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Modest Maps JS is a BSD-licensed display and interaction library for tile-based maps in Javascript.
Our intent is to provide a minimal, extensible, customizable, and free display library for discriminating designers and developers who want to use interactive maps in their own projects. Modest Maps provides a core set of features in a tight, clean package, with plenty of hooks for additional functionality.
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Week 8 Cultural Blog
Conflicts
2/15/15
Personally, I hate conflicts. I tend to avoid them at all costs, but some conflicts are unavoidable. One conflict I had recently was with my now ex boyfriend. When we were still together, it was hard for us to find time to spend together because we both worked full time jobs. I worked Monday through Friday while he usually worked on the weekends. Towards the end of our relationship, I got accepted into DBC and started my phase 0 work. My free time became even more scarce. When I went over to his apartment, he would usually stay on his computer and continue to play games for an hour or two. I didn't mind it at first, before I started phase 0. I would usually keep myself occupied and wait for him to finish playing. However, after I started phase 0, I became annoyed by his behavior.
I realized the source of this conflict was because I felt like he was wasting my time, when my free time has become scarce. Upon realizing the conflict, I felt disrespected. I spoke with him about this conflict, but he continued his behavior. Eventually this conflict, among others, became the reason why we ended our relationship. If I could go back, I don't think I would do anything differently. From this experience, I learned that wasting other people's time is disrespectful.


Previous: Tuning validation, Up: Cache transparency [Contents][Index]
If cacheIsShared
is false (it is true by default), Polipo will
ignore the server-side ‘Cache-Control’ directives ‘private’,
‘s-maxage’ and ‘proxy-must-revalidate’. This is highly
desirable behaviour when the proxy is used by just one user, but might
break some sites if the proxy is shared.
When connectivity is very poor, the variable relaxTransparency
can be used to cause Polipo to serve stale instances under some
circumstances. If relaxTransparency
is false
(the
default), all stale instances are validated (see Cache transparency), and failures to connect are reported to the client.
This is the default mode of operation of most other proxies, and the
least likely to surprise the user.
If relaxTransparency
is maybe
, all stale instances are
still validated, but a failure to connect is only reported as an error
if no data is available in the cache. If a connection fails and stale
data is available, it is served to the client with a suitable HTTP/1.1
‘Warning’ header. Current user-agents do not provide visible
indication of such warnings, however, and this setting will typically
cause the browser to display stale data with no indication that
anything went wrong. It is useful when you are consulting a live web
site but don’t want to be bothered with failed revalidations.
If relaxTransparency
is true
, missing data is fetched
from remote servers, but stale data are unconditionally served with no
validation. Client-side ‘Cache-Control’ directives are still
honoured, which means that you can force an end-to-end revalidation
from the browser’s interface (typically by shift-clicking on
“reload”). This setting is only useful if you have very bad network
connectivity or are consulting a very slow web site or one that
provides incorrect cache control information12 and are willing to manually revalidate pages that you suspect
are stale.
If mindlesslyCacheVary
is true, the presence of a ‘Vary’
header (which indicates that content-negotiation occurred,
see Censor Accept-Language) is ignored, and cached negotiated
instances are mindlessly returned to the client. If it is false (the
default), negotiated instances are revalidated on every client
request.
Unfortunately, a number of servers (most notably some versions of
Apache’s mod_deflate
module) send objects with a ‘ETag’
header that will confuse Polipo in the presence of a ‘Vary’
header. Polipo will make a reasonable check for consistency if
‘dontTrustVaryETag’ is set to ‘maybe’ (the default); it will
systematically ignore ‘ETag’ headers on objects with ‘Vary’
headers if it is set to ‘true’.
A number of websites incorrectly mark variable resources as cachable;
such issues can be worked around in polipo by manually marking given
categories of objects as uncachable. If dontCacheCookies
is
true, all pages carrying HTTP cookies will be treated as uncachable.
If dontCacheRedirects
is true, all redirects (301 and 302) will
be treated as uncachable. Finally, if everything else fails, a list
of uncachable URLs can be given in the file specified by
uncachableFile
, which has the same format as the
forbiddenFile
(see Internal forbidden list). If not
specified, its location defaults to ‘~/.polipo-uncachable’ or
‘/data/data/com.termux/files/usr/etc/polipo/uncachable’, whichever
exists.
This is for
example the case of www.microsoft.com
, and also of websites
generated by a popular Free content management system written in
Python.
Previous: Tuning validation, Up: Cache transparency [Contents][Index]


The infinite scroll component dynamically loads data as users scroll.
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Fires when scrolling triggers the need to load more data or when users click the button to load more data. This event only fires when the enabled property is set to true and data is not already loading.


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Work Experience
Senior Research Scientist @ IBM Research AI
NOV 2015 - Current0. Data Quality for AI: I am currently the technical lead in a new strategic area of "Data Quality for AI". Stressing on the importance of data in the AI lifecycle, the aim of this project is to solve the quality problems in the world of data and prepare the data to be AI-ready. We are building a system and toolkit called "Data Readiness Toolkit for AI"
1. Neural Network Modeller: I served as the technical lead for this project: Problem formulation from scratch -> Filing Disclosures -> Writing Research Papers (NeurIPS 2019, AAAI 2018, ICSE NIER 2017) -> Building Prototyping -> Presenting to clients, workshops, business partners -> Writing product level code -> Leading the team into product integration -> Product Release @ IBM Think Event 2018! This system helps in the extreme rapid prototyping of deep learning models and the implementation of state of art papers.
2. Machine Learning for Creativity: Served as the Global Technical co-lead (2018) for IBM Research AI for the research growth strategy "Machine Learning for Creativity - Building Creative Assistants". Got the prestigious IBM Research recognition award for serving as a global technical co-lead in the Global Technical Outreach AI Leadership program, 2018. We worked on a system "PersuAIDE" to create/generate more creative and persuading taglines and images (published in WWW 2018, KDD workshop 2017).
3. iSight: Lead researcher for project iSight, which aims in creating a cognitive automated system to extract the error context for SAP system screenshots and to supply a structured resolution procedure, mined from previous instances of data.
Teaching Experience
Visiting Faculty@ IIIT Bangalore, India
Jan 2019 - May 2019Grad Level course on Visual Recognition (Course Information)
Teaching a graduate level course on “Visual Recognition“ at IIIT Bangalore between Jan - May 2019. This course is at the intersection of Deep Learning and Computer Vision.
Visiting Faculty@ ISME Bangalore, India
Oct 2019 - Dec 2019Foundations of Machine Learning (Course Information)
Teaching a introductory course on “Machine Learning“ at ISM Bangalore between Oct - Dec 2019. This course is for MBA students who does not have a CS/Math background with heavy emphasis on taking ML to business.
Internships
Lane Department, West Virginia University (Visiting Research Scholar )
June 2014 - October 2014Guidance of Prof. Afzel Noore
Eye gaze analysis for latent fingerprint matching: Analyzing the eye gaze patterns of experts while matching latent prints, provides insights of the process and heuristics used by the experts. The gained insights can be used to design better algorithms for automated latent fingerprint matching.
HongKong Polytechnic University (Visiting Research Scholar)
June 2011 - August 2011Guidance of Dr. Ajay Kumar
Feature level fusion of fingerprints: Propose a novel generic framework to fuse information from multiple data sources at feature level, instead of at match score level or decision level. The framework was applied in fingerprints to use multiple fingers of a person as a more confident biometric than using a single finger.
