Because Wikipedia's article on them are all grad student-level gobbledegook.
I need something more on the level of A Brief History of Time or Cosmos.
Hi BA, I've been doing research for weeks and am struggling to figure out what parts I need to get the most juice out of my $500. I want a system primarily for music listening with movies and production or mixing secondary (so do not NEED a flat signature) I'll be moving the system between a desk and living room.
room specs are 1,000 sq ft and would like enough sound for a small 10 ish person party. Will mainly be plugging in a 3.5 mm but would like the option to bluetooth in preferably from a DAC.
Should I just get a DAC and 2 bookshelf speaker combo? Do I bother with an amp or just get the best powered speakers I can? WHen's a good time to buy speakers?
Thank you for any help!
EDIT: PARTY NOW FULL THANK YOU.
Hello there,
I'm a DM (1.6 YEARS XP)/ newbie player (6 sessions as a PC), looking for a group to either DM for or play with as a PC.
I generally don't mind what we do, however my experience is generally with D&D 5E, I am open to new settings and systems so please don't exclude anything you might want to run.
I've ran homebrewed campaigns (50/50, RP/combat split) mostly, however I did dabble in a session zero for Ravenloft, before the party fell apart due to conflicting commitments. I've also done a few sessions with a local group as a PC in a homebrewed campaign but again unfortunately due to conflicting commitments that too fell apart rather quickly.
If anybody is interested in either adoption for myself as a PC or if anybody wants to join a homebrew setting I've made please DM me or comment below.
I mainly use roll20 and Skype however I'm open to anything that gets us playing.
EVERYBODY IS WELCOME SO PLEASE BE WELCOMING, NO DISCRIMINATION OR PAID SESSIONS.
So I just started on Quantum Mechanics recently because of a youtube video about invisibility. I basically had the question of how indistinguishable particles could exist at first and I followed the rabbit hole of the Exclusion Principle.
I get that the basic distinction between bosons and fermions is due to eigenvalues and basically definition. The consequences of which are that indistinguishable fermions can never be in the same quantum state but bosons can. I donβt understand however where to start my search on how we know bosons exist and also why their spin properties inherently differ. Thanks in advance!
Hi all
In my signals and systems course, we have been given the following difference equation:
π¦[π] = π₯[π] + π¦[π β 1] + π¦[π + 2]
Which we have shown to be both unstable and non-causal. We were given the question "what is the purpose of this system". Since it is both unstable and non-causal, I would believe that it does not have a purpose, as I cannot find a useful purpose for such a system, but maybe I am missing something?
August 14 - 15, 2021
---
Submission deadline (extended): May 27, 2021, anywhere on Earth
Format: 3 page extended abstract + references + appendices, ACM Proceeding Template
Submission website: https://cmt3.research.microsoft.com/BCIRWIS2021
---
Increasingly we use machine learning to build interactive systems that learn from past actions and the reward obtained. Theory suggests several possible approaches, such as contextual bandits, reinforcement learning, the do-calculus, or plain old Bayesian decision theory. What are the most theoretically appropriate and practical approaches to doing causal inference for interactive systems?
We are particularly interested in case studies of applying machine learning methods to interactive systems that did or did not use Bayesian or likelihood based methods, with a discussion about why this choice was made in terms of practical or theoretical arguments. We also welcome submissions in the following areas:
- Offline evaluation of recommender and interactive systems.
- Comparison of Bayesian, off-policy and other heuristic approaches for offline metrics.
- Probabilistic approaches applied to contextual bandits and reinforcement learning approaches.
- Probabilistic approaches to incrementality and attribution.
- Non-Bayesian approaches and trade-offs with Bayesian/Likelihood approaches.
- Bayesian methods in a production environment.
Organizers
- Nicholas Chopin (ENSAE)
- Mike Gartrell (Criteo AI Lab)
- Dawen Liang (Netflix)
- Alberto Lumbreras (Criteo AI Lab)
- David Rohde (Criteo AI Lab)
- Yixin Wang (UC Berkeley)
I have posted a picture of the question and the 4 possible answers but I can't for the life of my figure it out. any help would be greatly appreciated!
https://preview.redd.it/alu1pcp16tu61.png?width=616&format=png&auto=webp&s=2fe4e7a01ad513f799119862bc270e07f24b838a
Racial discrimination, given it's immense relevance in today's political discourse as well as it's longstanding role in the United Statesβ history, has been the subject of an immense amount of research in economics.
Questions like "what is the causal effect of race on the probability of receiving a loan?" and, with renewed fervor in recent years questions like "what is the effect of race on things like police use of force, probability of being arrested, and conditional on being arrested, what's the probability of being prosecuted?". This R1 is about https://5harad.com/papers/post-treatment-bias.pdf (Goel et al from now on), which is itself a rebuttal to https://scholar.princeton.edu/sites/default/files/jmummolo/files/klm.pdf, (Mummolo et al) which is itself a rebuttal to papers like https://scholar.harvard.edu/fryer/publications/empirical-analysis-racial-differences-police-use-force (Freyer) which try to estimate the role of race in police use of force.
