The document discusses advanced analytics applications in public policy and bracketology. It summarizes Laura Albert's background and research interests in operations research and systems engineering. She studies how mathematical models and systems thinking can help analyze complex, interconnected systems and issues. Advanced analytics turn data into useful information to make better decisions. Examples discussed include risk-based screening models for aviation security and emergency response optimization to improve response times for medical emergencies.
A Summary of Computational Social Science - Lecture 8 in Introduction to Comp...Lauri Eloranta
Final lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
Social Network Analysis - Lecture 4 in Introduction to Computational Social S...Lauri Eloranta
Fourth lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
Introduction to Computational Social Science - Lecture 1Lauri Eloranta
First lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015. (http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
Everything is connected: people, information, events and places. A practical way of making sense of the tangle of connections is to analyze them as networks. The objective of this workshop is to introduce the essential concepts of Social Network Analysis (SNA). It also seeks to show how SNA may help organizations unlock and mobilize these informal networks in order to achieve sustainable strategic goals. After discussing the essential concepts in theory of SNA, the computational tools for modeling and analysis of social networks will also be introduced in this presentation.
The emerging field of computational social science (CSS) is devoted to the pursuit of interdisciplinary social science research from an information processing perspective, through the medium of advanced computing and information technologies.
A Summary of Computational Social Science - Lecture 8 in Introduction to Comp...Lauri Eloranta
Final lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
Social Network Analysis - Lecture 4 in Introduction to Computational Social S...Lauri Eloranta
Fourth lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
Introduction to Computational Social Science - Lecture 1Lauri Eloranta
First lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015. (http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
Everything is connected: people, information, events and places. A practical way of making sense of the tangle of connections is to analyze them as networks. The objective of this workshop is to introduce the essential concepts of Social Network Analysis (SNA). It also seeks to show how SNA may help organizations unlock and mobilize these informal networks in order to achieve sustainable strategic goals. After discussing the essential concepts in theory of SNA, the computational tools for modeling and analysis of social networks will also be introduced in this presentation.
The emerging field of computational social science (CSS) is devoted to the pursuit of interdisciplinary social science research from an information processing perspective, through the medium of advanced computing and information technologies.
From Search to Predictions in Tagged Information SpacesChristoph Trattner
Tagging gained tremendously in popularity over past few years. When looking into the literature of tagging we find a lot of work regarding people's tagging motivation, their behavior, models that describe the folksonomy generation process, emergent semantic structures, etc., but interestingly we find quite little research showing the value of tags for searching an overloaded information space. Furthermore, there is lot of literature on the tag or item prediction problem, but interestingly almost all of them lookat the issue from a data-driven perspective. To bridge this gap in the literature, we have conducted several in-depth studies in the past showing the value of tags for lookup and exploratory search. We looked at the problem from a network theoretic and interface perspective and we will show how useful tags are for searching. Furthermore, we reviewed literature on memory processes from cognitive science and have invented a number of novel recommender algorithms based on the ACT-R and MINERVA2 theory. We will show that these approaches can not only predict tags and items extremely well, but also reveal how these models can help in explaining the recommendation processes better than current approaches.
Social Computing in the area of Big Data at the Know-Center Austria's leading...Christoph Trattner
Nowadays, social networks and media, such as Facebook, Twitter & Co, affect our communication and our exchange of knowledge more than ever. But which additional benefits can offer social media apart from easy interaction with friends and how can they be used to create additional value for companies and institutions? These are the questions that the area Social Computing at Know-Center addresses in detail.
In this talk we will give a brief overview of industry and non-industry related research projects which we have been involved in recently with my group, Social Computing at the Know-Center, in the context of Big Data and social media. In particular, the talk will highlight specific research project outcomes and work-in-progress that make use of social media data to help people to explore the vastly growing overloaded information space more efficiently.
