We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

https://cmi.ac.in/~madhavan/courses/verification-2011/alur-dill-tcs-94.pdf

A theory of timed automata

The 11th edition of Harrison's Principles of Internal Medicine welcomes Anthony Fauci to its editorial staff, in addition to more than 85 new contributors. While the organization of the book is similar to previous editions, major emphasis has been placed on disorders that affect multiple organ systems. Important advances in genetics, immunology, and oncology are emphasized. Many chapters of the book have been rewritten and describe major advances in internal medicine. Subjects that received only a paragraph or two of attention in previous editions are now covered in entire chapters. Among the chapters that have been extensively revised are the chapters on infections in the compromised host, on skin rashes in infections, on many of the viral infections, including cytomegalovirus and Epstein-Barr virus, on sexually transmitted diseases, on diabetes mellitus, on disorders of bone and mineral metabolism, and on lymphadenopathy and splenomegaly. The major revisions in these chapters and many

Harrison's Principles of Internal Medicine

The concept of an agent has become important in both Artificial Intelligence (AI) and mainstream computer science. Our aim in this paper is to point the reader at what we perceive to be the most important theoretical and practical issues associated with the design and construction of intelligent agents. For convenience, we divide these issues into three areas (though as the reader will see, the divisions are at times somewhat arbitrary). Agent theory is concerned with the question of what an agent is, and the use of mathematical formalisms for representing and reasoning about the properties of agents. Agent architectures can be thought of as software engineering models of agents;researchers in this area are primarily concerned with the problem of designing software or hardware systems that will satisfy the properties specified by agent theorists. Finally, agent languages are software systems for programming and experimenting with agents; these languages may embody principles proposed by theorists. The paper is not intended to serve as a tutorial introduction to all the issues mentioned; we hope instead simply to identify the most important issues, and point to work that elaborates on them. The article includes a short review of current and potential applications of agent technology.

/pdf/intelligent-agents-theory-and-practice-5dchj7p8pc.pdf

Intelligent Agents: Theory and Practice

/pdf/convex-analysisnoer-san-nojin-zhan-nituite-5dl2rbmoyr.pdf

Convex Analysisの二,三の進展について

T HE 2015 GRACE HOPPER Celebration of Women in Computing (GHC, for short) will take place October 14–16 in Houston, TX. GHC is an annual conference designed to bring the research and career interests of women in computing to the forefront. It is the world’s largest gathering of women in computing. GHC is organized by the Anita Borg Institute for Women in Technology in partnership with ACM. This year’s event is expected to bring together more than 12,000—mostly female—computer scientists! But this impressive number should not be taken to mean all is well on the gender-diversity front. Far from it! According to the most recent Taulbee Survey (covering academic year 2013–2014), conducted by the Computing Research Association in North America, only 14.7% of CS bachelor’s degrees went to women. The U.S. Department of Education’s data shows the female participation level in computing peaked at about 35% in 1984, more than twice as high as it is today. The low participation of women in computer science has been, indeed, a matter of concern for many years. The Anita Borg Institute was founded in 1997 “to recruit, retain, and advance women in technology.” (GHC is the Institute’s most prominent program.) The National Center for Women & Information Technology, founded in 2004, is another organization that works to increase the meaningful participation of girls and women in computing. And yet, we seem to be regressing rather than progressing on this issue. The gender-diversity issue received a fair amount of attention over the past year, when several major technology companies released workforce-diversity data, showing, no surprise, a significant underrepresentation of women in technical jobs. Tech companies point, of course, to the narrow pipeline of women with computing degrees to explain this underrepresentation, but the culture inside some of these companies also seems to be a major factor. In fact, the male-dominated tech culture gave rise to the phrase “brogramming,” a slang term used to refer to computer code produced by “bros” (slang for male friends). A magazine article on the subject, titled: “Brogramming—The Disturbing Rise of Frat Culture in Silicon Valley,” was circulated widely a few years ago. But amid the deluge of bad news, one can find some points of light. Carnegie Mellon University decided in the late 1990s to take decisive action on gender diversity and was able to increase the percentage of women entering its computer science program to 40%. A similar outcome was recently reported by Harvey Mudd College. The Anita Borg Institute, together with Harvey Mudd College, launched the BRAID Initiative (http://anitaborg. org/braid-building-recruiting-andinclusion-for-diversity/) in 2014 to increase the percentage of women and students of color majoring in computer science in the U.S. At my own institution, Rice University, we were able to raise the percentage of declared female majors (Rice students declare their major toward the end of the second year of study) from 14% in 2007 to 30% in 2014. What distinguishes Rice from Carnegie Mellon and Harvey Mudd is that computer science at Rice has no control whatsoever of the undergraduate-admission pipeline. To raise the level of participation of women in computer science at Rice required a departmental decision that we cannot simply blame the situation on the narrow pipeline of female high school graduates with interest in CS. Several measures were adopted: ˲ Changing CS1 from a course about programming techniques to a course about computational thinking. The latter course is more popular with both male and female students, and also puts students with widely varied high school computing experiences on a more level playing field. ˲ Creating a club for female computer science students. There are a fair number of female students who desire the camaraderie of an all-women computing group on campus, given that the CS student body is still very much male dominated. ˲ Having faculty members, male and female, develop mentoring relationships with female students to motivate and encourage them, including offering opportunities for interaction beyond the classroom, for example, undergraduate research opportunities. ˲ Continually dispel myths about the preparedness and ability of women for technical jobs. ˲ Last, but not least, sending female students to GHC. Especially given Rice’s small size, this allows students to see there are many successful women in the field. The bottom line is that while the gender-diversity problem is a very challenging one, it is not hopeless. Indeed, the pipeline is narrow, but it can be expanded, one student at a time, one program at a time, one company at a time. Institutional and personal commitments can make a significant difference! Follow me on Facebook, Google+, and Twitter.