Education
Doctorate of Philosophy (CS)@ IIIT Delhi
July 2010 - Aug 2017Guidance of Dr. Mayank Vatsa and Dr. Richa Singh
Thesis title: "Learning Representations of Matching Fingerprint Variants" (More info)
Undergraduate studies (CS) @ Coimbatore Institute of Technology
August 2006 - April 2010Undergraduate thesis title: "Multi-resolution image query using Haar transformation and image tagging"
Publications
Journals
S. Agrawal, A. Sankaran, A. Laha, S. Ahmed CG, D. Shrivastava, K. Sankaranarayanan, What is Deemed Computationally Creative?, IBM Journal On Research and Development, Special Issue on Computational Creativity, vol. 63, no. 1, pp. 3:1-3:12, Jan.-Feb. 2019. Impact Factor: 0.962
A. Sankaran, A. Majumdar, M. Vatsa, and R. Singh, Group Sparse Autoencoder, Image and Vision Computing, Special Issue on Regularization Techniques for High-Dimensional Data Analysis, Elsevier, vol. 60, pp. 64-74, 2017. Impact Factor: 2.671
A. Sankaran, A. Jain, T. Vashisth, M. Vatsa, and R. Singh, Adaptive latent fingerprint segmentation using feature selection and random decision forest classification, Information Fusion, Elsevier, vol. 34, pp. 1-15, 2017. Impact Factor: 5.667
A. Sankaran, G. Goswami, M. Vatsa, R. Singh, and A. Majumdar, Class Sparsity Signature based Restricted Boltzmann Machines, Pattern Recognition, Special Issue on Deep Image Video, Elsevier, vol. 61, pp. 674-685, 2017. Impact Factor: 4.582
A. Sankaran, M. Vatsa, R. Singh, Multisensor Optical and Latent Fingerprint Database, IEEE Access, vol. 3, pp. 653 - 665, 2015. Impact Factor: 3.244
A. Sankaran, M. Vatsa, R. Singh, Latent Fingerprint Matching: A Survey, IEEE Access, vol. 2, pp. 982-1004, 2014. (Appeared as one of the top-10 highly viewed publication of 2014). Impact Factor: 3.244
Book Chapters
A. Sankaran, A. Malhotra, M. Vatsa, and R. Singh, Learning Representations for Uncontrolled Fingerprint Recognition, Deep Learning in Biometrics, CRC Press, 2018.
A. Malhotra, A. Sankaran, A. Mittal, M. Vatsa, and R. Singh, Fingerphoto Authentication using Smartphone Camera captured under Varying Environmental Conditions, Human Recognition in Outdoor Unconstrained Environments: Using Computer Vision, Pattern Recognition and Machine Learning Methods for Biometrics, Elsevier, 2016. Editors: Maria De Marsico, Michele Nappi and Hugo Proenca
Peer-reviewed Conferences
A. Prabhu, R. Dasgupta, A. Sankaran, S. Tamilselvam, S. Mani. “You might also like this model”: Data Driven Approach for Recommending Deep Learning Models for Unknown Image Datasets, 33rd Conference on Neural Information Processing Systems (NeurIPS), New in ML 2019.
R. Sinha, A. Sankaran, M. Vatsa, and R. Singh. AuthorGAN: Improving GAN Reproducibility using a Modular GAN Framework, 33rd Conference on Neural Information Processing Systems (NeurIPS), MLSys: Workshop on Systems for ML 2019.
S. Tamilselvam, N. Panwar, S. Khare, R. Aralikatte, A. Sankaran, S. Mani, A Visual Programming Paradigm for Abstract Deep Learning Model Development, IndiaHCI, HCI in the age of Machine Intelligence, 2019.
S. Mani, A. Sankaran, R. Aralikatte , DeepTriage: Exploring the Effectiveness of Deep Learning for Bug Triaging, ACM India Joint International Conference on Data Science and Management of Data (CoDS-COMAD), 2019.
N. Gantayat, G. Sridhara, A. Sankaran, S. Dechu, S. Mani, G. Dasgupta, Towards Creating Business Process Models from Images, In International Conference on Service-Oriented Computing (ICSOC), 2018.
A. Sankaran, M. Vatsa, and R. Singh. Intuition Learning, Joint IJCAI/ECAI/AAMAS/ICML Workshop on Domain Adaptation for Visual Understanding (DAVU) 2018.
S. Suri, A. Sankaran, M. Vatsa, and R. Singh. On Matching Faces with Alterations due to Plastic Surgery and Disguise, International Conference on Biometrics: Theory, Applications and Systems (BTAS) , 2018.
R. Aralikatte, N. Gantayat, N. Panwar, A. Sankaran, S. Mani, Sanskrit Sandhi Splitting using seq2(seq)2, In Empirical Methods in Natural Language Processing (EMNLP), 2018.
V. Munigala, A. Mishra, S. G. Tamilselvam, S. Khare, R. Dasgupta, and A. Sankaran, PersuaAIDE ! An Adaptive Persuasive Text Generation System for Fashion Domain, Cognitive Computing Track, TheWebConf (WWW), 2018
A. Sethi, A. Sankaran, N. Panwar, S. Khare, and S. Mani, DLPaper2Code: Auto-generation of Code from Deep Learning Research Papers, Association for the Advancement of Artificial Intelligence (AAAI), 2018
S. Mani, N. Gantayat, R> Aralikatte, M. Gupta, S. Dechu, A. Sankaran, S. Khare, B. Mitchell, H. Subramanian, and H> Venkatarangan, Hi, How can I help you?: Automating enterprise IT support help desks, Innovative Applications of Artificial Intelligence (IAAI), 2018.
A. Sankaran, N. Panwar, S. Khare, S. Mani, A. Sethi, R. Aralikatte, and N. Gantayat, Democratization of Deep Learning using DARVIZ, AAAI - Demo Track, 2018.
S. Mani, N. Gantayat, R. Aralikatte, M. Gupta, S. Dechu, A. Sankaran, S. Khare, B. Mitchell, H. Subramanian, and H. Venkatarangan, Agent Assist: Automating enterprise IT support help desks, AAAI - Demo Track, 2018.
A. Sankaran, R. Aralikatte, S. Mani, S. Khare, N. Panwar, and N. Gantayat. DARVIZ: deep abstract representation, visualization, and verification of deep learning models. International Conference on Software Engineering: New Ideas and Emerging Results Track, pp. 47-50. IEEE Press, 2017.
A. Taneja, A> Tayal, A. Malhotra, A. Sankaran, M. Vatsa, and R. Singh, Fingerphoto Spoofing in Mobile Devices: A Preliminary Study, International Conference on Biometrics: Theory, Applications and Systems, pp. 1-7, 2016.
A. Sankaran, A. Malhotra, A. Mittal, M. Vatsa, and R. Singh. On smartphone camera based fingerphoto authentication, International Conference on Biometrics Theory, Applications and Systems pp. 1-7, 2015
A. Sankaran, A. Agarwal, R. Keshari, S. Ghosh, A. Sharma, M. Vatsa, and R. Singh, Latent Fingerprint from Multiple Surfaces: Database and Quality Analysis, International Conference on Biometrics: Theory, Applications and Systems, pp. 1-6, 2015.
A. Sankaran, P. Pandey, M. Vatsa, R. Singh, On Latent Fingerprint Minutiae Extraction using Stacked Denoising Sparse AutoEncoders, International Joint Conference on Biometrics, 2014, pp. 1-7, (Best Poster Award).
