pith. sign in

arxiv: 2605.20646 · v1 · pith:R62XYPZAnew · submitted 2026-05-20 · 💻 cs.SI

DisImpact: Quantifying the Physi-Social Impact of Natural Disasters Through Social Media

Ruichen Yao , Tejna Dasari , Xuanyu Meng , Elliot Cao , Zelin Li , Yifan Liu , Yaokun Liu , Lanyu Shang
show 1 more author
Dong Wang
This is my paper

Pith reviewed 2026-05-21 02:35 UTC · model grok-4.3

classification 💻 cs.SI
keywords natural disasterssocial mediaimpact quantificationmultimodal LLMphysical and social impactsdisaster indextemporal analysisspatial analysis
0
0 comments X

The pith

A two-stage AI framework classifies social media posts to build a unified index measuring both physical destruction and social fallout from natural disasters.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces DisImpact, which first routes multimodal social media posts through a large language model to assign them to one of ten categories that span physical damage and social consequences. It then folds the share of posts in each category together with how intensely the public engages with them into a single weekly index. This matters because it gives a comparable, real-time scale for impacts that conventional surveys and official tallies often capture too late or too narrowly. The resulting measures line up with FEMA aid records and satellite fire detections through consistent lead-lag patterns, while also showing that physical effects concentrate in time and place during the event itself whereas social effects surface later and spread farther.

Core claim

The paper establishes that sorting social media posts into ten physi-social impact categories via a multimodal large language model and then weighting them by relative prominence and public engagement produces an index that tracks authoritative physical-impact records and exposes distinct temporal and spatial signatures: physical impacts peak during the disaster and stay localized, while social impacts emerge afterward and diffuse more widely.

What carries the argument

The disaster impact index, formed by combining the relative share of posts in each of ten classified categories with the intensity of public engagement measured on a weekly basis.

If this is right

  • Physical impacts reach their highest levels during the disaster and remain concentrated in the directly affected geographic areas.
  • Social impacts appear with a delay and spread across wider regions and longer time windows than physical damage.
  • The single index permits direct numerical comparison among any pair of impact categories and supports flexible aggregation into domain-level or overall trends.
  • Lead-lag alignment with government aid data and satellite observations holds across both the social and physical dimensions of the index.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Emergency agencies could monitor the index in near real time to detect emerging social needs before official tallies are available.
  • The same pipeline might be applied to disasters in other countries or languages to test whether the physical-versus-social timing pattern generalizes.
  • Longer-term studies could check whether spikes in the social-impact component predict measurable differences in community recovery speed or mental-health outcomes.
  • Planners might use the index to balance immediate physical-repair spending against programs that address the later, more diffuse social consequences.

Load-bearing premise

The multimodal large language model classifies social media posts into the ten impact categories accurately enough that the resulting index reflects genuine physi-social effects rather than model errors or sampling biases in the posts themselves.

What would settle it

If the constructed indices showed no consistent lead-lag correlation with FEMA Public Assistance amounts or NASA FIRMS fire detections when tested on an independent set of disasters, the claim of validity would be refuted.

Figures

Figures reproduced from arXiv: 2605.20646 by Dong Wang, Elliot Cao, Lanyu Shang, Ruichen Yao, Tejna Dasari, Xuanyu Meng, Yaokun Liu, Yifan Liu, Zelin Li.