Lugović, S., Čolić, M., & Dunđer, I. (2014, January), Znanstveni pristup dizajnu informacijskih sustava, Design Science and Information Systems, Overview of Design Science models over the years presented @ International Scientific Conference On Printing & Design 2014
Practical applications for altmetrics in a changing metrics landscapeDigital Science
"Practical applications for altmetrics in a changing metrics landscape" - Sara Rouhi, Altmetric product specialist, and Anirvan Chatterjee, Director Data Strategy for CTSI at UCSF
UCL joint Institute of Education (London Knowledge Lab) & UCL Interaction Centre seminar, 20th April 2016. Replay: https://youtu.be/0t0IWvcO-Uo
Algorithmic Accountability & Learning Analytics
Simon Buckingham Shum
Connected Intelligence Centre, University of Technology Sydney
ABSTRACT. As algorithms pervade societal life, they are moving from the preserve of computer science to becoming the object of far wider academic and media attention. Many are now asking how the behaviour of algorithms can be made “accountable”. But why are they “opaque” and to whom? As this vital discussion unfolds in relation to Big Data in general, the Learning Analytics community must articulate what would count as meaningful questions and satisfactory answers in educational contexts. In this talk, I propose different lenses that we can bring to bear on a given learning analytics tool, to ask what it would mean for it to be accountable, and to whom. From a Human-Centred Informatics perspective, it turns out that algorithmic accountability may be the wrong focus.
BIO. Simon Buckingham Shum is Professor of Learning Informatics at the University of Technology Sydney, which he joined in August 2014 to direct the new Connected Intelligence Centre. Prior to that he was at The Open University’s Knowledge Media Institute 1995-2014. He brings a Human-Centred Informatics (HCI) approach to his work, with a background in Psychology (BSc, York), Ergonomics (MSc, London) and HCI (PhD, York) where he worked with Rank Xerox Cambridge EuroPARC on Design Rationale. He co-edited Visualizing Argumentation (2003) followed by Knowledge Cartography (2008, 2nd Edn. 2014), and with Al Selvin wrote Constructing Knowledge Art (2015). He is active in the emerging field of Learning Analytics and is a co-founder of the Society for Learning Analytics Research, Compendium Institute and Learning Emergence network.
The results of the RISCOSS evaluation have been presented in the PMI academic workshop 2016 at the United Nations Global Service Centre in Brindisi. Many thanks to PMI-NIC volunteers and to TAAS branch.
Optimization with impact: my journey in public sector operations research Laura Albert
eynote talk at the Advances in Data Science & Operations Research Virtual Conference, presented by Universidad Galileo in collaboration with INFORMSttt. It's the first INFORMS conference made for Latino America that brings together the scientific community from the areas of operations research, business intelligence, and data science.
From Search to Predictions in Tagged Information SpacesChristoph Trattner
Tagging gained tremendously in popularity over past few years. When looking into the literature of tagging we find a lot of work regarding people's tagging motivation, their behavior, models that describe the folksonomy generation process, emergent semantic structures, etc., but interestingly we find quite little research showing the value of tags for searching an overloaded information space. Furthermore, there is lot of literature on the tag or item prediction problem, but interestingly almost all of them lookat the issue from a data-driven perspective. To bridge this gap in the literature, we have conducted several in-depth studies in the past showing the value of tags for lookup and exploratory search. We looked at the problem from a network theoretic and interface perspective and we will show how useful tags are for searching. Furthermore, we reviewed literature on memory processes from cognitive science and have invented a number of novel recommender algorithms based on the ACT-R and MINERVA2 theory. We will show that these approaches can not only predict tags and items extremely well, but also reveal how these models can help in explaining the recommendation processes better than current approaches.
Social Computing in the area of Big Data at the Know-Center Austria's leading...Christoph Trattner
Nowadays, social networks and media, such as Facebook, Twitter & Co, affect our communication and our exchange of knowledge more than ever. But which additional benefits can offer social media apart from easy interaction with friends and how can they be used to create additional value for companies and institutions? These are the questions that the area Social Computing at Know-Center addresses in detail.
In this talk we will give a brief overview of industry and non-industry related research projects which we have been involved in recently with my group, Social Computing at the Know-Center, in the context of Big Data and social media. In particular, the talk will highlight specific research project outcomes and work-in-progress that make use of social media data to help people to explore the vastly growing overloaded information space more efficiently.
Lugović, S., Čolić, M., & Dunđer, I. (2014, January), Znanstveni pristup dizajnu informacijskih sustava, Design Science and Information Systems, Overview of Design Science models over the years presented @ International Scientific Conference On Printing & Design 2014
Practical applications for altmetrics in a changing metrics landscapeDigital Science
"Practical applications for altmetrics in a changing metrics landscape" - Sara Rouhi, Altmetric product specialist, and Anirvan Chatterjee, Director Data Strategy for CTSI at UCSF
UCL joint Institute of Education (London Knowledge Lab) & UCL Interaction Centre seminar, 20th April 2016. Replay: https://youtu.be/0t0IWvcO-Uo
Algorithmic Accountability & Learning Analytics
Simon Buckingham Shum
Connected Intelligence Centre, University of Technology Sydney
ABSTRACT. As algorithms pervade societal life, they are moving from the preserve of computer science to becoming the object of far wider academic and media attention. Many are now asking how the behaviour of algorithms can be made “accountable”. But why are they “opaque” and to whom? As this vital discussion unfolds in relation to Big Data in general, the Learning Analytics community must articulate what would count as meaningful questions and satisfactory answers in educational contexts. In this talk, I propose different lenses that we can bring to bear on a given learning analytics tool, to ask what it would mean for it to be accountable, and to whom. From a Human-Centred Informatics perspective, it turns out that algorithmic accountability may be the wrong focus.