A. Sankaran, M. Vatsa, R. Singh, Automated Clarity and Quality Assessment for Latent Fingerprints: A Preliminary Study, International Conference on Biometrics: Theory, Applications and Systems, pp. 1-6, 2013 (Best Poster Award).
A. Sankaran, M. Vatsa, R. Singh, Hierarchical Fusion for Matching Simultaneous Latent Fingerprint, International Conference on Biometrics: Theory, Applications and Systems, pp. 377-382, 2012.
A. Sankaran, T.I. Dhamecha, M. Vatsa, R. Singh, On Matching Latent to Latent Fingerprints, International Joint Conference on Biometrics, pp. 1-6, 2011.
Technical Reports
S. Mani, A. Sankaran, S. Tamilselvam, A. Sethi, Coverage Testing of Deep Learning Models using Dataset Characterization, arXiv preprint arXiv:1911.07309, 2019.
T. Narendra, A. Sankaran, D. Vijaykeerthy, S. Mani, Explaining Deep Learning Models using Causal Inference, arXiv preprint arXiv:1811.04376, 2018.
N. Panwar, S. Khare, N. Gantayat, R. Aralikatte, S. Mani, and A. Sankaran, mAnI: Movie Amalgamation using Neural Imitation, arXiv preprint arXiv:1708.04923, 2017
V. Munigala, S. Tamilselvam, and A. Sankaran, "Let me convince you to buy my product...": A Case Study of an Automated Persuasive System for Fashion Products, arXiv preprint arXiv:1709.08366, 2017


I'm partial to air conditioning.
 I am not a killer. I think he's got a crush on you, Dex! Makes me a … scientist. Rorschach would say you have a hard time relating to others.
I'm really more an apartment person. I'm generally confused most of the time. Rorschach would say you have a hard time relating to others. Rorschach would say you have a hard time relating to others. Watching ice melt. This is fun.
I'm Dexter, and I'm not sure what I am.
He taught me a code. To survive. I feel like a jigsaw puzzle missing a piece. And I'm not even sure what the picture should be.  I catch killers. I'm a sociopath; there's not much he can do for me.
- Keep your mind limber.
- I'm Dexter, and I'm not sure what I am.
- God created pudding, and then he rested.
Watching ice melt. This is fun.
Tell him time is of the essence.  I catch killers.  I catch killers. I'm really more an apartment person. I've lived in darkness a long time. Over the years my eyes adjusted until the dark became my world and I could see.
- I'm Dexter, and I'm not sure what I am.
- I've lived in darkness a long time. Over the years my eyes adjusted until the dark became my world and I could see.
- I love Halloween. The one time of year when everyone wears a mask … not just me.
I'm partial to air conditioning. I'm going to tell you something that I've never told anyone before. I love Halloween. The one time of year when everyone wears a mask … not just me. I am not a killer.
I'm not the monster he wants me to be. So I'm neither man nor beast. I'm something new entirely. With my own set of rules. I'm Dexter. Boo. I'm generally confused most of the time. God created pudding, and then he rested.
I've lived in darkness a long time. Over the years my eyes adjusted until the dark became my world and I could see.    Oh I beg to differ, I think we have a lot to discuss.
I like seafood. He taught me a code. To survive.  I catch killers. Makes me a … scientist.
I'm really more an apartment person. I'm going to tell you something that I've never told anyone before. Only you could make those words cute. I have a dark side, too. I'm really more an apartment person.
Only you could make those words cute. Finding a needle in a haystack isn't hard when every straw is computerized. I'm a sociopath; there's not much he can do for me. I'm really more an apartment person.
Tonight's the night. And it's going to happen again and again. It has to happen. Somehow, I doubt that. You have a good heart, Dexter. You look…perfect. I am not a killer.
I feel like a jigsaw puzzle missing a piece. And I'm not even sure what the picture should be. Like a sloth. I can do that. He taught me a code. To survive. Oh I beg to differ, I think we have a lot to discuss.
I have a dark side, too. Like a sloth. I can do that. I'm Dexter, and I'm not sure what I am. Tonight's the night. And it's going to happen again and again. It has to happen.
Tell him time is of the essence. I'm a sociopath; there's not much he can do for me. You all right, Dexter? Pretend. You pretend the feelings are there, for the world, for the people around you. Who knows? Maybe one day they will be.
I'm not the monster he wants me to be. So I'm neither man nor beast. I'm something new entirely. With my own set of rules. I'm Dexter. Boo. Rorschach would say you have a hard time relating to others. I'm generally confused most of the time.


Does the following code correctly perform a liner search on an array of ints?
for (int i=0; i < values.length; i++) { if (values[i] == searchedValue){ found = true; } else{ found = false; } } if (found) { System.out.println("found " + searchedValue); } else { System.out.println ("not found"); }
There are no hints for this question


Getting Stuff Done for the Movement.
A place for progressive organizations* (especially those addressing immigration, women's rights, civil rights, criminal justice reform and LGBT rights) to find like minded individuals for discrete tasks.
Please fill out the brief form below so we can begin to develop a repository of volunteers for progressive organizations.
 Your information will not be shared publicly and will only be shared with vetted organizations upon your consent. Still, this is a googleform. Assume our government, the Russian government and wikileaks will read everything you say.
This form was created by long time activists, organizers and lawyers** ready to get to work.
*Organizations will be vetted by a small group of long time activists. Are you a group who wants to get involved? Have a question? Shoot an email to<EMAIL_ADDRESS>**Specifically Marc Krupanski, Joe Phelan, Mariana Ruiz, Purvi Shah, and Sean Sullivan.


America Ferrera Saw Brad Pitt on a Disney Channel Movie Set
by Anne Victoria ClarkSuperstore’s Ben Feldman and America Ferrera came by our video studio at Vulture Festival to talk about some of the early roles we know and love them for. And it turns out America Ferrera’s memories of making the Disney Channel Original Movie Gotta Kick It Up! are just as fond as the ones we have of watching it — specifically, how on her first day on set she saw Jennifer Aniston and Brad Pitt and George Clooney, together! As you may remember, in the ’90s this was basically every celebrity that existed at the time, so it was a pretty big deal.


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API Example
Latest Sensor Data
An example of how to query the latest sensor data for a list of MAC / Device IDs


Assume you have two traits which contains methods with the same signatures e.g. 'foo()' and you create a class 'MyImplementor' implementing both these traits. So the result of invoking
'new MyImplementor().foo()' can be unexpected.
Reports classes which have such a problem.


Arithmetic subgroups, finite index subgroups of \(\SL_2(\ZZ)\)#
- class sage.modular.arithgroup.arithgroup_generic.ArithmeticSubgroup#
Bases:
Group
Base class for arithmetic subgroups of \(\SL_2(\ZZ)\). Not intended to be used directly, but still includes quite a few general-purpose routines which compute data about an arithmetic subgroup assuming that it has a working element testing routine.
- Element#
alias of
ArithmeticSubgroupElement
- are_equivalent(x, y, trans=False)#
Test whether or not cusps x and y are equivalent modulo self.
If self has a reduce_cusp() method, use that; otherwise do a slow explicit test.
If trans = False, returns True or False. If trans = True, then return either False or an element of self mapping x onto y.
EXAMPLES:
sage: Gamma0(7).are_equivalent(Cusp(1/3), Cusp(0), trans=True) [ 3 -1] [-14 5] sage: Gamma0(7).are_equivalent(Cusp(1/3), Cusp(1/7)) False
- as_permutation_group()#
Return self as an arithmetic subgroup defined in terms of the permutation action of \(SL(2,\ZZ)\) on its right cosets.