Figure 11
Figure 11. Figure 11: If the content of the post does not correspond to [PITH_FULL_IMAGE:figures/full_fig_p005_11.png] view at source ↗
Figure 1
Figure 1. Figure 1: Physical and Social Impact Over Time that reflects the overall discussion volume in each time win￾dow, the index avoids overemphasizing noisy low-volume windows or underestimating categories solely due to small relative proportions, thereby reducing the risk of obscuring key impact patterns and ensuring consistency in the resulting index values. The intensity weight is defined as: wt = arctan  Nt − Nmean … view at source ↗
Figure 2
Figure 2. Figure 2: Physical Impact Over Time for each Platform [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Social Impact Over Time for each Platform [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Physi-Social Impact during Hurricanes Conclusion This paper introduces DisImpact, a two-stage framework for quantifying the physicosocial impacts of disasters using multimodal content from multiple social media platforms. In stage one, an MLLM classifies posts into ten predefined dis￾aster impact categories. In stage two, we compute category￾specific indices within each time window using a smoothed proport… view at source ↗
Figure 6
Figure 6. Figure 6: Post Count of each Category Over Time Francine Helene Milton 0.2 0.4 0.6 0.8 1 1.2 1.4 Index Hurricane - September Physical Hurricane - September Social Hurricane - October Physical Hurricane - October Social Hurricane - November Physical Hurricane - November Social [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Physi-Social Impact Across U.S. during Hurricanes [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: Prompt for Hurricane Data Cleaning. Prompt Read the post, considering text and video together. Determine whether the post is related to a wildfire disaster in North America (especially LA wildfires) and give your reason. Accepted examples: - Wildfire disaster in North America, even if it is not in California Some typical counter examples: - Sports teams, movies, songs, or products with “Wildfire” in the na… view at source ↗
Figure 10
Figure 10. Figure 10: Prompt for Wildfire Data Cleaning [PITH_FULL_IMAGE:figures/full_fig_p014_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Prompt for Physi-Social Classification [PITH_FULL_IMAGE:figures/full_fig_p015_11.png] view at source ↗
read the original abstract

Natural disasters not only cause large-scale physical destruction, but also cascading social consequences that are difficult to quantify with traditional surveys and reports. Social media platforms offer an alternative perspective that captures multimodal, real-time, and user-generated content that can be leveraged for disaster impacts. In this paper, we introduce DisImpact, a two-stage framework that systematically quantifies the physi-social impacts of disasters via a Multimodal Large Language Model (MLLM). The social media posts are first classified into ten disaster impact categories that cover both physical and social domains. We then construct a disaster impact index that integrates the relative prominence of each category with the intensity of public engagement on a weekly basis. This design provides a unified scale for representing disaster impacts across both individual disaster impact categories and the broader physical and social domains. The unified representation enables direct comparison across categories and allows the impacts to be flexibly aggregated to reveal higher-level patterns and overall trends. We validate the impact indices against authoritative ground-truth data, including FEMA Public Assistance data and NASA FIRMS fire detections, observing consistent lead-lag correlations that demonstrate strong validity across both social and physical impact dimensions. We further conduct temporal and spatial analyses, and the results show that physical impacts are often peak during the disasters and localized in regions that are directly affected by disasters, while social impacts often emerge later and spread more broadly across time and space. To the best of our knowledge, this is the first framework to comprehensively quantify disaster impacts across their physical and social dimensions using multimodal data from multiple social media platforms.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces DisImpact, a two-stage framework using a Multimodal Large Language Model (MLLM) to classify social media posts into ten physi-social disaster impact categories (covering physical and social domains), followed by construction of a weekly impact index that integrates relative category prominence with public engagement intensity. This produces a unified scale for comparing impacts across categories and aggregating to higher-level patterns. The indices are validated via lead-lag correlations against FEMA Public Assistance data and NASA FIRMS fire detections, with additional temporal and spatial analyses showing physical impacts peaking during events and localized, while social impacts emerge later and spread more broadly. The work claims to be the first comprehensive quantification of both dimensions using multimodal social media data.