BIO. Simon Buckingham Shum is Professor of Learning Informatics at the University of Technology Sydney, which he joined in August 2014 to direct the new Connected Intelligence Centre. Prior to that he was at The Open University’s Knowledge Media Institute 1995-2014. He brings a Human-Centred Informatics (HCI) approach to his work, with a background in Psychology (BSc, York), Ergonomics (MSc, London) and HCI (PhD, York) where he worked with Rank Xerox Cambridge EuroPARC on Design Rationale. He co-edited Visualizing Argumentation (2003) followed by Knowledge Cartography (2008, 2nd Edn. 2014), and with Al Selvin wrote Constructing Knowledge Art (2015). He is active in the emerging field of Learning Analytics and is a co-founder of the Society for Learning Analytics Research, Compendium Institute and Learning Emergence network.
The results of the RISCOSS evaluation have been presented in the PMI academic workshop 2016 at the United Nations Global Service Centre in Brindisi. Many thanks to PMI-NIC volunteers and to TAAS branch.
Similar to Advanced analytics for supporting public policy, bracketology, and beyond! (20)
Optimization with impact: my journey in public sector operations research Laura Albert
eynote talk at the Advances in Data Science & Operations Research Virtual Conference, presented by Universidad Galileo in collaboration with INFORMSttt. It's the first INFORMS conference made for Latino America that brings together the scientific community from the areas of operations research, business intelligence, and data science.
Designing emergency medical service systems to enhance community resilience Laura Albert
Emergency response to patients with medical needs after a disaster is a critical aspect of public safety and community resilience. An effective response to emergency medical patients can be achieved by designing a system that
- Allocates limited resources such as ambulances in resource-constrained settings,
- Leverages data and triage information to inform the design of response districts, and
- Sheds light on how these decisions change after a disaster.
In this talk, Dr. Laura Albert will discuss how analytical methods can be used to design emergency response systems and provide guidance into how to design data-driven emergency response systems. She will discuss how system design decisions must change after weather disasters when the system is congested and critical infrastructure is impaired.
A lecture on location models for public sector operations research. Topics include facility location, coverage models, the p-median model, the p-center model, integer programming.
Operations Research for Homeland Security and Beyond!Laura Albert
Operations Research for Homeland Security and Beyond! Laura Albert McLay's talk on aviation security at the University of Wisconsin-Madison's 50th anniversary reunion for ISYE.
Screening Commercial Aviation Passengers in the Aftermath of September 11, 2001Laura Albert
Screening Commercial Aviation Passengers in the Aftermath of September 11, 2001. Slides from a presentation at the the University of Wisconsin-Madison on September 11, 2015.
Delivering emergency medical services: research, application, and outreachLaura Albert
Laura McLay's slides from the German Operations Research Society Conference for the presentation entitled "Delivering emergency medical services: research application, and outreach"
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
7. Our world is becoming increasingly
complex and increasingly connected
Systems matter!
Mathematical models
and systems thinking
help us study systems
and navigate the
complex, interconnected
world we live in.
WiscNet 2017 Laura Albert 7
17. Why are homeland security problems
good analytics problems?
• Limited resources
• Passenger risk assessments
• Tradeoffs among criteria (efficiency, security, cost)
• Note: TSA has a goal of <10 minutes waiting for
screening
• System and goals are always changing: security is a moving
target
We will always have security challenges, and systems
engineering/analytical tools will always help us address these
challenges.
WiscNet 2017 Laura Albert 17
57. Transitions
Rutgers 52 @ Wisconsin 72
Wisconsin Rutgers 1 − 𝑊
𝑊
𝑊
1 − 𝑊
How much credit should Wisconsin get for beating Rutgers by
20 at home?
𝑊 = effective wins (fraction of a vote), which help us compute
our Markov chain transition probabilities
WiscNet 2017 Laura Albert 57