This method uses Todd-Coxeter enumeration (via the method
todd_coxeter()
) which can be extremely slow for arithmetic subgroups with relatively large index in \(SL(2,\ZZ)\).EXAMPLES:
sage: G = Gamma(3) sage: P = G.as_permutation_group(); P Arithmetic subgroup of index 24 sage: G.ncusps() == P.ncusps() True sage: G.nu2() == P.nu2() True sage: G.nu3() == P.nu3() True sage: G.an_element() in P True sage: P.an_element() in G True
- coset_reps(G=None)#
Return right coset representatives for self \ G, where G is another arithmetic subgroup that contains self. If G = None, default to G = SL2Z.
For generic arithmetic subgroups G this is carried out by Todd-Coxeter enumeration; here G is treated as a black box, implementing nothing but membership testing.
EXAMPLES:
sage: sage.modular.arithgroup.arithgroup_generic.ArithmeticSubgroup().coset_reps() Traceback (most recent call last): ... NotImplementedError: Please implement _contains_sl2 for <class 'sage.modular.arithgroup.arithgroup_generic.ArithmeticSubgroup_with_category'> sage: sage.modular.arithgroup.arithgroup_generic.ArithmeticSubgroup.coset_reps(Gamma0(3)) [ [1 0] [ 0 -1] [ 0 -1] [ 0 -1] [0 1], [ 1 0], [ 1 1], [ 1 2] ]
- cusp_data(c)#
Return a triple (g, w, t) where g is an element of self generating the stabiliser of the given cusp, w is the width of the cusp, and t is 1 if the cusp is regular and -1 if not.
EXAMPLES:
sage: Gamma1(4).cusp_data(Cusps(1/2)) ( [ 1 -1] [ 4 -3], 1, -1 )
- cusp_width(c)#
Return the width of the orbit of cusps represented by c.
EXAMPLES:
sage: Gamma0(11).cusp_width(Cusps(oo)) 1 sage: Gamma0(11).cusp_width(0) 11 sage: [Gamma0(100).cusp_width(c) for c in Gamma0(100).cusps()] [100, 1, 4, 1, 1, 1, 4, 25, 1, 1, 4, 1, 25, 4, 1, 4, 1, 1]
- cusps(algorithm='default')#
Return a sorted list of inequivalent cusps for self, i.e. a set of representatives for the orbits of self on \(\mathbb{P}^1(\QQ)\).
These should be returned in a reduced form where this makes sense.
INPUT:
algorithm
– which algorithm to use to compute the cusps of self.'default'
finds representatives for a known complete set of cusps.'modsym'
computes the boundary map on the space of weight two modular symbols associated to self, which finds the cusps for self in the process.
EXAMPLES:
sage: Gamma0(36).cusps() [0, 1/18, 1/12, 1/9, 1/6, 1/4, 1/3, 5/12, 1/2, 2/3, 5/6, Infinity] sage: Gamma0(36).cusps(algorithm='modsym') == Gamma0(36).cusps() True sage: GammaH(36, [19,29]).cusps() == Gamma0(36).cusps() True sage: Gamma0(1).cusps() [Infinity]
- dimension_cusp_forms(k=2)#
Return the dimension of the space of weight k cusp forms for this group. For \(k \ge 2\), this is given by a standard formula in terms of k and various invariants of the group; see Diamond + Shurman, “A First Course in Modular Forms”, section 3.5 and 3.6. If k is not given, default to k = 2.
For dimensions of spaces of cusp forms with character for Gamma1, use the dimension_cusp_forms method of the Gamma1 class, or the standalone function dimension_cusp_forms().
For weight 1 cusp forms this generic implementation only works in cases where one can prove solely via Riemann-Roch theory that there aren’t any cusp forms (i.e. when the number of regular cusps is strictly greater than the degree of the canonical divisor). Otherwise a NotImplementedError is raised.
EXAMPLES:
sage: Gamma1(31).dimension_cusp_forms(2) 26 sage: Gamma(5).dimension_cusp_forms(1) 0 sage: Gamma1(4).dimension_cusp_forms(1) # irregular cusp 0 sage: Gamma(13).dimension_cusp_forms(1) Traceback (most recent call last): ... NotImplementedError: Computation of dimensions of weight 1 cusp forms spaces not implemented in general
- dimension_eis(k=2)#
Return the dimension of the space of weight k Eisenstein series for this group, which is a subspace of the space of modular forms complementary to the space of cusp forms.
INPUT:
k
- an integer (default 2).
EXAMPLES:
sage: GammaH(33,[2]).dimension_eis() 7 sage: GammaH(33,[2]).dimension_eis(3) 0 sage: GammaH(33, [2,5]).dimension_eis(2) 3 sage: GammaH(33, [4]).dimension_eis(1) 4
- dimension_modular_forms(k=2)#
Return the dimension of the space of weight k modular forms for this group. This is given by a standard formula in terms of k and various invariants of the group; see Diamond + Shurman, “A First Course in Modular Forms”, section 3.5 and 3.6. If k is not given, defaults to k = 2.
For dimensions of spaces of modular forms with character for Gamma1, use the dimension_modular_forms method of the Gamma1 class, or the standalone function dimension_modular_forms().
For weight 1 modular forms this generic implementation only works in cases where one can prove solely via Riemann-Roch theory that there aren’t any cusp forms (i.e. when the number of regular cusps is strictly greater than the degree of the canonical divisor). Otherwise a NotImplementedError is raised.
EXAMPLES:
sage: Gamma1(31).dimension_modular_forms(2) 55 sage: Gamma1(3).dimension_modular_forms(1) 1 sage: Gamma1(4).dimension_modular_forms(1) # irregular cusp 1 sage: Gamma(13).dimension_modular_forms(1) Traceback (most recent call last): ... NotImplementedError: Computation of dimensions of weight 1 cusp forms spaces not implemented in general
- farey_symbol()#
Return the Farey symbol associated to this subgroup. See the
farey_symbol
module for more information.EXAMPLES:
sage: Gamma1(4).farey_symbol() FareySymbol(Congruence Subgroup Gamma1(4))
- gen(i)#
Return the i-th generator of self, i.e. the i-th element of the tuple self.gens().
EXAMPLES:
sage: SL2Z.gen(1) [1 1] [0 1]
- generalised_level()#
Return the generalised level of
self
, i.e., the least common multiple of the widths of all cusps.If
self
is even, Wohlfart’s theorem tells us that this is equal to the (conventional) level ofself
whenself
is a congruence subgroup. This can fail ifself
is odd, but the actual level is at most twice the generalised level. See the paper by Kiming, Schuett and Verrill for more examples.EXAMPLES:
sage: Gamma0(18).generalised_level() 18 sage: from sage.modular.arithgroup.arithgroup_perm import HsuExample18 sage: HsuExample18().generalised_level() 24
In the following example, the actual level is twice the generalised level. This is the group \(G_2\) from Example 17 of K-S-V.
sage: G = CongruenceSubgroup(8, [ [1,1,0,1], [3,-1,4,-1] ]) sage: G.level() 8 sage: G.generalised_level() 4
- generators(algorithm='farey')#
Return a list of generators for this congruence subgroup. The result is cached.
INPUT:
algorithm
(string): eitherfarey
ortodd-coxeter
.
