This talk explores possibilities for exploiting structure in voting profiles to obtain efficient algorithms for problems that are computationally intractable in general.
Elicitation for Preferences Single Peaked on Trees Neeldhara Misra
This talk will focus on the problem of preference elicitation, where the goal is to understand the preferences of agents (which we model by total orders) by querying them about their pairwise preferences. We will survey known results, which have studied the problem both on general domains and structured ones, such as the domain of single-peaked preferences. As one might expect, structured domains admit a lower query complexity. We will consider domains that are single peaked over trees, which generalize the notion of single-peakedness.
Digital Transformation agency MullenLowe Profero and their UK team take a look through the trends that will challenge brands and shape digital experiences in 2020.
Agree to Disagree: Improving Disagreement Detection with Dual GRUs. Presentation of our work on disagreement detection at ESSEM 2017. In this work, we show that by using a Siamese inspired architecture to encode the discussions, we no longer need to rely on hand-crafted features to exploit the meta thread structure. The research paper can be found at https://arxiv.org/abs/1708.05582
2 4. A researcher is interested in the level of ideolog.docxfelicidaddinwoodie
2
4. A researcher is interested in the level of ideological consistency among Democrats and Republicans.
She creates a measure of ideological consistency that ranges from 0 (total lack of consistency) to 10
(absolute consistency). What kind of statistical test should the researcher employ?
A. Chi-Square
B. Guessing
C. Differences of Means Test
D. Correlation
5. Regression in appropriate when our dependent variable is measured at what level of measurement?
A. Interval
B. Ordinal
C. Nominal
D. Dummy
6. A type one error occurs…
A. When we incorrectly fail to reject the null hypothesis even though it is false
B. When we have measurement error in one of our variables
C. When we incorrectly reject the null hypothesis even though it is true
D. When the results of our analysis do not support our alternative hypothesis
7. Below are four different hypotheses, which of the four should be tested using a one tailed test?
A. Democrats and Republicans will differ in their support for tax cuts
B. Republicans will be more supportive of tax cuts than Democrats
C. Republicans and Democrats will not differ in their support for tax cuts
D. Support for tax cuts will differ by party
8. In Chi Square testing our expected frequencies are…
A. The frequencies we would expect to observe if the null hypothesis was true
B. The frequencies we actually observe
3
C. The frequencies we would expect to observe if the alternative hypothesis was true
D. The frequencies we would expect to observe if the null hypothesis was false
9. A researcher is interested in testing whether males and females differ in their level of political
knowledge. To test this the researcher administers a political knowledge test to a sample of 10 males
and 10 females. Tests are scored out of 100 points. What statistical test should the researcher use to
test her hypothesis that males and females will differ in their level of political knowledge. (Hint think
about what test is appropriate for the level of measurement of these variables)
A. Correlation
B. Chi Square
C. Difference of Means Test
D. Standard Deviation
10. Outliers are a particular problem for which statistical test?
A. Correlation
B. Regression
C. Difference of Means
D. Chi Square
11. In regression our constant (Y intercept) is equal to:
A. The predicted value of Y when all of the X’s in our model = 0
B. The expected change in Y associated with a one unit change in X
C. The predicted value of X when all the Y’s in our model = 0
D. The expected change in X associated with a one unit change in Y
12. If we decrease our probability of making a Type 1 error we…
A. Decease our probability of making a Type 2 error
B. Increase our probability of making a Type 2 error
C. Have the same probability of making a Type 2 error
D. Have 0 probability of making a Type 2 error
4
13. Correlation and regression ana ...
On the Parameterized Complexity of Party NominationsNeeldhara Misra
Consider a fixed voting rule R. In the Possible President problem, we are given an election where the candidates are partitioned into parties, and the problem is to determine if, given a party P, it is possible for every party to nominate a candidate such that the nominee from P is a winner of the election that is obtained by restricting the votes to the nominated candidates. In the Necessary President problem, we would like to find a nominee who wins no matter who else is nominated. In this talk, we explore the complexity of these problems, which can be thought of as the two natural extremes of the party nomination problem, with an emphasis on a parameterized perspective and algorithms on structured profiles.
We consider a natural variant of the well-known Feedback Vertex Set problem, namely the problem of deleting a small subset of vertices or edges to a full binary tree. This version of the problem is motivated by real-world scenarios that are best modeled by full binary trees. We establish that both versions of the problem are NP-hard, which stands in contrast to the fact that deleting edges to obtain a forest or a tree is equivalent to the problem of finding a minimum cost spanning tree, which can be solved in polynomial time. We also establish that both problems are FPT by the standard parameter.
Elicitation for Preferences Single Peaked on Trees Neeldhara Misra
This talk will focus on the problem of preference elicitation, where the goal is to understand the preferences of agents (which we model by total orders) by querying them about their pairwise preferences. We will survey known results, which have studied the problem both on general domains and structured ones, such as the domain of single-peaked preferences. As one might expect, structured domains admit a lower query complexity. We will consider domains that are single peaked over trees, which generalize the notion of single-peakedness.
Digital Transformation agency MullenLowe Profero and their UK team take a look through the trends that will challenge brands and shape digital experiences in 2020.