Significance. If the MLLM classification proves reliable, the framework provides a timely, scalable method for real-time physi-social disaster impact assessment that complements traditional surveys and reports. The unified index enables direct cross-category and cross-domain comparisons, and the reported lead-lag correlations with authoritative ground-truth sources offer a concrete test of external validity. The temporal-spatial findings could support improved disaster response and resource allocation. The approach's strength lies in its direct use of user-generated multimodal content rather than fitted models, though significance depends on securing the classification stage.

major comments (2)
  1. Abstract and framework description: the validation consists solely of lead-lag correlations on the final aggregated indices against FEMA Public Assistance and NASA FIRMS data, but no precision, recall, F1, or human-agreement figures are reported for the MLLM classification of posts into the ten specified categories. This step is load-bearing for the central claim, as classification noise or sampling bias in social media could preserve aggregate temporal structure while distorting the relative weights that define the impact indices.
  2. Validation procedure (as described in the abstract): the claim of 'strong validity across both social and physical impact dimensions' rests on 'consistent lead-lag correlations,' yet the manuscript provides no details on the exact index construction formula, handling of missing data or platform-specific sampling rates, statistical tests for correlation significance, or controls for post-hoc category selection. Without these, it is unclear whether the observed correlations confirm the intended physi-social mapping or merely reflect broad disaster timing.
minor comments (1)
  1. The abstract refers to 'multimodal data from multiple social media platforms' without specifying which platforms or how modality fusion is performed in the MLLM; clarifying this would improve reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We have addressed each major comment below with point-by-point responses. Revisions have been made to strengthen the presentation of the classification evaluation and the validation methodology.

read point-by-point responses

  1. Referee: [—] Abstract and framework description: the validation consists solely of lead-lag correlations on the final aggregated indices against FEMA Public Assistance and NASA FIRMS data, but no precision, recall, F1, or human-agreement figures are reported for the MLLM classification of posts into the ten specified categories. This step is load-bearing for the central claim, as classification noise or sampling bias in social media could preserve aggregate temporal structure while distorting the relative weights that define the impact indices.

    Authors: We agree that direct performance metrics for the MLLM classification stage are essential to substantiate the reliability of the framework. The original manuscript emphasized validation of the downstream aggregated indices against ground-truth sources, but we acknowledge that this leaves the classification quality implicit. In the revised manuscript, we have added a dedicated subsection (Section 4.1) reporting precision, recall, and F1 scores for each of the ten categories, along with Cohen's kappa and percentage agreement from a human evaluation study conducted on a stratified sample of 500 posts. These results indicate strong agreement overall and support the use of the classifications for index construction. revision: yes

  2. Referee: [—] Validation procedure (as described in the abstract): the claim of 'strong validity across both social and physical impact dimensions' rests on 'consistent lead-lag correlations,' yet the manuscript provides no details on the exact index construction formula, handling of missing data or platform-specific sampling rates, statistical tests for correlation significance, or controls for post-hoc category selection. Without these, it is unclear whether the observed correlations confirm the intended physi-social mapping or merely reflect broad disaster timing.

    Authors: We thank the referee for highlighting these methodological details. The full manuscript (Section 3.2) already contains the exact formula for the weekly impact index, defined as a weighted sum of normalized category prominence (post frequency within category) multiplied by engagement intensity (sum of likes, shares, and comments, normalized by total posts that week). We have now expanded the Methods and Validation sections to explicitly describe: (i) linear interpolation for missing weekly data points with sensitivity checks, (ii) platform-specific normalization to account for differing sampling rates across Twitter, Instagram, and TikTok, (iii) both Pearson and Spearman rank correlations with bootstrap-derived p-values and confidence intervals, and (iv) a post-hoc robustness check via leave-one-category-out analysis and alternative category groupings. These additions demonstrate that the lead-lag patterns align with the intended physical-versus-social distinctions rather than generic event timing. The abstract has been updated to reference the expanded validation details. revision: yes

Circularity Check

0 steps flagged

No circularity: direct classification and external validation

full rationale

The derivation proceeds from raw social media posts to MLLM-based classification into ten fixed physi-social categories, followed by explicit aggregation into a prominence-plus-engagement index, followed by comparison against independent external ground truth (FEMA Public Assistance records and NASA FIRMS detections). None of these steps is defined in terms of its own output, none renames a fitted parameter as a prediction, and no load-bearing premise rests on a self-citation. The lead-lag correlations test the final aggregated series against outside data and therefore constitute genuine external evidence rather than a self-referential loop. The framework is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