If
algorithm
is set to"farey"
, then the generators will be calculated using Farey symbols, which will always return a minimal generating set. Seefarey_symbol
for more information.If
algorithm
is set to"todd-coxeter"
, a simpler algorithm based on Todd-Coxeter enumeration will be used. This is exceedingly slow for general subgroups, and the list of generators will be far from minimal (indeed it may contain repetitions).EXAMPLES:
sage: Gamma(2).generators() [ [1 2] [ 3 -2] [-1 0] [0 1], [ 2 -1], [ 0 -1] ] sage: Gamma(2).generators(algorithm="todd-coxeter") [ [1 2] [-1 0] [ 1 0] [-1 0] [-1 2] [-1 0] [1 0] [0 1], [ 0 -1], [-2 1], [ 0 -1], [-2 3], [ 2 -1], [2 1] ]
- gens(*args, **kwds)#
Return a tuple of generators for this congruence subgroup.
The generators need not be minimal. For arguments, see
generators()
.EXAMPLES:
sage: SL2Z.gens() ( [ 0 -1] [1 1] [ 1 0], [0 1] )
- genus()#
Return the genus of the modular curve of
self
.EXAMPLES:
sage: Gamma1(5).genus() 0 sage: Gamma1(31).genus() 26 sage: from sage.modular.dims import dimension_cusp_forms sage: Gamma1(157).genus() == dimension_cusp_forms(Gamma1(157), 2) True sage: GammaH(7, [2]).genus() 0 sage: [Gamma0(n).genus() for n in [1..23]] [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 2, 2] sage: [n for n in [1..200] if Gamma0(n).genus() == 1] [11, 14, 15, 17, 19, 20, 21, 24, 27, 32, 36, 49]
- index()#
Return the index of self in the full modular group.
EXAMPLES:
sage: Gamma0(17).index() 18 sage: sage.modular.arithgroup.congroup_generic.CongruenceSubgroup(5).index() Traceback (most recent call last): ... NotImplementedError
- is_abelian()#
Return True if this arithmetic subgroup is abelian.
Since arithmetic subgroups are always nonabelian, this always returns False.
EXAMPLES:
sage: SL2Z.is_abelian() False sage: Gamma0(3).is_abelian() False sage: Gamma1(12).is_abelian() False sage: GammaH(4, [3]).is_abelian() False
- is_congruence()#
Return True if self is a congruence subgroup.
EXAMPLES:
sage: Gamma0(5).is_congruence() True sage: sage.modular.arithgroup.arithgroup_generic.ArithmeticSubgroup().is_congruence() Traceback (most recent call last): ... NotImplementedError
- is_even()#
Return True precisely if this subgroup contains the matrix -1.
EXAMPLES:
sage: SL2Z.is_even() True sage: Gamma0(20).is_even() True sage: Gamma1(5).is_even() False sage: GammaH(11, [3]).is_even() False
- is_finite()#
Return True if this arithmetic subgroup is finite.
Since arithmetic subgroups are always infinite, this always returns False.
EXAMPLES:
sage: SL2Z.is_finite() False sage: Gamma0(3).is_finite() False sage: Gamma1(12).is_finite() False sage: GammaH(4, [3]).is_finite() False
- is_normal()#
Return True precisely if this subgroup is a normal subgroup of SL2Z.
EXAMPLES:
sage: Gamma(3).is_normal() True sage: Gamma1(3).is_normal() False
- is_odd()#
Return True precisely if this subgroup does not contain the matrix -1.
EXAMPLES:
sage: SL2Z.is_odd() False sage: Gamma0(20).is_odd() False sage: Gamma1(5).is_odd() True sage: GammaH(11, [3]).is_odd() True
- is_parent_of(x)#
Check whether this group is a valid parent for the element x. Required by Sage’s testing framework.
EXAMPLES:
sage: Gamma(3).is_parent_of(ZZ(1)) False sage: Gamma(3).is_parent_of([1,0,0,1]) False sage: Gamma(3).is_parent_of(SL2Z([1,1,0,1])) False sage: Gamma(3).is_parent_of(SL2Z(1)) True
- is_regular_cusp(c)#
Return True if the orbit of the given cusp is a regular cusp for self, otherwise False. This is automatically true if -1 is in self.
EXAMPLES:
sage: Gamma1(4).is_regular_cusp(Cusps(1/2)) False sage: Gamma1(4).is_regular_cusp(Cusps(oo)) True
- is_subgroup(right)#
Return True if self is a subgroup of right, and False otherwise. For generic arithmetic subgroups this is done by the absurdly slow algorithm of checking all of the generators of self to see if they are in right.
EXAMPLES:
sage: sage.modular.arithgroup.arithgroup_generic.ArithmeticSubgroup().is_subgroup(SL2Z) Traceback (most recent call last): ... NotImplementedError: Please implement _contains_sl2 for <class 'sage.modular.arithgroup.arithgroup_generic.ArithmeticSubgroup_with_category'> sage: sage.modular.arithgroup.arithgroup_generic.ArithmeticSubgroup.is_subgroup(Gamma1(18), Gamma0(6)) True
- matrix_space()#
Return the parent space of the matrices, which is always
MatrixSpace(ZZ, 2)
.EXAMPLES:
sage: Gamma(3).matrix_space() Full MatrixSpace of 2 by 2 dense matrices over Integer Ring
- ncusps()#
Return the number of cusps of this arithmetic subgroup. This is provided as a separate function since for dimension formulae in even weight all we need to know is the number of cusps, and this can be calculated very quickly, while enumerating all cusps is much slower.
EXAMPLES:
sage: sage.modular.arithgroup.arithgroup_generic.ArithmeticSubgroup.ncusps(Gamma0(7)) 2
- ngens()#
Return the size of the minimal generating set of self returned by
generators()
.EXAMPLES:
sage: Gamma0(22).ngens() 8 sage: Gamma1(14).ngens() 13 sage: GammaH(11, [3]).ngens() 3 sage: SL2Z.ngens() 2
- nirregcusps()#
Return the number of cusps of self that are “irregular”, i.e. their stabiliser can only be generated by elements with both eigenvalues -1 rather than +1. If the group contains -1, every cusp is clearly regular.
EXAMPLES:
sage: sage.modular.arithgroup.arithgroup_generic.ArithmeticSubgroup.nirregcusps(Gamma1(4)) 1
- nregcusps()#
Return the number of cusps of self that are “regular”, i.e. their stabiliser has a generator with both eigenvalues +1 rather than -1. If the group contains -1, every cusp is clearly regular.
EXAMPLES:
sage: sage.modular.arithgroup.arithgroup_generic.ArithmeticSubgroup.nregcusps(Gamma1(4)) 2
- nu2()#
Return the number of orbits of elliptic points of order 2 for this arithmetic subgroup.
EXAMPLES:
sage: sage.modular.arithgroup.arithgroup_generic.ArithmeticSubgroup().nu2() Traceback (most recent call last): ... NotImplementedError: Please implement _contains_sl2 for <class 'sage.modular.arithgroup.arithgroup_generic.ArithmeticSubgroup_with_category'> sage: sage.modular.arithgroup.arithgroup_generic.ArithmeticSubgroup.nu2(Gamma0(1105)) == 8 True
- nu3()#
Return the number of orbits of elliptic points of order 3 for this arithmetic subgroup.
