deviation from the Code of Ethics.
+
+    - The authors should make sure to preserve anonymity (e.g., if there is a
+      special consideration due to laws or regulations in their jurisdiction).
+  ])
+
++ #claim(
+  name: [Broader Impacts],
+  question: [
+    Does the paper discuss both potential positive societal impacts and
+    negative societal impacts of the work performed?
+  ],
+  answer: TODO,  // Replace by answerYes, answerNo, or answerNA.
+  justification: TODO,
+  guidelines: [
+    - The answer NA means that there is no societal impact of the work
+      performed.
+
+    - If the authors answer NA or No, they should explain why their work has no
+      societal impact or why the paper does not address societal impact.
+
+    - Examples of negative societal impacts include potential malicious or
+      unintended uses (e.g., disinformation, generating fake profiles,
+      surveillance), fairness considerations (e.g., deployment of technologies
+      that could make decisions that unfairly impact specific groups), privacy
+      considerations, and security considerations.
+
+    - The conference expects that many papers will be foundational research and
+      not tied to particular applications, let alone deployments. However, if
+      there is a direct path to any negative applications, the authors should
+      point it out. For example, it is legitimate to point out that an
+      improvement in the quality of generative models could be used to generate
+      deepfakes for disinformation. On the other hand, it is not needed to
+      point out that a generic algorithm for optimizing neural networks could
+      enable people to train models that generate Deepfakes faster.
+
+    - The authors should consider possible harms that could arise when the
+      technology is being used as intended and functioning correctly, harms
+      that could arise when the technology is being used as intended but gives
+      incorrect results, and harms following from (intentional or
+      unintentional) misuse of the technology.
+
+    - If there are negative societal impacts, the authors could also discuss
+      possible mitigation strategies (e.g., gated release of models, providing
+      defenses in addition to attacks, mechanisms for monitoring misuse,
+      mechanisms to monitor how a system learns from feedback over time,
+      improving the efficiency and accessibility of ML).
+  ])
+
++ #claim(
+  name: [Safeguards],
+  question: [
+    Does the paper describe safeguards that have been put in place for
+    responsible release of data or models that have a high risk for misuse
+    (e.g., pretrained language models, image generators, or scraped datasets)?
+  ],
+  answer: TODO,  // Replace by answerYes, answerNo, or answerNA.
+  justification: TODO,
+  guidelines: [
+    - The answer NA means that the paper poses no such risks.
+
+    - Released models that have a high risk for misuse or dual-use should be
+      released with necessary safeguards to allow for controlled use of the
+      model, for example by requiring that users adhere to usage guidelines or
+      restrictions to access the model or implementing safety filters.
+
+    - Datasets that have been scraped from the Internet could pose safety
+      risks. The authors should describe how they avoided releasing unsafe
+      images.
+
+    - We recognize that providing effective safeguards is challenging, and many
+      papers do not require this, but we encourage authors to take this into
+      account and make a best faith effort.
+  ])
+
++ #claim(
+  name: [Licenses for existing assets],
+  question: [
+    Are the creators or original owners of assets (e.g., code, data, models),
+    used in the paper, properly credited and are the license and terms of use
+    explicitly mentioned and properly respected?
+  ],
+  answer: TODO,  // Replace by answerYes, answerNo, or answerNA.
+  justification: TODO,
+  guidelines: [
+    - The answer NA means that the paper does not use existing assets.
+
+    - The authors should cite the original paper that produced the code package
+      or dataset.
+
+    - The authors should state which version of the asset is used and, if
+      possible, include a URL.
+
+    - The name of the license (e.g., CC-BY 4.0) should be included for each
+      asset.
+
+    - For scraped data from a particular source (e.g., website), the copyright
+      and terms of service of that source should be provided.
+
+    - If assets are released, the license, copyright information, and terms of
+      use in the package should be provided. For popular datasets,
+      #url("https://paperswithcode.com/datasets") has curated licenses for some
+      datasets. Their licensing guide can help determine the license of a
+      dataset.
+
+    - For existing datasets that are re-packaged, both the original license and
+      the license of the derived asset (if it has changed) should be provided.
+
+    - If this information is not available online, the authors are encouraged
+      to reach out to the asset's creators.
+  ])
+
++ #claim(
+  name: [New Assets],
+  question: [
+    Are new assets introduced in the paper well documented and is the
+    documentation provided alongside the assets?
+  ],
+  answer: TODO,  // Replace by answerYes, answerNo, or answerNA.
+  justification: TODO,
+  guidelines: [
+    - The answer NA means that the paper does not release new assets.
+
+    - Researchers should communicate the details of the dataset/code/model as
+      part of their submissions via structured templates. This includes details
+      about training, license, limitations, etc.
+
+    - The paper should discuss whether and how consent was obtained from people
+      whose asset is used.
+
+    - At submission time, remember to anonymize your assets (if applicable).
+      You can either create an anonymized URL or include an anonymized zip file.
+  ])
+
++ #claim(
+  name: [Crowdsourcing and Research with Human Subjects],
+  question: [
+    For crowdsourcing experiments and research with human subjects, does the
+    paper include the full text of instructions given to participants and
+    screenshots, if applicable, as well as details about compensation (if any)?
+  ],
+  answer: TODO,  // Replace by answerYes, answerNo, or answerNA.
+  justification: TODO,
+  guidelines: [
+    - The answer NA means that the paper does not involve crowdsourcing nor
+      research with human subjects.
+
+    - Including this information in the supplemental material is fine, but if
+      the main contribution of the paper involves human subjects, then as much
+      detail as possible should be included in the main paper.
+
+    - According to the NeurIPS Code of Ethics, workers involved in data
+      collection, curation, or other labor should be paid at least the minimum
+      wage in the country of the data collector.
+  ])
+
++ #claim(
+  name: [
+    Institutional Review Board (IRB) Approvals or Equivalent for Research with
+    Human Subjects
+  ],
+  question: [
+    Does the paper describe potential risks incurred by study participants,
+    whether such risks were disclosed to the subjects, and whether
+    Institutional Review Board (IRB) approvals (or an equivalent
+    approval/review based on the requirements of your country or institution)
+    were obtained?
+  ],
+  answer: TODO, // Replace by answerYes, answerNo, or answerNA.
+  justification: TODO,
+  guidelines: [
+    - The answer NA means that the paper does not involve crowdsourcing nor
+      research with human subjects.
+
+    - Depending on the country in which research is conducted, IRB approval (or
+      equivalent) may be required for any human subjects research. If you
+      obtained IRB approval, you should clearly state this in the paper.
+
+    - We recognize that the procedures for this may vary significantly between
+      institutions and locations, and we expect authors to adhere to the
+      NeurIPS Code of Ethics and the guidelines for their institution.
+
+    - For initial submissions, do not include any information that would break
+      anonymity (if applicable), such as the institution conducting the review.
+  ])

