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My father fell ill and could not con- in elementary school reading a Super- The clerk helped me select the right tinue to run his grocery store, so I re- man comic whose centerfold showed part. Oh, the magic of listening to mu- alized I could not attend college in how to build a crystal radio out of sic from my newly built radio; and it day session and had no choice but to household items that one could find required no battery or power at all.

Af- go to night school and bring home a on the street: a razor blade, some pen- ter that, I kept cannibalizing old radios salary by working full time during the cil lead, a toilet paper roll, and an ear- and used the parts to design new radios day. That was a big blow. My father phone which I stole from the tele- that I put together. My mother never took me to an electronics firm where phone booth in the candy store down got in my way and allowed me a place I could get a job serving as an elec- the street.

I also needed a variable ca- behind our sofa to make a mess and to tronics technician and eventually as pacitor and had no clue what that was, do my tinkering. I worked point I would be working full time at at night. My day work was, however, in a group at Lincoln run by Wes MIT Lincoln Lab and we needed the wonderfully interesting: we were in- Clark who built arguably one of the money since my wife would have to volved in designing and using photo- first PCs the Linc computer. So care for our newborn.

So, I was not at electric devices in many applications. But The people in night school were an Lincoln; and of course MIT profes- Frank Reintjes was insistent and, interesting bunch—after all, who at- sors would often visit.

Yet, my mas- accepted the Ph. Two mined. I made use of founders of the Internet, see later— ters. I remember a professor bring- the Kerr magneto-optic effect where- Ed.

Turing Award recipient—Ed. As a result, one could eliminate the temperature-dependent use polarized light to non-destruc- What were the first years of your MIT variations. My Our Ph. My MS at and insight behind theory. Claude The thesis involved experiments and MIT made it easier since the qualify- Shannon, who was then—and still is— models and I even constructed a spe- ing exam was largely based on the my role model, similarly had great in- cial digital logic using light bouncing MIT MS curriculum, but full of trick sight and physical intuition into why off a sequence of thin films.

My thesis questions. Interestingly Ivan Suther- things happened alongside his mathe- must have impressed my MS supervi- land came in directly from Caltech matical approach to problems. How did you go to But the idea was that after a Lincoln to study; he is one heck of a smart guy. MIT instead? And Ph. Our first child was MIT, and so called up Claude Shan- would provide considerable financial non founder of information theory. I remember the Ed.

Shannon wanted me sitting at the back. That I with each other. MIT as a Lincoln Labs associate? I was looking for a fresh field to work My first supervisor at Lincoln was on. It seemed to me that even Shannon. It seemed to me that the changing messages—whose size and problems that remained seemed I taught while at MIT. And chess a probability distribution—across a was not my forte. At the same time, be- network of what we would call routers ing surrounded by computers at MIT today.

Networks of steady determinis- and at Lincoln Lab, it seemed inevita- tic traffic flows had been studied for ble to me they would eventually need example, Max-flow Min-cut theorems to communicate with each other and had just appeared and one node sys- the existing telephone network was control theory pioneer were part of tems with stochastic arrivals had been woefully inadequate for such a chal- the same suite with me.

This lab well studied queueing theory. How- lenge. I remember the stimulating combination of those two, and this led thurs by the way one other student of conversations in which we engaged to stochastic networks. This was a very Arthurs was Irwin Jacobs of Qual- and taught each other our very differ- hard problem to solve analytically, comm fame. Arthurs mentioned a ent fields information theory, control and to this day, the exact analysis is classified project he had encountered theory, networking, and so forth and still intractable.

However, I was able for a network between computers. How did that happen? Erlang in the early of slow circuit switching, was not up s, then taken up by the mathema- You were inducted into the Internet to the job. Yet queueing systems models networks, the technology underpin- a demand request when that demand had all the ingredients of a mathe- ning the Internet. This concept is manifest to- since it dealt with ways of analyzing sue was how to support bursty traffic day in so many systems for example, throughput, response time, buffer in a data network, it became clear Uber, AirBnB, seats on an airplane, size, efficiency, and so forth.

Further, that the mathematics needed to rep- and so forth in what we often refer to and importantly, it was a perfect resent the network had to be based as a shared economy. I tried to remedy that later by writ- There were at least two other inde- were especially interesting since, as ing a two-volume textbook called pendent threads that were nearly systems scale up, emergent properties Queueing Systems, which was queueing concurrent.

Tell us that was published in a book. He also pro- mation theory pioneer and inventor of enced many others because of its posed distributed network topolo- Lempel-Ziv coding and Tom Kailath clarity and strong intuition. His work albeit less optimal, protocol where the was classified and so I did not see his dynamics of the network were reflect- papers until my thesis was complet- While we think of the ed in which links of interest out of a ed.

His work was right on! Another Internet today to send node were idle, indicating that the important thread was from Donald route to the destination using a Davies who was working indepen- email and support queued link was not desirable and dently at the National Physical Labo- social networks, that the uncongested links leading ratory in England and who realized out of a node to that destination were that the packet switching was good the motivation then currently better choices.