What can be done about gender diversity in computing?: a lot!

a few years by then, driven by offshoring and outsourcing of IT services. Alarming reports by consulting firms predicted the migration of millions of jobs. The political response to the news was quite shrill, referring to “Benedict Arnold CEOs.” Grim jokes circulated about the U.S. offshoring the President’s job. Parents and students became convinced the IT field had no future. U.S. computing enrollments, already hammered by the dot-com and telecomm crashes, took another dive. In response to these concerns about the viability of computing as a field of study and work in developed countries, ACM Council commissioned in 2004 a Task Force to “look at the facts behind the rapid globalization of IT and the migration of jobs resulting from outsourcing and offshoring.” The Task Force, co-chaired by Frank Mayadas and myself, with the assistance of William Aspray as Editor, was convened in late 2004 and issued its report in February 2006. See http://www.acm.org/ globalizationreport/ Do the insights produced by the report still ring true four years and a major economic crisis later? To me, the most fundamental insight is that offshoring is just a symptom; the underlying phenomenon is the globalization of computing. Globalization is driven by trade policies, by the evolution of work and business processes, and by IT itself. The dramatic drop in communication costs means that labor can now be viewed as a tradeable good, just as containerization and air freight reduced shipping costs and increased global trade. There is no doubt that many people in developed countries lost their jobs to offshoring. Indeed, some categories of jobs, for example, call centers, have migrated more than others. Yet, there is no evidence that offshoring had a measurable impact on aggregate IT employment in developed countries. U.S. employment and wage data showed complete recovery from the dot-com and telecomm crashes by 2005. The report pointed out that once we ignore the many biased and hyped reports and concentrate on concrete data, there is no reason to believe that IT jobs are migrating away. After the report’s release, many people asked me for a “one-bit answer”: Is offshoring good or bad? Of course, this is not a meaningful question. Good for whom? Undoubtedly, trade contributes to economic growth and greater wealth. Offshoring pulled tens of millions of people out of poverty in India, and that is surely a good thing. But economists admit that “Trade gains may be distributed differentially,” meaning some individuals gain and some lose; some localities gain and some lose. Globalization leads to enhanced competition, and technology leaders risk losing their current dominant position. IT is now a truly global field, business, and industry. Between 1980 and 2000, the market share of the top-five software firms dropped almost 30%. The bottom line, therefore, is one of a thriving computing field, with projections of continued growth for years to come. The difference is the field is now global, and so is the competition. Such competition is moving up the skill ladder. Companies, including start-ups, are learning how to access and use higher skill levels in developed countries. We are seeing new research labs and increasing national research investment in India and China. We are also seeing an increase in the total worldwide investment in research and a wider distribution of research activities around the world. These are all signs of a thriving global field, with intense global competition. Can offshoring be ignored? Absolutely not! We must continue monitoring employment and wage trends, as well as global R&D trends, to assess its ongoing impact. To stay competitive in a global IT environment and industry, countries must adopt policies that invest in R&D and foster innovation. Policies that improve a country’s ability to attract, educate, and retain the best IT talent are critical. Education is a primary means for both developed and developing countries to mount a response to offshoring so their work forces can compete globally for IT jobs. So how do countries and individuals thrive in a global computing world? Invest, innovate, develop, produce, educate, compete. Simple.