Agree to Disagree: Improving Disagreement Detection with Dual GRUs. Presentation of our work on disagreement detection at ESSEM 2017. In this work, we show that by using a Siamese inspired architecture to encode the discussions, we no longer need to rely on hand-crafted features to exploit the meta thread structure. The research paper can be found at https://arxiv.org/abs/1708.05582
2 4. A researcher is interested in the level of ideolog.docxfelicidaddinwoodie
2
4. A researcher is interested in the level of ideological consistency among Democrats and Republicans.
She creates a measure of ideological consistency that ranges from 0 (total lack of consistency) to 10
(absolute consistency). What kind of statistical test should the researcher employ?
A. Chi-Square
B. Guessing
C. Differences of Means Test
D. Correlation
5. Regression in appropriate when our dependent variable is measured at what level of measurement?
A. Interval
B. Ordinal
C. Nominal
D. Dummy
6. A type one error occurs…
A. When we incorrectly fail to reject the null hypothesis even though it is false
B. When we have measurement error in one of our variables
C. When we incorrectly reject the null hypothesis even though it is true
D. When the results of our analysis do not support our alternative hypothesis
7. Below are four different hypotheses, which of the four should be tested using a one tailed test?
A. Democrats and Republicans will differ in their support for tax cuts
B. Republicans will be more supportive of tax cuts than Democrats
C. Republicans and Democrats will not differ in their support for tax cuts
D. Support for tax cuts will differ by party
8. In Chi Square testing our expected frequencies are…
A. The frequencies we would expect to observe if the null hypothesis was true
B. The frequencies we actually observe
3
C. The frequencies we would expect to observe if the alternative hypothesis was true
D. The frequencies we would expect to observe if the null hypothesis was false
9. A researcher is interested in testing whether males and females differ in their level of political
knowledge. To test this the researcher administers a political knowledge test to a sample of 10 males
and 10 females. Tests are scored out of 100 points. What statistical test should the researcher use to
test her hypothesis that males and females will differ in their level of political knowledge. (Hint think
about what test is appropriate for the level of measurement of these variables)
A. Correlation
B. Chi Square
C. Difference of Means Test
D. Standard Deviation
10. Outliers are a particular problem for which statistical test?
A. Correlation
B. Regression
C. Difference of Means
D. Chi Square
11. In regression our constant (Y intercept) is equal to:
A. The predicted value of Y when all of the X’s in our model = 0
B. The expected change in Y associated with a one unit change in X
C. The predicted value of X when all the Y’s in our model = 0
D. The expected change in X associated with a one unit change in Y
12. If we decrease our probability of making a Type 1 error we…
A. Decease our probability of making a Type 2 error
B. Increase our probability of making a Type 2 error
C. Have the same probability of making a Type 2 error
D. Have 0 probability of making a Type 2 error
4
13. Correlation and regression ana ...
On the Parameterized Complexity of Party NominationsNeeldhara Misra
Consider a fixed voting rule R. In the Possible President problem, we are given an election where the candidates are partitioned into parties, and the problem is to determine if, given a party P, it is possible for every party to nominate a candidate such that the nominee from P is a winner of the election that is obtained by restricting the votes to the nominated candidates. In the Necessary President problem, we would like to find a nominee who wins no matter who else is nominated. In this talk, we explore the complexity of these problems, which can be thought of as the two natural extremes of the party nomination problem, with an emphasis on a parameterized perspective and algorithms on structured profiles.
We consider a natural variant of the well-known Feedback Vertex Set problem, namely the problem of deleting a small subset of vertices or edges to a full binary tree. This version of the problem is motivated by real-world scenarios that are best modeled by full binary trees. We establish that both versions of the problem are NP-hard, which stands in contrast to the fact that deleting edges to obtain a forest or a tree is equivalent to the problem of finding a minimum cost spanning tree, which can be solved in polynomial time. We also establish that both problems are FPT by the standard parameter.
The document presents algorithms for finding the largest induced q-colorable subgraph of a given graph G. It first describes a randomized algorithm that runs in time proportional to enumerating maximal independent sets and a polynomial in n and q. For perfect graphs, where maximum independent sets can be found efficiently, it gives a deterministic algorithm running in similar time. It also shows that the problem does not admit a polynomial kernel when parameterized by the solution size for split and perfect graphs under standard assumptions.
The document describes research into the maximum edge coloring problem, which involves coloring the edges of a graph such that each vertex sees at most two colors. The goal is to maximize the number of colors used. The problem is known to be NP-complete. The authors present a fixed-parameter tractable algorithm that runs in time O*(20k) by reducing the problem into smaller subproblems involving color palettes, vertex covers, and independent sets. They also discuss some open problems regarding improving the running time and determining whether the problem admits a polynomial kernel.
This document discusses reasons for pursuing research in computer science, including the challenges, joys, and mindset required. It addresses circumstances around research, fascination with problems, eureka moments versus dull periods, the importance of persistence and breadth versus depth. It provides advice on coming to terms with limitations, the social aspects of research, balancing theory and practice, and having other interests besides work. Overall, the document presents research as rewarding but requiring hard work.
This document provides an overview of techniques for solving hard computational problems. It discusses the complexity classes P, NP, and NP-complete, and provides examples of NP-complete problems like the travelling salesman problem. The document then discusses heuristic approaches like approximation and randomized algorithms. It also discusses exploiting additional structure in problem inputs and parameterized/exact analysis. Finally, it provides an example of using vertex cover techniques like degree bounds to solve the vertex cover problem in polynomial time for certain cases.