Mummolo et al is making the argument that common causal estimates of the effect of race on police-related outcomes are biased. Fivethirtyeight does a good job outlining the case here https://fivethirtyeight.com/features/why-statistics-dont-capture-the-full-extent-of-the-systemic-bias-in-policing/ but the basic idea is that if you believe that police are more likely to arrest minorities then your set of arrest records is a biased sample and will produce biased estimates of the effect of race on police-related outcomes.
The paper I am R1ing is about the question "conditional on being arrested, what is the effect of race on the probability of being prosecuted?" Goel et al use a set of covariates, including data from the police report and the arresteeβs race to try and get a causal estimate of the effect of race on the decision to prosecute. They claim that the problems outlined by Mummolo et al do not apply. They cite that in their sample, conditional on the details in the police report, White people who are arrested are prosecuted 51% of the time, while Black people are prosecuted 50% of the time. They use this to argue that there is a limited effect of race o
... keep reading on reddit β‘Theoretically speaking, what if Majorana Fermions interacted with magnetic fields? How could this be theoretically possible? How would this impact the dark fluid theory? More Information on Majorana Fermions
Can I get some help figuring out how to write this partition function? I have a system with 3 energy states, E*0=0, E1=ε, E2*=ε, and have to write the partition function for bosons and fermions in this system. I figured that bosons have degeneracies 1,4,4, and fermions have 0,4,2. My problem is figuring out how to write the degeneracies as a function of n so that I can write the partition function as 0Σ^(2) d(n)*e^(-βnε). Am I approaching this problem from the right angle?
Oggi, secondo Wikipedia, si festeggia il capodanno in questi due calendari.
Il Capodanno ortodosso, in inglese Old New Year, segue semplice il vecchio calendario Giuliano, cioè istituito da Giulio Cesare, invece di quello gregoriano, promulgato da Papa Gregorio XIII, che non rende bisestili gli anni i multipli di 100.
Il Capodanno berbero Γ¨ un agrario tradizionalmente in uso Maghreb, Γ¨ noto anche come calendario contadino, viene impiegato per regolare i lavori agricoli, perchΓ© quello islamico Γ¨ lunare e poco si presta a seguire i ritmi stagionali.
August 14 - 18, 2021 (final workshop date TBD)
---
Submission deadline (extended): May 20, 2021, anywhere on Earth
Format: 3 page extended abstract + references + appendices, ACM Proceeding Template
Submission website: https://cmt3.research.microsoft.com/BCIRWIS2021
---
Increasingly we use machine learning to build interactive systems that learn from past actions and the reward obtained. Theory suggests several possible approaches, such as contextual bandits, reinforcement learning, the do-calculus, or plain old Bayesian decision theory. What are the most theoretically appropriate and practical approaches to doing causal inference for interactive systems?
We are particularly interested in case studies of applying machine learning methods to interactive systems that did or did not use Bayesian or likelihood based methods, with a discussion about why this choice was made in terms of practical or theoretical arguments. We also welcome submissions in the following areas:
- Offline evaluation of recommender and interactive systems.
- Comparison of Bayesian, off-policy and other heuristic approaches for offline metrics.
- Probabilistic approaches applied to contextual bandits and reinforcement learning approaches.
- Probabilistic approaches to incrementality and attribution.
- Non-Bayesian approaches and trade-offs with Bayesian/Likelihood approaches.
- Bayesian methods in a production environment.
Organizers
- Nicholas Chopin (ENSAE)
- Mike Gartrell (Criteo AI Lab)
- Dawen Liang (Netflix)
- Alberto Lumbreras (Criteo AI Lab)
- David Rohde (Criteo AI Lab)
- Yixin Wang (UC Berkeley)
August 14 - 18, 2021 (final workshop date TBD)
---
Submission deadline: May 10, 2021, anywhere on Earth
Format: 3 page extended abstract + references + appendices, ACM Proceeding Template
Submission website: https://cmt3.research.microsoft.com/BCIRWIS2021
---
Increasingly we use machine learning to build interactive systems that learn from past actions and the reward obtained. Theory suggests several possible approaches, such as contextual bandits, reinforcement learning, the do-calculus, or plain old Bayesian decision theory. What are the most theoretically appropriate and practical approaches to doing causal inference for interactive systems?
We are particularly interested in case studies of applying machine learning methods to interactive systems that did or did not use Bayesian or likelihood based methods, with a discussion about why this choice was made in terms of practical or theoretical arguments. We also welcome submissions in the following areas:
- Offline evaluation of recommender and interactive systems.
- Comparison of Bayesian, off-policy and other heuristic approaches for offline metrics.
- Probabilistic approaches applied to contextual bandits and reinforcement learning approaches.
- Probabilistic approaches to incrementality and attribution.
- Non-Bayesian approaches and trade-offs with Bayesian/Likelihood approaches.
- Bayesian methods in a production environment.
Organizers
- Nicholas Chopin (ENSAE)
- Mike Gartrell (Criteo AI Lab)
- Dawen Liang (Netflix)
- Alberto Lumbreras (Criteo AI Lab)
- David Rohde (Criteo AI Lab)
- Yixin Wang (UC Berkeley)