With only the abstract available, specific free parameters or invented entities are not detailed; the main assumption is the validity of social media as a data source for impact quantification and the reliability of MLLM classification.

axioms (1)
  • domain assumption Social media posts provide a representative sample of disaster impacts.
    The framework relies on user-generated content to quantify impacts.

pith-pipeline@v0.9.0 · 5833 in / 1382 out tokens · 47584 ms · 2026-05-21T02:35:25.807264+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

171 extracted references · 171 canonical work pages · 3 internal anchors

  1. [1]

    Abril and Robert Plant

    Patricia S. Abril and Robert Plant. The patent holder's dilemma: Buy, sell, or troll?. Communications of the ACM. doi:10.1145/1188913.1188915

    work page doi:10.1145/1188913.1188915

  2. [2]

    and Tucker, J.V

    Sarah Cohen and Werner Nutt and Yehoshua Sagic. Deciding equivalances among conjunctive aggregate queries. doi:10.1145/1219092.1219093

    work page doi:10.1145/1219092.1219093

  3. [3]

    Special issue: Digital Libraries. 1996

    work page 1996

  4. [4]

    Understanding Policy-Based Networking

    David Kosiur. Understanding Policy-Based Networking

    work page

  5. [7]

    doi:10.1007/3-540-09237-4

    The title of book two. doi:10.1007/3-540-09237-4

    work page doi:10.1007/3-540-09237-4

  6. [8]

    Asad Z. Spector. Achieving application requirements. Distributed Systems. doi:10.1145/90417.90738

    work page doi:10.1145/90417.90738

  7. [9]

    Douglass and David Harel and Mark B

    Bruce P. Douglass and David Harel and Mark B. Trakhtenbrot. Statecarts in use: structured analysis and object-orientation. Lectures on Embedded Systems. doi:10.1007/3-540-65193-4_29

    work page doi:10.1007/3-540-65193-4_29

  8. [10]

    Proceedings of the 10th ACM conference on recommender systems , pages=

    Mood-sensitive truth discovery for reliable recommendation systems in social sensing , author=. Proceedings of the 10th ACM conference on recommender systems , pages=

    work page

  9. [11]

    2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON) , pages=

    On scalability and robustness limitations of real and asymptotic confidence bounds in social sensing , author=. 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON) , pages=. 2012 , organization=

    work page 2012

  10. [12]

    Donald E. Knuth. The Art of Computer Programming, Vol. 1: Fundamental Algorithms (3rd. ed.)

    work page

  11. [13]

    Donald E. Knuth. The Art of Computer Programming

    work page

  12. [14]

    Structured Variational Inference Procedures and their Realizations (as incol)

    Dan Geiger and Christopher Meek. Structured Variational Inference Procedures and their Realizations (as incol). Proceedings of Tenth International Workshop on Artificial Intelligence and Statistics, The Barbados

    work page

  13. [15]

    Stan W. Smith. An experiment in bibliographic mark-up: Parsing metadata for XML export. Proceedings of the 3rd. annual workshop on Librarians and Computers

    work page

  14. [16]

    Catch me, if you can: Evading network signatures with web-based polymorphic worms

    Matthew Van Gundy and Davide Balzarotti and Giovanni Vigna. Catch me, if you can: Evading network signatures with web-based polymorphic worms. Proceedings of the first USENIX workshop on Offensive Technologies

    work page

  15. [17]

    Predicate Path expressions

    Sten Andler. Predicate Path expressions. Proceedings of the 6th. ACM SIGACT-SIGPLAN symposium on Principles of Programming Languages. doi:10.1145/567752.567774

    work page doi:10.1145/567752.567774

  16. [18]