The document discusses matchings in graphs and the Erdos-Ko-Rado (EKR) theorem. It introduces Baranyai partitions, which is a decomposition of the edges of a complete bipartite graph K2n into (2n-1) perfect matchings. Considering cyclic permutations of the edges within each perfect matching partition provides a way to set up "Katona-like local environments" to prove bounds on intersecting families of matchings, in analogy to Katona's proof technique for intersecting families of sets.
The document discusses connected separators and 2-connected separators in graphs. It presents the treewidth reduction theorem, which shows that the 2-connected separator problem can be solved by finding an equivalent instance on a graph of small treewidth. It also discusses properties of 2-connected Steiner trees, including that the non-terminal vertices induce a forest, and presents an algorithm that guesses and maps the structure of the 2-connected Steiner tree.
The document describes a generic algorithm for the F-deletion problem, where the goal is to remove at most k vertices from a graph such that the remaining graph does not contain graphs from F as minors. It shows that when F contains only planar graphs, the algorithm provides a constant-factor approximation. It analyzes special cases where the algorithm works with different constants, such as when the graph minus the solution is independent, a matching, or acyclic. It then discusses how the algorithm extends to more general graphs by exploiting that the graph minus solution must have bounded treewidth when F contains planar graphs.
A Kernel for Planar F-deletion: The Connected CaseNeeldhara Misra
The document discusses polynomial kernels for planar F-deletion problems. It presents an algorithm that works by guessing the existence of protrusions and either reducing them or inferring irrelevant edges. If every guess finds an irrelevant edge, those edges can safely be removed from the graph. The algorithm aims to obtain a graph with constant treewidth by repeatedly guessing and reducing protrusions or deleting irrelevant edges. This leads to a polynomial kernel for certain cases of planar F-deletion.
Kernels for Planar F-Deletion (Restricted Variants)Neeldhara Misra
The document discusses kernelization for the F-deletion problem, where graphs in F are connected and at least one is planar. It is shown that the planar F-deletion problem admits a polynomial kernel whenever F contains a planar graph called the "onion" graph. Several other positive and negative results are also presented, including that planar F-deletion admits an approximation algorithm and a polynomial kernel on claw-free graphs. The document concludes by outlining the ingredients for showing that planar F-deletion admits a polynomial kernel.
Efficient Simplification: The (im)possibilitiesNeeldhara Misra
The document discusses techniques for simplifying problems, including kernelization procedures. It proposes definitions for what constitutes a good simplification procedure and kernelization procedure. A kernelization procedure takes an input of size n and parameter k and maps it to an equivalent instance of size only g(k) in polynomial time, where g is some computable function. This implies the problem is fixed-parameter tractable. The document also discusses how some NP-complete problems may still admit efficient simplification procedures when restricted to instances with certain properties, like bounded degree graphs.
This document discusses the kernelization complexity of finding colorful motifs in graphs. It introduces colorful motifs as a problem with applications in bioinformatics. It shows the problem is NP-complete even on very simple graph classes from the kernelization perspective. It presents an observation that leads to many polynomial kernels in a special case of comb graphs, but notes this does not generalize. It also provides NP-hardness results and observations ruling out polynomial kernels for more general graph classes and problems.
The document discusses q-expansions and their applications to problems like vertex cover and feedback vertex set. It introduces the q-expansion lemma, which states that if the neighborhood of a set S is at least q times the size of S, then there exist q matchings saturating S that are disjoint in the neighborhood. This lemma is then used to obtain polynomial kernelizations for problems like vertex cover and feedback vertex set by finding a high-degree vertex v and using q-expansions to find a small hitting set that does not contain v. The technique can be generalized to finding solutions for graphs excluding a fixed minor H by using q-expansions to find a small set avoiding a high degree vertex v.
The document discusses kernelization procedures for parameterized problems. It begins by defining kernelization as a polynomial-time preprocessing function that maps an input instance to an equivalent, compressed instance whose size depends only on the parameter. It then proves that a problem admits a kernel (can be kernelized) if and only if it is fixed-parameter tractable. Specifically, a kernel implies an FPT algorithm, and an FPT runtime implies the existence of a kernel. The document advocates for polynomial-sized kernels as the most efficient type of kernelization.
The document discusses connected dominating sets and short cycles. It begins by explaining that excluding longer cycles makes related problems easier to solve. Specifically, it shows that on graphs with girth at least five, high degree vertices must be in any minimum dominating set. However, this does not hold for connected dominating sets, since connectivity must also be maintained. It then describes how to obtain fixed-parameter tractable algorithms for connected dominating set problems by guessing the minimum dominating set and extending it. It also shows that these problems do not admit polynomial kernels by providing a reduction from Fair Connected Colors, which is W-hard.