2024/documents/by-name/vision-whitepaper/logo.typ

@@ -0,0 +1,18 @@
+#let kern(length) = h(length, weak: true)
+#let TeX = style(styles => {
+  let e = measure(text("E"), styles)
+  let T = "T"
+  let E = text(baseline: e.height / 2, "E")
+  let X = "X"
+  box(T + kern(-0.1667em) + E + kern(-0.125em) + X)
+})
+#let LaTeX = style(styles => {
+  let l = measure(text(10pt, "L"), styles)
+  let a = measure(text(7pt, "A"), styles)
+  let L = "L"
+  let A = text(7pt, baseline: a.height - l.height, "A")
+  box(L + kern(-0.36em) + A + kern(-0.15em) + TeX)
+})
+#let LaTeXe = style(styles => {
+  box(LaTeX + sym.space.sixth + [2#text(baseline: 0.3em, $epsilon$)])
+})

2024/documents/by-name/vision-whitepaper/main.bib

@@ -0,0 +1,25 @@
+@article{alexander1994template,
+  title={Template-based algorithms for connectionist rule extraction},
+  author={Alexander, Jay and Mozer, Michael C},
+  journal={Advances in neural information processing systems},
+  volume={7},
+  year={1994}
+}
+
+@book{bower2012book,
+  title={The book of GENESIS: exploring realistic neural models with the GEneral NEural SImulation System},
+  author={Bower, James M and Beeman, David},
+  year={2012},
+  publisher={Springer Science \& Business Media}
+}
+
+@article{hasselmo1995dynamics,
+  title={Dynamics of learning and recall at excitatory recurrent synapses and cholinergic modulation in rat hippocampal region CA3},
+  author={Hasselmo, Michael E and Schnell, Eric and Barkai, Edi},
+  journal={Journal of Neuroscience},
+  volume={15},
+  number={7},
+  pages={5249--5262},
+  year={1995},
+  publisher={Soc Neuroscience}
+}