EXAMPLES:
sage: sage.modular.arithgroup.arithgroup_generic.ArithmeticSubgroup().nu3() Traceback (most recent call last): ... NotImplementedError: Please implement _contains_sl2 for <class 'sage.modular.arithgroup.arithgroup_generic.ArithmeticSubgroup_with_category'> sage: sage.modular.arithgroup.arithgroup_generic.ArithmeticSubgroup.nu3(Gamma0(1729)) == 8 True
We test that a bug in handling of subgroups not containing -1 is fixed:
sage: sage.modular.arithgroup.arithgroup_generic.ArithmeticSubgroup.nu3(GammaH(7, [2])) 2
- order()#
Return the number of elements in this arithmetic subgroup.
Since arithmetic subgroups are always infinite, this always returns infinity.
EXAMPLES:
sage: SL2Z.order() +Infinity sage: Gamma0(5).order() +Infinity sage: Gamma1(2).order() +Infinity sage: GammaH(12, [5]).order() +Infinity
- projective_index()#
Return the index of the image of self in \(\PSL_2(\ZZ)\). This is equal to the index of self if self contains -1, and half of this otherwise.
This is equal to the degree of the natural map from the modular curve of self to the \(j\)-line.
EXAMPLES:
sage: Gamma0(5).projective_index() 6 sage: Gamma1(5).projective_index() 12
- reduce_cusp(c)#
Given a cusp \(c \in \mathbb{P}^1(\QQ)\), return the unique reduced cusp equivalent to c under the action of self, where a reduced cusp is an element \(\tfrac{r}{s}\) with r,s coprime non-negative integers, s as small as possible, and r as small as possible for that s.
NOTE: This function should be overridden by all subclasses.
EXAMPLES:
sage: sage.modular.arithgroup.arithgroup_generic.ArithmeticSubgroup().reduce_cusp(1/4) Traceback (most recent call last): ... NotImplementedError
- sturm_bound(weight=2)#
Returns the Sturm bound for modular forms of the given weight and level this subgroup.
INPUT:
weight
- an integer \(\geq 2\) (default: 2)
EXAMPLES:
sage: Gamma0(11).sturm_bound(2) 2 sage: Gamma0(389).sturm_bound(2) 65 sage: Gamma0(1).sturm_bound(12) 1 sage: Gamma0(100).sturm_bound(2) 30 sage: Gamma0(1).sturm_bound(36) 3 sage: Gamma0(11).sturm_bound() 2 sage: Gamma0(13).sturm_bound() 3 sage: Gamma0(16).sturm_bound() 4 sage: GammaH(16,[13]).sturm_bound() 8 sage: GammaH(16,[15]).sturm_bound() 16 sage: Gamma1(16).sturm_bound() 32 sage: Gamma1(13).sturm_bound() 28 sage: Gamma1(13).sturm_bound(5) 70
FURTHER DETAILS: This function returns a positive integer \(n\) such that the Hecke operators \(T_1,\ldots, T_n\) acting on cusp forms generate the Hecke algebra as a \(\ZZ\)-module when the character is trivial or quadratic. Otherwise, \(T_1,\ldots,T_n\) generate the Hecke algebra at least as a \(\ZZ[\varepsilon]\)-module, where \(\ZZ[\varepsilon]\) is the ring generated by the values of the Dirichlet character \(\varepsilon\). Alternatively, this is a bound such that if two cusp forms associated to this space of modular symbols are congruent modulo \((\lambda, q^n)\), then they are congruent modulo \(\lambda\).
REFERENCES:
See the Agashe-Stein appendix to Lario and Schoof, Some computations with Hecke rings and deformation rings, Experimental Math., 11 (2002), no. 2, 303-311.
This result originated in the paper Sturm, On the congruence of modular forms, Springer LNM 1240, 275-280, 1987.
REMARK: Kevin Buzzard pointed out to me (William Stein) in Fall 2002 that the above bound is fine for \(\Gamma_1(N)\) with character, as one sees by taking a power of \(f\). More precisely, if \(f \cong 0 \pmod{p}\) for first \(s\) coefficients, then \(f^r \cong 0 \pmod{p}\) for first \(sr\) coefficients. Since the weight of \(f^r\) is \(r\cdot k(f)\), it follows that if \(s \geq b\), where \(b\) is the Sturm bound for \(\Gamma_0(N)\) at weight \(k(f)\), then \(f^r\) has valuation large enough to be forced to be \(0\) at \(r*k(f)\) by Sturm bound (which is valid if we choose \(r\) correctly). Thus \(f \cong 0 \pmod{p}\). Conclusion: For \(\Gamma_1(N)\) with fixed character, the Sturm bound is exactly the same as for \(\Gamma_0(N)\).
A key point is that we are finding \(\ZZ[\varepsilon]\) generators for the Hecke algebra here, not \(\ZZ\)-generators. So if one wants generators for the Hecke algebra over \(\ZZ\), this bound must be suitably modified (and I’m not sure what the modification is).
AUTHORS:
William Stein
- to_even_subgroup()#
Return the smallest even subgroup of \(SL(2, \ZZ)\) containing self.
EXAMPLES:
sage: sage.modular.arithgroup.arithgroup_generic.ArithmeticSubgroup().to_even_subgroup() Traceback (most recent call last): ... NotImplementedError: Please implement _contains_sl2 for <class 'sage.modular.arithgroup.arithgroup_generic.ArithmeticSubgroup_with_category'>
- todd_coxeter(G=None, on_right=True)#
Compute coset representatives for self \ G and action of standard generators on them via Todd-Coxeter enumeration.
If
G
isNone
, default toSL2Z
. The method also computes generators of the subgroup at same time.INPUT:
G
- intermediate subgroup (currently not implemented if different from SL(2,Z))on_right
- boolean (default: True) - if True return right coset enumeration, if False return left one.
This is extremely slow in general.
OUTPUT:
a list of coset representatives
a list of generators for the group
l
- list of integers that correspond to the action of the standard parabolic element [[1,1],[0,1]] of \(SL(2,\ZZ)\) on the cosets of self.s
- list of integers that correspond to the action of the standard element of order \(2\) [[0,-1],[1,0]] on the cosets of self.
EXAMPLES:
sage: L = SL2Z([1,1,0,1]) sage: S = SL2Z([0,-1,1,0]) sage: G = Gamma(2) sage: reps, gens, l, s = G.todd_coxeter() sage: len(reps) == G.index() True sage: all(reps[i] * L * ~reps[l[i]] in G for i in range(6)) True sage: all(reps[i] * S * ~reps[s[i]] in G for i in range(6)) True sage: G = Gamma0(7) sage: reps, gens, l, s = G.todd_coxeter() sage: len(reps) == G.index() True sage: all(reps[i] * L * ~reps[l[i]] in G for i in range(8)) True sage: all(reps[i] * S * ~reps[s[i]] in G for i in range(8)) True sage: G = Gamma1(3) sage: reps, gens, l, s = G.todd_coxeter(on_right=False) sage: len(reps) == G.index() True sage: all(~reps[l[i]] * L * reps[i] in G for i in range(8)) True sage: all(~reps[s[i]] * S * reps[i] in G for i in range(8)) True sage: G = Gamma0(5) sage: reps, gens, l, s = G.todd_coxeter(on_right=False) sage: len(reps) == G.index() True sage: all(~reps[l[i]] * L * reps[i] in G for i in range(6)) True sage: all(~reps[s[i]] * S * reps[i] in G for i in range(6)) True
- sage.modular.arithgroup.arithgroup_generic.is_ArithmeticSubgroup(x)#
Return
True
ifx
is of typeArithmeticSubgroup
.EXAMPLES:
sage: from sage.modular.arithgroup.all import is_ArithmeticSubgroup sage: is_ArithmeticSubgroup(GL(2, GF(7))) False sage: is_ArithmeticSubgroup(Gamma0(4)) True


public class cmdExec
extends java.lang.Object
| Constructor and Description |
|---|
cmdExec(java.lang.String user,
java.lang.String host,
java.lang.String paswrd)
Constructor method, currently assigns values for login
credentials
|
| Modifier and Type | Method and Description |
|---|---|
void |
closeSession()
Close the connection to the channel.