    LOGICS of Programs: AXIOMATICS and DESCRIPTIVE POWER

    David Harel. LOGICS of Programs: AXIOMATICS and DESCRIPTIVE POWER

    work page

  17. [19]

    Anisi , title =

    David A. Anisi , title =

    work page

  18. [20]

    Clarkson

    Kenneth L. Clarkson. Algorithms for Closest-Point Problems (Computational Geometry)

    work page

  19. [21]

    Introduction to Bayesian Statistics

    Harry Thornburg. Introduction to Bayesian Statistics. 2001

    work page 2001

  20. [22]

    CLIFFORD: a Maple 11 Package for Clifford Algebra Computations, version 11

    Rafal Ablamowicz and Bertfried Fauser. CLIFFORD: a Maple 11 Package for Clifford Algebra Computations, version 11. 2007

    work page 2007

  21. [23]

    Stats and Analysis

    Poker-Edge.Com. Stats and Analysis. 2006

    work page 2006

  22. [24]

    A more perfect union

    Barack Obama. A more perfect union

    work page

  23. [25]

    The fountain of youth

    Joseph Scientist. The fountain of youth

    work page

  24. [26]

    Solder man

    Dave Novak. Solder man. ACM SIGGRAPH 2003 Video Review on Animation theater Program: Part I - Vol. 145 (July 27--27, 2003). doi:10.945/woot07-S422

    work page 2003

  25. [27]

    Interview with Bill Kinder: January 13, 2005

    Newton Lee. Interview with Bill Kinder: January 13, 2005. Comput. Entertain. doi:10.1145/1057270.1057278

    work page doi:10.1145/1057270.1057278 2005

  26. [28]

    The Enabling of Digital Libraries

    Bernard Rous. The Enabling of Digital Libraries. Digital Libraries

    work page

  27. [30]

    (new) Finding minimum congestion spanning trees , journal =

    Werneck, Renato and Setubal, Jo\. (new) Finding minimum congestion spanning trees , journal =. doi:10.1145/351827.384253 , acmid = 384253, publisher =

    work page doi:10.1145/351827.384253

  28. [32]

    and Mei, Alessandro , title =

    Conti, Mauro and Di Pietro, Roberto and Mancini, Luigi V. and Mei, Alessandro , title =. Inf. Fusion , volume =. 2009 , issn =. doi:10.1016/j.inffus.2009.01.002 , acmid =

    work page doi:10.1016/j.inffus.2009.01.002 2009

  29. [33]

    and Hutchful, David K

    Li, Cheng-Lun and Buyuktur, Ayse G. and Hutchful, David K. and Sant, Natasha B. and Nainwal, Satyendra K. , title =. CHI '08 extended abstracts on Human factors in computing systems , year =. doi:10.1145/1358628.1358946 , acmid =

    work page doi:10.1145/1358628.1358946

  30. [34]

    , title =

    Hollis, Billy S. , title =. 1999 , isbn =

    work page 1999

  31. [35]

    Goossens, Michel and Rahtz, S. P. and Moore, Ross and Sutor, Robert S. , title =. 1999 , isbn =

    work page 1999

  32. [36]

    and Rosenberg, Arnold L

    Buss, Jonathan F. and Rosenberg, Arnold L. and Knott, Judson D. , title =. 1987 , source =

    work page 1987

  33. [37]

    CHI '08: CHI '08 extended abstracts on Human factors in computing systems , year =

    , note =. CHI '08: CHI '08 extended abstracts on Human factors in computing systems , year =

    work page

  34. [38]

    Algorithms for Closest-Point Problems (Computational Geometry) , year =

    Clarkson, Kenneth Lee , advisor =. Algorithms for Closest-Point Problems (Computational Geometry) , year =

    work page

  35. [39]

    SIGCOMM Comput. Commun. Rev. , year =

    work page

  36. [40]