The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
Compositions of iron-meteorite parent bodies constrainthe structure of the pr...Sérgio Sacani
Magmatic iron-meteorite parent bodies are the earliest planetesimals in the Solar System,and they preserve information about conditions and planet-forming processes in thesolar nebula. In this study, we include comprehensive elemental compositions andfractional-crystallization modeling for iron meteorites from the cores of five differenti-ated asteroids from the inner Solar System. Together with previous results of metalliccores from the outer Solar System, we conclude that asteroidal cores from the outerSolar System have smaller sizes, elevated siderophile-element abundances, and simplercrystallization processes than those from the inner Solar System. These differences arerelated to the formation locations of the parent asteroids because the solar protoplane-tary disk varied in redox conditions, elemental distributions, and dynamics at differentheliocentric distances. Using highly siderophile-element data from iron meteorites, wereconstruct the distribution of calcium-aluminum-rich inclusions (CAIs) across theprotoplanetary disk within the first million years of Solar-System history. CAIs, the firstsolids to condense in the Solar System, formed close to the Sun. They were, however,concentrated within the outer disk and depleted within the inner disk. Future modelsof the structure and evolution of the protoplanetary disk should account for this dis-tribution pattern of CAIs.
BIRDS DIVERSITY OF SOOTEA BISWANATH ASSAM.ppt.pptxgoluk9330
Ahota Beel, nestled in Sootea Biswanath Assam , is celebrated for its extraordinary diversity of bird species. This wetland sanctuary supports a myriad of avian residents and migrants alike. Visitors can admire the elegant flights of migratory species such as the Northern Pintail and Eurasian Wigeon, alongside resident birds including the Asian Openbill and Pheasant-tailed Jacana. With its tranquil scenery and varied habitats, Ahota Beel offers a perfect haven for birdwatchers to appreciate and study the vibrant birdlife that thrives in this natural refuge.
The document presents algorithms for finding the largest induced q-colorable subgraph of a given graph G. It first describes a randomized algorithm that runs in time proportional to enumerating maximal independent sets and a polynomial in n and q. For perfect graphs, where maximum independent sets can be found efficiently, it gives a deterministic algorithm running in similar time. It also shows that the problem does not admit a polynomial kernel when parameterized by the solution size for split and perfect graphs under standard assumptions.
The document describes research into the maximum edge coloring problem, which involves coloring the edges of a graph such that each vertex sees at most two colors. The goal is to maximize the number of colors used. The problem is known to be NP-complete. The authors present a fixed-parameter tractable algorithm that runs in time O*(20k) by reducing the problem into smaller subproblems involving color palettes, vertex covers, and independent sets. They also discuss some open problems regarding improving the running time and determining whether the problem admits a polynomial kernel.
This document discusses reasons for pursuing research in computer science, including the challenges, joys, and mindset required. It addresses circumstances around research, fascination with problems, eureka moments versus dull periods, the importance of persistence and breadth versus depth. It provides advice on coming to terms with limitations, the social aspects of research, balancing theory and practice, and having other interests besides work. Overall, the document presents research as rewarding but requiring hard work.
This document provides an overview of techniques for solving hard computational problems. It discusses the complexity classes P, NP, and NP-complete, and provides examples of NP-complete problems like the travelling salesman problem. The document then discusses heuristic approaches like approximation and randomized algorithms. It also discusses exploiting additional structure in problem inputs and parameterized/exact analysis. Finally, it provides an example of using vertex cover techniques like degree bounds to solve the vertex cover problem in polynomial time for certain cases.
The document discusses matchings in graphs and the Erdos-Ko-Rado (EKR) theorem. It introduces Baranyai partitions, which is a decomposition of the edges of a complete bipartite graph K2n into (2n-1) perfect matchings. Considering cyclic permutations of the edges within each perfect matching partition provides a way to set up "Katona-like local environments" to prove bounds on intersecting families of matchings, in analogy to Katona's proof technique for intersecting families of sets.
The document discusses connected separators and 2-connected separators in graphs. It presents the treewidth reduction theorem, which shows that the 2-connected separator problem can be solved by finding an equivalent instance on a graph of small treewidth. It also discusses properties of 2-connected Steiner trees, including that the non-terminal vertices induce a forest, and presents an algorithm that guesses and maps the structure of the 2-connected Steiner tree.
The document describes a generic algorithm for the F-deletion problem, where the goal is to remove at most k vertices from a graph such that the remaining graph does not contain graphs from F as minors. It shows that when F contains only planar graphs, the algorithm provides a constant-factor approximation. It analyzes special cases where the algorithm works with different constants, such as when the graph minus the solution is independent, a matching, or acyclic. It then discusses how the algorithm extends to more general graphs by exploiting that the graph minus solution must have bounded treewidth when F contains planar graphs.
A Kernel for Planar F-deletion: The Connected CaseNeeldhara Misra
The document discusses polynomial kernels for planar F-deletion problems. It presents an algorithm that works by guessing the existence of protrusions and either reducing them or inferring irrelevant edges. If every guess finds an irrelevant edge, those edges can safely be removed from the graph. The algorithm aims to obtain a graph with constant treewidth by repeatedly guessing and reducing protrusions or deleting irrelevant edges. This leads to a polynomial kernel for certain cases of planar F-deletion.
Kernels for Planar F-Deletion (Restricted Variants)Neeldhara Misra
The document discusses kernelization for the F-deletion problem, where graphs in F are connected and at least one is planar. It is shown that the planar F-deletion problem admits a polynomial kernel whenever F contains a planar graph called the "onion" graph. Several other positive and negative results are also presented, including that planar F-deletion admits an approximation algorithm and a polynomial kernel on claw-free graphs. The document concludes by outlining the ingredients for showing that planar F-deletion admits a polynomial kernel.