|
void |
connectSession()
Open the connection to the channel.
|
void |
getCommand()
Retrieve command through manual user input
|
java.lang.String |
getReturnVal()
Retrieve the return value of the last command.
|
static void |
main(java.lang.String[] args)
The main method of our class, so we can run the whole shebang.
|
void |
runCommand()
A method to run the commands
|
void |
setCommand(java.lang.String command)
Set the command to run pragmatically
|
void |
setPort(java.lang.Integer port)
Sets the port to something other than default if necessary.
|
public cmdExec(java.lang.String user, java.lang.String host, java.lang.String paswrd)
user
- String value of the ssh username to login to the server.host
- String value of the server to connect to.paswrd
- String value of the password to connect to the server.public void connectSession()
Maybe this should return something, so it's known if the connection was successful or not.
public void closeSession()
public void setPort(java.lang.Integer port)
port
- Integer value of port to connect to on the server.public void getCommand()
public void setCommand(java.lang.String command)
command
- String value of the command that should be sent to
the serverpublic java.lang.String getReturnVal()
public void runCommand()
public static void main(java.lang.String[] args)
The main method of our class, so we can run the whole shebang.
Usage:Currently the only possibility is running jython scripts. Future support of jruby scripts is planned. Although, it should be fairly easy to add other scripting languages.
-s Pass a jython script for the program to execute.
It's possible to start directly from the command line by passing various parameters.
An example connection:
java -jar sshClient.jar -h example.com -u user
As you can see the password parameter was left off. This can be done for added security, as it will prompt you for the password later.
args
- accepts a string array of possible arguments.


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--- title: Projects layout: my-projects-collection-layout author_profile: true permalink: /projects/ collection: projects second_collection: projects2 entries_layout: grid classes: wide ---
These are not all my projects, just a subset I want to show:


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Collider2D¶
The Collider2D module.
Collider2D Cast¶
-
UnityEngineCollider2D.
__Cast__UnityEngineVector2_UnityEngineContactFilter2D_UnityEngineRaycastHit2DArray__SystemInt32
(UnityEngine.Collider2D instanceUnityEngine.Vector2 directionUnityEngine.ContactFilter2D contactFilterUnityEngine.RaycastHit2D[] results)¶
outputs: unnamed (Int32
)
flow inputs: unnamed
flow outputs: unnamed
Collider2D Distance¶
-
UnityEngineCollider2D.
__Distance__UnityEngineCollider2D__UnityEngineColliderDistance2D
(UnityEngine.Collider2D instanceUnityEngine.Collider2D collider)¶
outputs: unnamed (ColliderDistance2D
)
flow: none
op_Equality¶
-
UnityEngineCollider2D.
__op_Equality__UnityEngineObject_UnityEngineObject__SystemBoolean
(UnityEngine.Object UnityEngine.Object)¶
outputs: unnamed (Boolean
)
flow: none
Collider2D Equals¶
-
UnityEngineCollider2D.
__Equals__SystemObject__SystemBoolean
(UnityEngine.Collider2D instanceSystem.Object other)¶
outputs: unnamed (Boolean
)
flow: none
Collider2D get_attachedRigidbody¶
-
UnityEngineCollider2D.
__get_attachedRigidbody__UnityEngineRigidbody2D
(UnityEngine.Collider2D instance)¶
outputs: unnamed (Rigidbody2D
)
flow: none
Collider2D get_bounciness¶
-
UnityEngineCollider2D.
__get_bounciness__SystemSingle
(UnityEngine.Collider2D instance)¶
outputs: unnamed (Single
)
flow: none
Collider2D get_bounds¶
-
UnityEngineCollider2D.
__get_bounds__UnityEngineBounds
(UnityEngine.Collider2D instance)¶
outputs: unnamed (Bounds
)
flow: none
Collider2D get_composite¶
-
UnityEngineCollider2D.
__get_composite__UnityEngineCompositeCollider2D
(UnityEngine.Collider2D instance)¶
outputs: unnamed (CompositeCollider2D
)
flow: none
Collider2D get_density¶
-
UnityEngineCollider2D.
__get_density__SystemSingle
(UnityEngine.Collider2D instance)¶
outputs: unnamed (Single
)
flow: none
Collider2D get_enabled¶
-
UnityEngineCollider2D.
__get_enabled__SystemBoolean
(UnityEngine.Collider2D instance)¶
outputs: unnamed (Boolean
)
flow: none
Collider2D get_friction¶
-
UnityEngineCollider2D.
__get_friction__SystemSingle
(UnityEngine.Collider2D instance)¶
outputs: unnamed (Single
)
flow: none
Collider2D get_gameObject¶
-
UnityEngineCollider2D.
__get_gameObject__UnityEngineGameObject
(UnityEngine.Collider2D instance)¶
outputs: unnamed (GameObject
)
flow: none
Collider2D get_isTrigger¶
-
UnityEngineCollider2D.
__get_isTrigger__SystemBoolean
(UnityEngine.Collider2D instance)¶
outputs: unnamed (Boolean
)
flow: none
Collider2D get_name¶
-
UnityEngineCollider2D.
__get_name__SystemString
(UnityEngine.Collider2D instance)¶
outputs: unnamed (String
)
flow: none
Collider2D get_offset¶
-
UnityEngineCollider2D.
__get_offset__UnityEngineVector2
(UnityEngine.Collider2D instance)¶
outputs: unnamed (Vector2
)
flow: none
Collider2D get_shapeCount¶
-
UnityEngineCollider2D.
__get_shapeCount__SystemInt32
(UnityEngine.Collider2D instance)¶
outputs: unnamed (Int32
)
flow: none
Collider2D get_transform¶
-
UnityEngineCollider2D.
__get_transform__UnityEngineTransform
(UnityEngine.Collider2D instance)¶
outputs: unnamed (Transform
)
flow: none
Collider2D get_usedByComposite¶
-
UnityEngineCollider2D.
__get_usedByComposite__SystemBoolean
(UnityEngine.Collider2D instance)¶
outputs: unnamed (Boolean
)
flow: none
Collider2D get_usedByEffector¶
-
UnityEngineCollider2D.
__get_usedByEffector__SystemBoolean
(UnityEngine.Collider2D instance)¶
outputs: unnamed (Boolean
)
flow: none
Collider2D GetComponent¶
-
UnityEngineCollider2D.
__GetComponent__SystemType__UnityEngineComponent
(UnityEngine.Collider2D instanceSystem.Type type)¶
outputs: unnamed (Component
)
flow: none
Collider2D GetComponentInChildren¶
-
UnityEngineCollider2D.
__GetComponentInChildren__SystemBoolean__T
(UnityEngine.Collider2D instanceSystem.Boolean includeInactiveSystem.Type type)¶
outputs: T (Object
)
flow: none
Collider2D GetComponentInParent¶
-
UnityEngineCollider2D.
__GetComponentInParent__SystemType__UnityEngineComponent
(UnityEngine.Collider2D instanceSystem.Type t)¶
outputs: unnamed (Component
)
flow: none
Collider2D GetComponents¶
-
UnityEngineCollider2D.