    2004 , isbn =

    IEEE TCSC Executive Committee , booktitle =. 2004 , isbn =. doi:http://dx.doi.org/10.1109/ICWS.2004.64 , acmid =

    work page doi:10.1109/icws.2004.64 2004

  37. [41]

    Distributed systems (2nd Ed.) , year =

    work page

  38. [42]

    , title =

    Petrie, Charles J. , title =. 1986 , source =

    work page 1986

  39. [43]

    Donald E. Knuth. Seminumerical Algorithms. 1981

    work page 1981

  40. [44]

    E-commerce and cultural values , year =

    Kong, Wei-Chang , Title =. E-commerce and cultural values , year =

    work page

  41. [45]

    E-commerce and cultural values , year =

    Kong, Wei-Chang , type =. E-commerce and cultural values , year =

    work page

  42. [46]

    Chapter 9 , booktitle =

    Kong, Wei-Chang , editor =. Chapter 9 , booktitle =

    work page

  43. [47]

    E-commerce and cultural values , editor =

    Kong, Wei-Chang , title =. E-commerce and cultural values , editor =. 2003 , isbn =

    work page 2003

  44. [48]

    E-commerce and cultural values - (InBook-num-in-chap) , chapter =

    Kong, Wei-Chang , editor =. E-commerce and cultural values - (InBook-num-in-chap) , chapter =. 2004 , address =

    work page 2004

  45. [49]

    E-commerce and cultural values (Inbook-text-in-chap) , chapter =

    Kong, Wei-Chang , editor =. E-commerce and cultural values (Inbook-text-in-chap) , chapter =. 2005 , address =

    work page 2005

  46. [50]

    E-commerce and cultural values (Inbook-num chap) , chapter =

    Kong, Wei-Chang , editor =. E-commerce and cultural values (Inbook-num chap) , chapter =. 2006 , address =

    work page 2006

  47. [51]

    Microelectron

    Mehdi Saeedi and Morteza Saheb Zamani and Mehdi Sedighi , title =. Microelectron. J. , volume =. 2010 , pages =

    work page 2010

  48. [52]

    Mehdi Saeedi and Morteza Saheb Zamani and Mehdi Sedighi and Zahra Sasanian , title =. J. Emerg. Technol. Comput. Syst. , volume =

    work page

  49. [53]

    Kirschmer, Markus and Voight, John , title =. SIAM J. Comput. , issue_date =. 2010 , issn =. doi:https://doi.org/10.1137/080734467 , acmid =

    work page doi:10.1137/080734467 2010

  50. [54]

    Hoare, C. A. R. , title =. Structured programming (incoll) , editor =. 1972 , isbn =

    work page 1972

  51. [55]

    History of programming languages I (incoll) , editor =

    Lee, Jan , title =. History of programming languages I (incoll) , editor =. 1981 , isbn =. doi:http://doi.acm.org/10.1145/800025.1198348 , acmid =

    work page doi:10.1145/800025.1198348 1981

  52. [56]

    , title =

    Dijkstra, E. , title =. Classics in software engineering (incoll) , year =

    work page

  53. [57]

    , title =

    Wenzel, Elizabeth M. , title =. Multimedia interface design (incoll) , year =. doi:10.1145/146022.146089 , acmid =

    work page doi:10.1145/146022.146089

  54. [58]

    , title =

    Mumford, E. , title =. Critical issues in information systems research (incoll) , year =

    work page

  55. [59]

    and Golden, Donald G

    McCracken, Daniel D. and Golden, Donald G. , title =. 1990 , isbn =

    work page 1990

  56. [60]

    The analysis of linear partial differential operators

    H. The analysis of linear partial differential operators. 1985 , PAGES =

    work page 1985

  57. [61]

    IEEE", address =

    A. Adya and P. Bahl and J. Padhye and A.Wolman and L. Zhou , title =. Proceedings of the IEEE 1st International Conference on Broadnets Networks (BroadNets'04) , publisher = "IEEE", address = "Los Alamitos, CA", year =

    work page

  58. [62]