Efficient Simplification: The (im)possibilitiesNeeldhara Misra
The document discusses techniques for simplifying problems, including kernelization procedures. It proposes definitions for what constitutes a good simplification procedure and kernelization procedure. A kernelization procedure takes an input of size n and parameter k and maps it to an equivalent instance of size only g(k) in polynomial time, where g is some computable function. This implies the problem is fixed-parameter tractable. The document also discusses how some NP-complete problems may still admit efficient simplification procedures when restricted to instances with certain properties, like bounded degree graphs.
This document discusses the kernelization complexity of finding colorful motifs in graphs. It introduces colorful motifs as a problem with applications in bioinformatics. It shows the problem is NP-complete even on very simple graph classes from the kernelization perspective. It presents an observation that leads to many polynomial kernels in a special case of comb graphs, but notes this does not generalize. It also provides NP-hardness results and observations ruling out polynomial kernels for more general graph classes and problems.
The document discusses q-expansions and their applications to problems like vertex cover and feedback vertex set. It introduces the q-expansion lemma, which states that if the neighborhood of a set S is at least q times the size of S, then there exist q matchings saturating S that are disjoint in the neighborhood. This lemma is then used to obtain polynomial kernelizations for problems like vertex cover and feedback vertex set by finding a high-degree vertex v and using q-expansions to find a small hitting set that does not contain v. The technique can be generalized to finding solutions for graphs excluding a fixed minor H by using q-expansions to find a small set avoiding a high degree vertex v.
The document discusses kernelization procedures for parameterized problems. It begins by defining kernelization as a polynomial-time preprocessing function that maps an input instance to an equivalent, compressed instance whose size depends only on the parameter. It then proves that a problem admits a kernel (can be kernelized) if and only if it is fixed-parameter tractable. Specifically, a kernel implies an FPT algorithm, and an FPT runtime implies the existence of a kernel. The document advocates for polynomial-sized kernels as the most efficient type of kernelization.
The document discusses connected dominating sets and short cycles. It begins by explaining that excluding longer cycles makes related problems easier to solve. Specifically, it shows that on graphs with girth at least five, high degree vertices must be in any minimum dominating set. However, this does not hold for connected dominating sets, since connectivity must also be maintained. It then describes how to obtain fixed-parameter tractable algorithms for connected dominating set problems by guessing the minimum dominating set and extending it. It also shows that these problems do not admit polynomial kernels by providing a reduction from Fair Connected Colors, which is W-hard.
The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
Compositions of iron-meteorite parent bodies constrainthe structure of the pr...Sérgio Sacani
Magmatic iron-meteorite parent bodies are the earliest planetesimals in the Solar System,and they preserve information about conditions and planet-forming processes in thesolar nebula. In this study, we include comprehensive elemental compositions andfractional-crystallization modeling for iron meteorites from the cores of five differenti-ated asteroids from the inner Solar System. Together with previous results of metalliccores from the outer Solar System, we conclude that asteroidal cores from the outerSolar System have smaller sizes, elevated siderophile-element abundances, and simplercrystallization processes than those from the inner Solar System. These differences arerelated to the formation locations of the parent asteroids because the solar protoplane-tary disk varied in redox conditions, elemental distributions, and dynamics at differentheliocentric distances. Using highly siderophile-element data from iron meteorites, wereconstruct the distribution of calcium-aluminum-rich inclusions (CAIs) across theprotoplanetary disk within the first million years of Solar-System history. CAIs, the firstsolids to condense in the Solar System, formed close to the Sun. They were, however,concentrated within the outer disk and depleted within the inner disk. Future modelsof the structure and evolution of the protoplanetary disk should account for this dis-tribution pattern of CAIs.
BIRDS DIVERSITY OF SOOTEA BISWANATH ASSAM.ppt.pptxgoluk9330
Ahota Beel, nestled in Sootea Biswanath Assam , is celebrated for its extraordinary diversity of bird species. This wetland sanctuary supports a myriad of avian residents and migrants alike. Visitors can admire the elegant flights of migratory species such as the Northern Pintail and Eurasian Wigeon, alongside resident birds including the Asian Openbill and Pheasant-tailed Jacana. With its tranquil scenery and varied habitats, Ahota Beel offers a perfect haven for birdwatchers to appreciate and study the vibrant birdlife that thrives in this natural refuge.
Signatures of wave erosion in Titan’s coastsSérgio Sacani
The shorelines of Titan’s hydrocarbon seas trace flooded erosional landforms such as river valleys; however, it isunclear whether coastal erosion has subsequently altered these shorelines. Spacecraft observations and theo-retical models suggest that wind may cause waves to form on Titan’s seas, potentially driving coastal erosion,but the observational evidence of waves is indirect, and the processes affecting shoreline evolution on Titanremain unknown. No widely accepted framework exists for using shoreline morphology to quantitatively dis-cern coastal erosion mechanisms, even on Earth, where the dominant mechanisms are known. We combinelandscape evolution models with measurements of shoreline shape on Earth to characterize how differentcoastal erosion mechanisms affect shoreline morphology. Applying this framework to Titan, we find that theshorelines of Titan’s seas are most consistent with flooded landscapes that subsequently have been eroded bywaves, rather than a uniform erosional process or no coastal erosion, particularly if wave growth saturates atfetch lengths of tens of kilometers.