Globalization and Offshoring of Software revisited

There has been sound and fury in the Open Access movement over the last few months. provisions to prohibit open-access mandates for federally funded research , effectively nullifying the National Institutes of Health's policy that requires taxpayer-funded research to be freely accessible online. Many scholarly publishers, including the Association of American Publishers (AAP), expressed support for the bill. The reaction to the bill and its support by scholarly publishers has been one of sheer outrage, with headlines such as \" Academic Publishers Have Become the Enemies of Science. \" On Jan-uary 21, 2012, renowned British mathematician Timothy Gowers declared a boycott on Elsevier, a major scholarly publisher, in a detailed blog posting. The boycott movement then took off, with over 8,000 scientists having joined it so far. By the end of February, Else-vier had withdrawn its support of RWA, and its sponsors in U.S. House of Representatives announced that RWA \" has exhausted the useful role it can play in the debate. \" Now that things have quieted down a bit, we can revisit the arguments for open access, which I discussed first in July 2009, in \" Open, Closed, or Clopen Access? \" The basic question I would like to address again is what ACM's stance should be with respect to open-access publishing models. (As pointed out in the July 2009 letter, ACM is a Green-Open-Access publisher.) The arguments in favor of open access have evolved somewhat since the movement emerged about a decade ago. Early arguments talked about broad dissemination of information, under the slogan \" information wants to be free. \" Recent arguments have focused on fairness, as expressed in the headline \" Congress Considers Pay-walling Science You Already Paid For. \" But one should not conflate the cost of carrying out research with the cost of publishing the results. Furthermore, universities and academic researchers profit from intellectual property developed under federal funding, and I see no outrage about that. The real issue seems to me to be the divergence of interests between authors and publishers. Authors of scholarly articles would like to see them disseminated as broadly as possible, while publishers would like to cover the costs of publishing and, possibly, make some profit. This is a true divergence of interests. When it comes to commercial publishers, this divergence, in my opinion, simply cannot be bridged, which explains the outrage directed at Elsevier. As I said …

Fair access

Many systems are naturally modeled as Markov Decision Processes (MDPs), combining probabilities and strategic actions. Given a model of a system as an MDP and some logical specification of system behavior, the goal of synthesis is to find a policy that maximizes the probability of achieving this behavior. A popular choice for defining behaviors is Linear Temporal Logic (LTL). Policy synthesis on MDPs for properties specified in LTL has been well studied. LTL, however, is defined over infinite traces, while many properties of interest are inherently finite. Linear Temporal Logic over finite traces (LTLf) has been used to express such properties, but no tools exist to solve policy synthesis for MDP behaviors given finite-trace properties. We present two algorithms for solving this synthesis problem: the first via reduction of LTLf to LTL and the second using native tools for LTLf. We compare the scalability of these two approaches for synthesis and show that the native approach offers better scalability compared to existing automaton generation tools for LTL.

LTLf Synthesis on Probabilistic Systems.

The discussion of the relative merits of linear- versus
branching-time frameworks goes back to the early 1980s. 
We revisit here this issue from a fresh perspective and postulate three
principles that we view as fundamental to any discussion of process
equivalence. We show that our principles yield a unique notion of 
process equivalence, which is trace based, rather than tree based.

Moshe Y. Vardi

Papers

What can be done about gender diversity in computing?: a lot!

Globalization and Offshoring of Software revisited

Fair access

LTLf Synthesis on Probabilistic Systems.

Branching vs. Linear Time: Semantical Perspective