2024/documents/by-name/vision-whitepaper/main.typ

@@ -0,0 +1,99 @@
+#import "@preview/bloated-neurips:0.5.1": (
+  botrule,
+  midrule,
+  neurips2024,
+  paragraph,
+  toprule,
+  url,
+)
+#import "./logo.typ": LaTeX, LaTeXe, TeX
+
+#let affls = (
+  ucsb: (
+    // department: "AI Center",
+    institution: "University of California, Santa Barbara",
+    country: "United States",
+  ),
+)
+
+#let authors = (
+  (
+    name: "Youwen Wu",
+    affl: "ucsb",
+    email: "youwen@ucsb.edu",
+    equal: true,
+  ),
+)
+
+#show: neurips2024.with(
+  title: [Towards More Accessible Scientific Infrastructure: A Neural Vision Pipeline to Interface with Experiments],
+  authors: (authors, affls),
+  keywords: ("Machine Learning", "NeurIPS"),
+  abstract: [
+    Scientific instruments are often designed to be operated by humans. As
+    such, they are outfitted with analog dials and controls which are difficult
+    for machines to understand. In order to ameliorate the inaccessibility of
+    experimental equipment in fundamental disciplines such as quantum physics,
+    we seek a systematic approach to processing existing _analog systems_ into
+    _digital data_ without invasively augmenting them with sensors. In this
+    paper, we explore the state of the art in computer vision and their
+    applications in analyzing experimental instruments through a purely vision
+    based approach. We train a convolutional neural network to triangulate
+    visual fiducials and construct a pipeline to apply perspective warp based
+    corrections to normalize images of measurements. We end by designing
+    _Dendrite_, an end-to-end vision pipeline that can obtain detailed
+    digital readings from a video stream of an analog instrument.
+  ],
+  bibliography: bibliography("main.bib"),
+  bibliography-opts: (title: none, full: true),  // Only for example paper.
+  appendix: [
+    #include "appendix.typ"
+    #include "checklist.typ"
+  ],
+  accepted: true,
+)
+
+= Introduction
+
+The rise of online resources in scientific pedagogy has become increasingly
+prevalent. Around the world, students use virtual labs that simulate physical
+phenomena. However, still lacking is the accessibility of real world hardware
+to obtain real results. Experimental instruments are expensive and difficult to
+justify for many schools and institutions. One solution to this problem is to
+provide shared equipment that is accessible and controlled over the internet.
+This allows equipment located in a single place to be used from anywhere in the
+world.
+
+One way to build these systems is to augment existing devices with the
+capability to be controlled over the internet. However, many scientific
+instruments are designed with human operation in mind and contain many analog
+dials, readouts, and controls. We seek a way to non-invasively digitize these
+devices. Here non-invasively means that we should not perform any irreversible
+or drastic changes to the hardware. Digitize refers to obtaining all relevant
+outputs as digital data that can be processed by computers, and being able to
+operate relevant controls over digital protocols (such as the internet). In
+this paper, we focus primarily on obtaining the outputs.
+
+We propose a system which uses an end-to-end vision pipeline that can scan
+readouts and translate them into data. Then, the data can be streamed to
+virtual simulations which will react exactly as the real life equipment does.
+
+== Requirements
+
+Our end-to-end pipeline will consist of a component to locate the desired
+instrument in the image and determine the corrections needed to transform the
+image into a point of view where it is directly visible. This may be a neural
+network based model that identifies a key fiducial from which we can
+extrapolate the perspective transforms needed to bring the image to a
+normalized state (here normalized refers to a flattened 2D image that can be
+easily analyzed by computer vision).
+
+We then extrapolate from that data to map out all of the various points of
+interest. From that point, we can run specialized models on readouts such as
+dials to determine their readings.
+
+= The state of the art
+
+We first
+
+

2024/documents/by-name/vision-whitepaper/package.nix

@@ -0,0 +1,35 @@
+{
+  pkgs,
+  typstPackagesCache,
+  typixLib,
+  cleanTypstSource,
+  ...
+}:
+let
+  src = cleanTypstSource ./.;
+  commonArgs = {
+    typstSource = "main.typ";
+
+    fontPaths = [
+      # Add paths to fonts here
+      # "${pkgs.roboto}/share/fonts/truetype"
+    ];
+
+    virtualPaths = [
+      # Add paths that must be locally accessible to typst here
+      # {
+      #   dest = "icons";
+      #   src = "${inputs.font-awesome}/svgs/regular";
+      # }
+    ];
+
+    XDG_CACHE_HOME = typstPackagesCache;
+  };
+
+in
+typixLib.buildTypstProject (
+  commonArgs
+  // {
+    inherit src;
+  }
+)