Evidence of Jet Activity from the Secondary Black Hole in the OJ 287 Binary S...Sérgio Sacani
Wereport the study of a huge optical intraday flare on 2021 November 12 at 2 a.m. UT in the blazar OJ287. In the binary black hole model, it is associated with an impact of the secondary black hole on the accretion disk of the primary. Our multifrequency observing campaign was set up to search for such a signature of the impact based on a prediction made 8 yr earlier. The first I-band results of the flare have already been reported by Kishore et al. (2024). Here we combine these data with our monitoring in the R-band. There is a big change in the R–I spectral index by 1.0 ±0.1 between the normal background and the flare, suggesting a new component of radiation. The polarization variation during the rise of the flare suggests the same. The limits on the source size place it most reasonably in the jet of the secondary BH. We then ask why we have not seen this phenomenon before. We show that OJ287 was never before observed with sufficient sensitivity on the night when the flare should have happened according to the binary model. We also study the probability that this flare is just an oversized example of intraday variability using the Krakow data set of intense monitoring between 2015 and 2023. We find that the occurrence of a flare of this size and rapidity is unlikely. In machine-readable Tables 1 and 2, we give the full orbit-linked historical light curve of OJ287 as well as the dense monitoring sample of Krakow.
Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...Sérgio Sacani
Context. The observation of several L-band emission sources in the S cluster has led to a rich discussion of their nature. However, a definitive answer to the classification of the dusty objects requires an explanation for the detection of compact Doppler-shifted Brγ emission. The ionized hydrogen in combination with the observation of mid-infrared L-band continuum emission suggests that most of these sources are embedded in a dusty envelope. These embedded sources are part of the S-cluster, and their relationship to the S-stars is still under debate. To date, the question of the origin of these two populations has been vague, although all explanations favor migration processes for the individual cluster members. Aims. This work revisits the S-cluster and its dusty members orbiting the supermassive black hole SgrA* on bound Keplerian orbits from a kinematic perspective. The aim is to explore the Keplerian parameters for patterns that might imply a nonrandom distribution of the sample. Additionally, various analytical aspects are considered to address the nature of the dusty sources. Methods. Based on the photometric analysis, we estimated the individual H−K and K−L colors for the source sample and compared the results to known cluster members. The classification revealed a noticeable contrast between the S-stars and the dusty sources. To fit the flux-density distribution, we utilized the radiative transfer code HYPERION and implemented a young stellar object Class I model. We obtained the position angle from the Keplerian fit results; additionally, we analyzed the distribution of the inclinations and the longitudes of the ascending node. Results. The colors of the dusty sources suggest a stellar nature consistent with the spectral energy distribution in the near and midinfrared domains. Furthermore, the evaporation timescales of dusty and gaseous clumps in the vicinity of SgrA* are much shorter ( 2yr) than the epochs covered by the observations (≈15yr). In addition to the strong evidence for the stellar classification of the D-sources, we also find a clear disk-like pattern following the arrangements of S-stars proposed in the literature. Furthermore, we find a global intrinsic inclination for all dusty sources of 60 ± 20◦, implying a common formation process. Conclusions. The pattern of the dusty sources manifested in the distribution of the position angles, inclinations, and longitudes of the ascending node strongly suggests two different scenarios: the main-sequence stars and the dusty stellar S-cluster sources share a common formation history or migrated with a similar formation channel in the vicinity of SgrA*. Alternatively, the gravitational influence of SgrA* in combination with a massive perturber, such as a putative intermediate mass black hole in the IRS 13 cluster, forces the dusty objects and S-stars to follow a particular orbital arrangement. Key words. stars: black holes– stars: formation– Galaxy: center– galaxies: star formation
This presentation offers a general idea of the structure of seed, seed production, management of seeds and its allied technologies. It also offers the concept of gene erosion and the practices used to control it. Nursery and gardening have been widely explored along with their importance in the related domain.
Sexuality - Issues, Attitude and Behaviour - Applied Social Psychology - Psyc...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
4. definition, recognition, strategy-proofness, elicitation
definition, recognition and Condorcet winners
Single Crossing preferences
Single Peaked Preferences
and some problems that we will encounter.
The standard Voting Setup
5. definition, recognition, strategy-proofness, elicitation
definition, recognition and Condorcet winners
nearly structured preferences, dichotomous preferences
Single Crossing preferences
Concluding remarks
Single Peaked Preferences
and some problems that we will encounter.
The standard Voting Setup
6. The standard Voting Setup
and some problems that we will encounter.
The Handbook of Computational Social Choice,
Brandt, Conitzer, Endriss, Lang and Procaccia; 2016
7.
8. A typical voting scenario involves
a set of alternatives, and
a set of voters.
Voters have preferences over the alternatives,
which can be modelled in various ways.
In this talk, we’ll think of preferences as
linear orders over the rankings.
9. A typical voting scenario involves
a set of alternatives, and
a set of voters.
Voters have preferences over the alternatives,
which can be modelled in various ways.
In this talk, we’ll think of preferences as
linear orders over the rankings.
10. A typical voting scenario involves
a set of alternatives, and
a set of voters.
Voters have preferences over the alternatives,
which can be modelled in various ways.