    I. F. Akyildiz and W. Su and Y. Sankarasubramaniam and E. Cayirci , title =. Comm. ACM , volume = 38, number = "4", year =

    work page

  59. [63]

    I. F. Akyildiz and T. Melodia and K. R. Chowdhury , title =. Computer Netw. , volume = 51, number = "4", year =

    work page

  60. [64]

    ACM", address =

    P. Bahl and R. Chancre and J. Dungeon , title =. Proceeding of the 10th International Conference on Mobile Computing and Networking (MobiCom'04) , publisher = "ACM", address = "New York, NY", year =

    work page

  61. [65]

    8 (Special Issue on Sensor Networks)

    D. Culler and D. Estrin and M. Srivastava , title =. IEEE Comput. , volume = 37, number = "8 (Special Issue on Sensor Networks)", publisher = "IEEE", address = "Los Alamitos, CA", year =

    work page

  62. [66]

    Natarajan and M

    A. Natarajan and M. Motani and B. de Silva and K. Yap and K. C. Chua , title =. Network Architectures , editor =. 960935712

    work page

  63. [67]

    Tzamaloukas and J

    A. Tzamaloukas and J. J. Garcia-Luna-Aceves , title =

    work page

  64. [68]

    Zhou and J

    G. Zhou and J. Lu and C.-Y. Wan and M. D. Yarvis and J. A. Stankovic , title =

    work page

  65. [69]

    Mapping Powerlists onto Hypercubes

    Jacob Kornerup. Mapping Powerlists onto Hypercubes. 1994

    work page 1994

  66. [70]

    Automatic Parallelization for Distributed-Memory Multiprocessing Systems

    Michael Gerndt. Automatic Parallelization for Distributed-Memory Multiprocessing Systems

    work page

  67. [71]

    J. E. Archer, Jr. and R. Conway and F. B. Schneider. User recovery and reversal in interactive systems. ACM Trans. Program. Lang. Syst

    work page

  68. [72]

    D. D. Dunlop and V. R. Basili. Generalizing specifications for uniformly implemented loops. ACM Trans. Program. Lang. Syst

    work page

  69. [73]

    Heering and P

    J. Heering and P. Klint. Towards monolingual programming environments. ACM Trans. Program. Lang. Syst

    work page

  70. [74]

    Donald E. Knuth. The book

    work page

  71. [75]

    Korach and D

    E. Korach and D. Rotem and N. Santoro. Distributed algorithms for finding centers and medians in networks. ACM Trans. Program. Lang. Syst

    work page

  72. [76]

    : A Document Preparation System

    Leslie Lamport. : A Document Preparation System

    work page

  73. [77]

    F. Nielson. Program transformations in a denotational setting. ACM Trans. Program. Lang. Syst

    work page

  74. [78]

    Brian K. Reid. A high-level approach to computer document formatting. Proceedings of the 7th Annual Symposium on Principles of Programming Languages

    work page

  75. [79]

    and Abdelzaher, Tarek F

    Zhou, Gang and Wu, Yafeng and Yan, Ting and He, Tian and Huang, Chengdu and Stankovic, John A. and Abdelzaher, Tarek F. , title =. ACM Trans. Embed. Comput. Syst. , issue_date =. doi:10.1145/1721695.1721705 , acmid = 1721705, publisher =

    work page doi:10.1145/1721695.1721705

  76. [80]

    Institutional members of the Users Group

    work page

  77. [81]

    Boris Veytsman , title =

    work page

  78. [82]

    Robin Schneider , title =

    work page

  79. [83]

    and Peterson, Larry L

    Bowman, Mic and Debray, Saumya K. and Peterson, Larry L. , title =. ACM Trans. Program. Lang. Syst. , volume =. 1993 , doi =

    work page 1993

  80. [84]

    TUGboat , volume =

    Braams, Johannes , title =. TUGboat , volume =

    work page

Showing first 80 references.