__GetComponents__ListT__SystemVoid
(UnityEngine.Collider2D instanceSystem.Collections.Generic.List`1[UnityEngine.Object] results)¶
outputs: none
flow inputs: unnamed
flow outputs: unnamed
Collider2D GetComponentsInChildren¶
-
UnityEngineCollider2D.
__GetComponentsInChildren__SystemBoolean_ListT__SystemVoid
(UnityEngine.Collider2D instanceSystem.Boolean includeInactiveSystem.Collections.Generic.List`1[UnityEngine.Object] result)¶
outputs: none
flow inputs: unnamed
flow outputs: unnamed
Collider2D GetComponentsInParent¶
-
UnityEngineCollider2D.
__GetComponentsInParent__SystemBoolean_ListT__SystemVoid
(UnityEngine.Collider2D instanceSystem.Boolean includeInactiveSystem.Collections.Generic.List`1[UnityEngine.Object] results)¶
outputs: none
flow inputs: unnamed
flow outputs: unnamed
Collider2D GetContacts¶
-
UnityEngineCollider2D.
__GetContacts__UnityEngineCollider2DArray__SystemInt32
(UnityEngine.Collider2D instanceUnityEngine.Collider2D[] colliders)¶
outputs: unnamed (Int32
)
flow: none
Collider2D GetHashCode¶
-
UnityEngineCollider2D.
__GetHashCode__SystemInt32
(UnityEngine.Collider2D instance)¶
outputs: unnamed (Int32
)
flow: none
Collider2D GetInstanceID¶
-
UnityEngineCollider2D.
__GetInstanceID__SystemInt32
(UnityEngine.Collider2D instance)¶
outputs: unnamed (Int32
)
flow: none
Collider2D GetType¶
-
UnityEngineCollider2D.
__GetType__SystemType
(UnityEngine.Collider2D instance)¶
outputs: unnamed (Type
)
flow: none
op_Implicit¶
-
UnityEngineCollider2D.
__op_Implicit__UnityEngineObject__SystemBoolean
(UnityEngine.Object)¶
outputs: unnamed (Boolean
)
flow: none
op_Inequality¶
-
UnityEngineCollider2D.
__op_Inequality__UnityEngineObject_UnityEngineObject__SystemBoolean
(UnityEngine.Object UnityEngine.Object)¶
outputs: unnamed (Boolean
)
flow: none
Collider2D IsTouching¶
-
UnityEngineCollider2D.
__IsTouching__UnityEngineCollider2D__SystemBoolean
(UnityEngine.Collider2D instanceUnityEngine.Collider2D collider)¶
outputs: unnamed (Boolean
)
flow: none
Collider2D IsTouchingLayers¶
-
UnityEngineCollider2D.
__IsTouchingLayers__SystemBoolean
(UnityEngine.Collider2D instance)¶
outputs: unnamed (Boolean
)
flow: none
Collider2D OverlapCollider¶
-
UnityEngineCollider2D.
__OverlapCollider__UnityEngineContactFilter2D_UnityEngineCollider2DArray__SystemInt32
(UnityEngine.Collider2D instanceUnityEngine.ContactFilter2D contactFilterUnityEngine.Collider2D[] results)¶
outputs: unnamed (Int32
)
flow inputs: unnamed
flow outputs: unnamed
Collider2D OverlapPoint¶
-
UnityEngineCollider2D.
__OverlapPoint__UnityEngineVector2__SystemBoolean
(UnityEngine.Collider2D instanceUnityEngine.Vector2 point)¶
outputs: unnamed (Boolean
)
flow: none
Collider2D Raycast¶
-
UnityEngineCollider2D.
__Raycast__UnityEngineVector2_UnityEngineContactFilter2D_UnityEngineRaycastHit2DArray__SystemInt32
(UnityEngine.Collider2D instanceUnityEngine.Vector2 directionUnityEngine.ContactFilter2D contactFilterUnityEngine.RaycastHit2D[] results)¶
outputs: unnamed (Int32
)
flow inputs: unnamed
flow outputs: unnamed
Collider2D set_density¶
-
UnityEngineCollider2D.
__set_density__SystemSingle__SystemVoid
(UnityEngine.Collider2D instanceSystem.Single value)¶
outputs: none
flow inputs: unnamed
flow outputs: unnamed
Collider2D set_enabled¶
-
UnityEngineCollider2D.
__set_enabled__SystemBoolean__SystemVoid
(UnityEngine.Collider2D instanceSystem.Boolean value)¶
outputs: none
flow inputs: unnamed
flow outputs: unnamed
Collider2D set_isTrigger¶
-
UnityEngineCollider2D.
__set_isTrigger__SystemBoolean__SystemVoid
(UnityEngine.Collider2D instanceSystem.Boolean value)¶
outputs: none
flow inputs: unnamed
flow outputs: unnamed
Collider2D set_name¶
-
UnityEngineCollider2D.
__set_name__SystemString__SystemVoid
(UnityEngine.Collider2D instanceSystem.String value)¶
outputs: none
flow inputs: unnamed
flow outputs: unnamed
Collider2D set_offset¶
-
UnityEngineCollider2D.
__set_offset__UnityEngineVector2__SystemVoid
(UnityEngine.Collider2D instanceUnityEngine.Vector2 value)¶
outputs: none
flow inputs: unnamed
flow outputs: unnamed
Collider2D set_usedByComposite¶
-
UnityEngineCollider2D.
__set_usedByComposite__SystemBoolean__SystemVoid
(UnityEngine.Collider2D instanceSystem.Boolean value)¶
outputs: none
flow inputs: unnamed
flow outputs: unnamed
Collider2D set_usedByEffector¶
-
UnityEngineCollider2D.
__set_usedByEffector__SystemBoolean__SystemVoid
(UnityEngine.Collider2D instanceSystem.Boolean value)¶
outputs: none
flow inputs: unnamed
flow outputs: unnamed
Collider2D ToString¶
-
UnityEngineCollider2D.
__ToString__SystemString
(UnityEngine.Collider2D instance)¶
outputs: unnamed (String
)
flow: none


I am an Assistant Professor in the School of Communication and Information at Rutgers university. Prior to that, I was the Michael Hammer Post-doc at the Institute for Data, Society and Systems at MIT. Prior to that I spent a year working with Prof. Robert West in the Data science lab at EPFL. I finished my PhD at Aalto University, Helsinki, under the supervision of Prof. Aris Gionis. My PhD thesis was on identifying and countering online polarization using algorithmic techniques. My broad research interests include Social Computing, Computational Social Science, Graphs, Data Mining, and Machine Learning.
Before starting my PhD, I worked at Yahoo Research and Qatar Computing Research Institute for a few years (more here). I completed my Masters and Bachelors in Computer Science from IIIT-Hyderabad.
Over the past few years, I've been trying to work on inter-disciplinary problems with applications in the global south. I've been fortunate enough to work on a wide range of topics, bring my expertise from computer science collaborating with sociologists, psychologists, political scientists, journalists and demographers (See publications). My work has been covered extensively in the international media.
My current research is focused on building innovative, opt-in, privacy preserving data-collection tools for platforms like WhatsApp, with the goal of developing interventions to stop/slow the spread of misinformation.
I am looking for motivated students to work with for both short and long term positions. If you are interested in any of my research, please reach out to me.
first.last @ rutgers.edu
School of Information and Communication
Rutgers University
Last modified Plain Academic