In this talk, we’ll think of preferences as
linear orders over the rankings.
17. We say that a voter (or a group of voters)
can manipulate if they can obtain a more desirable
outcome by misreporting their preferences.
We’ll see that plurality is vulnerable to this behaviour.
18. We say that a voter (or a group of voters)
can manipulate if they can obtain a more desirable
outcome by misreporting their preferences.
We’ll see that plurality is vulnerable to this behaviour.
57. Independence of Irrelevant Alternatives
Suppose a SWF prefers A over B in
the consensus ranking for a profile P.
Let Q be a profile that is the same as P when
projected on the candidates A and B.
Then the SWF must prefer A over B in the
consensus ranking that it determines for Q as well.
59. When voters have three or more alternatives,
any social welfare function which respects
unanimity and
independence of irrelevant alternatives
is a dictatorship.
Arrow (1949)
78. Left RightCenter
A B C D E F G
If an agent with single-peaked preferences prefers x to y,
one of the following must be true:
- x is the agent’s peak,
- x and y are on opposite sides of the agent’s peak, or
- x is closer to the peak than y.
79. Left RightCenter
A B C D E F G
The notion is popular for several reasons:
- No Condorcet Cycles.
- No incentive for an agent to misreport its preferences.
- Identifiable in polynomial time.
- Reasonable (?) model of actual elections.
82. Recognising if a given profile is single-peaked with
respect to some preference ordering.
Checking if a 0/1 matrix has the
consecutive-ones property.
103. A B C D E F G
Claim: D beats all other candidates in pairwise elections.
104. A B C D E F G
Claim: D beats all other candidates in pairwise elections.
105. A B C D E F G
Claim: D beats all other candidates in pairwise elections.
106. A B C D E F G
Claim: D beats all other candidates in pairwise elections.
(Peak to the left of D,
F further than D)
107. A B C D E F G
Claim: D beats all other candidates in pairwise elections.
108. A B C D E F G
Claim: D beats all other candidates in pairwise elections.
109. A B C D E F G
Claim: D beats all other candidates in pairwise elections.
110. A B C D E F G
Claim: D beats all other candidates in pairwise elections.
(Peak to the right of D,
B further than D)
111. A B C D E F G
Claim: Choosing D also leaves nobody
with any incentive to manipulate.
112. A B C D E F G
Claim: Choosing D also leaves nobody
with any incentive to manipulate.
113. A B C D E F G
Claim: Choosing D also leaves nobody
with any incentive to manipulate.
114. A B C D E F G
Claim: Choosing D also leaves nobody
with any incentive to manipulate.
115. A B C D E F G
Claim: Choosing D also leaves nobody
with any incentive to manipulate.
116. A B C D E F G
Claim: Choosing D also leaves nobody
with any incentive to manipulate.
117. Single Peaked Preferences
Preference Elicitation
Eliciting single-peaked preferences using comparison queries,
Conitzer, J.Artif. Intell. Res.; 2016
118. When the number of candidates is large,
soliciting a full ranking can be a little unmanageable.
119. When the number of candidates is large,
soliciting a full ranking can be a little unmanageable.
Voters typically find it easier to answer
“comparison queries”:
Would you rather hang out over coffee
or join me for a concert?
120. How many such queries to do we need to make
to be able to reconstruct the full preference?
121. How many such queries to do we need to make
to be able to reconstruct the full preference?
Just like the weighing scale puzzles, except you can
only compare two single options at a time.
122. Using a “merge sort” like idea,
O(m log m) comparisons are enough.
123. Using a “merge sort” like idea,
O(m log m) comparisons are enough.
Recursively order half the alternatives.
124. Using a “merge sort” like idea,
O(m log m) comparisons are enough.
Recursively order half the alternatives.
125. Using a “merge sort” like idea,
O(m log m) comparisons are enough.
Merge the two lists with a linear number of queries.
127. Left RightCenter
A B C D E F G
…and we can do better if the preferences are single-peaked!
128. Left RightCenter
A B C D E F G
…and we can do better if the preferences are single-peaked!
(i) Identify the peak: use binary search.
129. Left RightCenter
A B C D E F G
(i) Identify the peak: use binary search.
[Better query complexities for elicitation.]
130. Left RightCenter
A B C D
E
F G
(i) Identify the peak: use binary search.
[Better query complexities for elicitation.]
131. Left RightCenter
A B C D
E
F G
(i) Identify the peak: use binary search.
this signals that the
peak is to the right.
[Better query complexities for elicitation.]
132. Left RightCenter
A B C D E F G
(i) Identify the peak: use binary search.
[Better query complexities for elicitation.]
133. Left RightCenter
A B C
D
E F G
(i) Identify the peak: use binary search.
[Better query complexities for elicitation.]
134. Left RightCenter
A B C
D
E F G
(i) Identify the peak: use binary search.
this signals that the
peak is to the left.
[Better query complexities for elicitation.]
135. Left RightCenter
A B C D E F G
(ii) Once we know the peak, the rest is O(m) queries.
[Better query complexities for elicitation.]
136. Left Right
A B C D E F G
(ii) Once we know the peak, the rest is O(m) queries.
[Better query complexities for elicitation.]
137. Left Right
A B C D E F G
(ii) Once we know the peak, the rest is O(m) queries.
C
[Better query complexities for elicitation.]
138. Left Right
A B C D E F G
(ii) Once we know the peak, the rest is O(m) queries.
C
[Better query complexities for elicitation.]
139. Left Right
A B C D E F G
(ii) Once we know the peak, the rest is O(m) queries.
C
[Better query complexities for elicitation.]
140. Left Right
A
B
C D E F G
(ii) Once we know the peak, the rest is O(m) queries.
C
[Better query complexities for elicitation.]
141. Left Right
A
B
C D E F G
(ii) Once we know the peak, the rest is O(m) queries.
C
[Better query complexities for elicitation.]
142. Left Right
A B C D E F G
(ii) Once we know the peak, the rest is O(m) queries.
C
[Better query complexities for elicitation.]
143. Left Right
A B C
D
E F G
(ii) Once we know the peak, the rest is O(m) queries.
C
[Better query complexities for elicitation.]
144. Left Right
A B C
D
E F G
(ii) Once we know the peak, the rest is O(m) queries.
C
[Better query complexities for elicitation.]
145. Left Right
A B C D E F G
Center
[Better query complexities for elicitation.]
146. Left Right
A B C D E F G
Center
(ii) Once we know the peak, the rest is O(m) queries.
(i) Identify the peak: use binary search (O(log m) queries).
[Better query complexities for elicitation.]
150. A profile is single-crossing if it admits an ordering of the
voters such that for every pair of candidates (a,b), either:
a) all voters who prefer a over b appear before all voters
who prefer b over a, or,
b) all voters who prefer a over b appear after all voters
who prefer b over a, or,
151.
152. The notion is popular for several reasons:
- No Condorcet Cycles.
- Identifiable in polynomial time.
- Reasonable (?) model of actual elections.
155. Recognising if a given profile is single-crossing
with respect to some preference ordering.
Checking if a 0/1 matrix has the
consecutive-ones property.
166. dissatisfaction of voter v =
rank of best candidate in the committee in his vote
Voters
Candidates
167. dissatisfaction of voter v =
rank of best candidate in the committee in his vote
Voters
Candidates
168. dissatisfaction of voter v =
rank of best candidate in the committee in his vote
On single-crossing profiles, optimal CC solutions exhibit a
“contiguous blocks property”.
Voters
Candidates
169. dissatisfaction of voter v =
rank of best candidate in the committee in his vote
On single-crossing profiles, optimal CC solutions exhibit a
“contiguous blocks property”.
Voters
Candidates
170. dissatisfaction of voter v =
rank of best candidate in the committee in his vote
On single-crossing profiles, optimal CC solutions exhibit a
“contiguous blocks property”.
Voters
Candidates
172. The single-peaked and single-crossing domains have been
generalised to notions of single-peaked and single-
crossing on trees. The generalised domains continue to
exhibit many of the nice properties we saw today.
Generalizing the Single-Crossing Property on Lines and Trees to Intermediate
Preferences on Median Graphs, Clearwater, Puppe, and Slinko, IJCAI 2015
Single-peaked orders on a tree, Gabrielle Demange,
Math. Soc. Sci, 3(4), 1982.
173. The single-peaked and single-crossing domains have been
generalised to notions of single-peaked-width and single-
crossing-width.
Here, it is common that algorithms that work in the single-
peaked or single-crossing settings can be generalised to
profiles of width w at an expense that is exponential in w.
Kemeny Elections with Bounded Single-peaked or Single-crossing Width,
Cornaz, Galand, and Spanjaard, IJCAI 2013
174. Profiles that are “close” to being single-peaked or single-
crossing (closeness measured usually in terms of candidate
or voter deletion) have also been studied.
It’s typically NP-complete to determine the optimal
distance, but FPT and approximation algorithms are
known.
Are There Any Nicely Structured Preference Profiles Nearby?
Bredereck, Chen, andWoeginger, AAAI 2013
On Detecting Nearly Structured Preference Profiles
Elkind and Lackner, AAAI 2014
Computational aspects of nearly single-peaked electorates,
Erdélyi, Lackner, and Pfandler, AAAI 2013
176. While the domain restrictions we saw today apply to votes
given as linear orders, similar restrictions have also been
studied in the context of votes that are given by approval
ballots.
As was the case here, there are close connections with
COP and many hard problems become easy on these
structured (dichotomous) profiles.
Structure in Dichotomous Preferences,
Elikind and Lackner; IJCAI 2015.
177. Euclidean preferences capture settings where voters and
alternatives can be identified with points in the Euclidean
space, with voters’ preferences driven by distances to
alternatives.
Psychological scaling without a unit of measurement,
Coombs; Psychological review, 1950
178. The Dark Side: Domain restrictions also have some side-
effects: problems like manipulation, bribery, and so forth
also become easy!
Bypassing Combinatorial Protections: Polynomial-Time Algorithms for Single-Peaked Electorates,
Brandt et al; AAAI 2010
The Shield that Never Was: Societies with Single-Peaked Preferences are More Open to
Manipulation and Control, Faliszewski et al; TARK 2009
179. Thank you!
Preference Restrictions in Computational Social Choice: Recent Progress, Elikind,
Lackner, and Peters; IJCAI 2016.
The Handbook of Computational Social Choice,
Brandt, Conitzer, Endriss, Lang and Procaccia; 2016