Larry Roberts, Ivan Sutherland, than were small packets; hence he and I were very close friends and did suggested packets of approximately our thesis defenses at the same time bytes, which was later used in the because we had all heavily used the ARPANET design.

The union vantage of allowing small messages to of our three Ph. These include techniques including Claude Shannon, who was this trade-off mathematically in my such as priority queueing for example on each of our committees, Marvin dissertation years earlier. Is there any- members. The projects were very would reduce delays in overall trans- thing else you want to mention before different; we were just all using the mission.

Further, he noted that the we move on from your thesis? Ivan did this great work on ability to pipeline packets reduced la- Some other aspects include the ef- Sketchpad, Larry did his on machine tency through the network.

I showed for the first perception of three-dimensional ob- time that in terms of performance, a jects, and I did mine on communica- So in some ways, Paul, Donald, and single link of capacity C is better than tion networks.

Paul fo- an example of the second principle I verify the accuracy of my mathemati- cused on routing resiliency, Donald on mentioned previously. I investigated cal approximations. How models. Is that accurate? I investigated distributed adaptive coln Labs? But issues whereas I was more focused on dered sequence of favorable routes they were remarkably generous and extracting the underlying principles for each destination and use the first offered to have me look around the and developing a mathematical theory route that was not congested locally.

Among the princi- see what opportunities were there. I ples were: dynamic resource sharing is Interesting! And porting lots of traffic are far more effi- to compute shortest paths. However, then there were academic offers, in- cient than small resources supporting that technique takes longer to respond cluding the one from UCLA for a ten- less traffic; and distributed adaptive to failure. Some networks today for ex- ure-track position at half the salary control is efficient, stable, robust, ample, MPLS protection use your idea I would get at Lincoln.

But I had fault-tolerant, and it works. So with the lyzes other benefits of packet switch- jkstra-type updating procedure dy- West Coast weather, the Wild West ing we rely on today that are comple- namically based on networkwide appeal, and a university position, I mentary to those pointed out by shortest paths.

I introduced a simpler, drove my family all the way across. The need for a more advanced am still here! On October 29, ed the contract.

My role in this ARPANET project mercial backbones who had to In my software team, besides Char- was performance evaluation, design, cooperate, and so forth, are all well ley there was Steve Crocker who head- experimentation, and measurement.

It was natural Tomlinson introduced email in Internet luminaries. The backstory that we would be the first node so that and it very quickly took over a major starts with Ivan Sutherland who be- we could begin to conduct experimen- portion of the traffic; that was when came head of IPTO for ARPA in Put another way, we identical IBM s on campus.

In- to share their computers, so that net- notations that go along with the Cre- deed, it has been the case over and work was never implemented. Was this deliberate? We were trying to send the nity has been surprised each time ma- How did it finally happen? Bob convinced Larry to the Internet we know today? While we that the ARPANET would shortly be ternet we did not foresee was the think of the Internet today to send emergence of the dark side in all its email and support social networks, manifestations that plagues us today.

Tell us more My first money on this crazy idea. Most of single-node packet switch and in- my students who followed concen- corporated their ideas in the ARPA- trated on various performance anal- NET design. Mario Ger- tunities for future generations of re- la provided optimal routing design I fear the power searchers.

I think it would be interest- and provided an effective protocol. Facebook grew so popular while oth- is, starting to forward a packet as ers failed so that we can try to be bet- soon as the router read the destina- ter at prediction. Perhaps a lens based tion address, thereby reducing laten- on behavioral economics of the sort cy, which is pervasive today in Local pioneered by Nobel Prize winning Area Networks. Simon Lam and Fouad Tobagi literally Los Angeles and metaphori- I am concerned about the Bal- initiated the analysis and design of cally the Internet was the new fron- kanization of the Internet as nation- wireless networking and provided the tier, the Wild West of communica- states cut off and censor Internet early analysis of Slotted Aloha Lam tion.

And so on. I fear the power of the Inter- What advice do you have for young re- net is being lost. I realize this is partly And the triumvirate: Gerla, Tobagi, searchers? For example, areas that are Mario and I worked on network de- in need of innovation, research, and You have had a remarkable career of sign techniques that went beyond my development include IoT, distributed academic excellence network perfor- thesis while Howie Frank and Ivan ledgers, the introduction of biologi- mance evaluation clearly begun with Frisch were concurrently working on cally inspired principles to network- your thesis and your later work with different techniques at Network Analy- ing and engineering in general , dis- your UCLA students , real-world impact sis corporation.

Mario went to work for tributed intelligent systems, advanced you were heavily involved in the evolu- Howie Frank but we later hired him network architectures, network secu- tion of the Internet including helping back at UCLA.

The space is write the famous Gore proposal , and of the satellite packet switching meet- awash with great problems to dive entrepreneurship you have started sev- ings we had where he was my right- into. In the case of distributed led- eral companies including Linkabit hand man. Out of that came his disser- gers, the technology that underlies with Jacobs and Viterbi, which later tation on the analysis of the instability bitcoin, I am excited on the one hand, led to Qualcomm and Nomadix an of Aloha that Abramson had created in but concerned on the other hand.

What Hawaii. The application was technologies soon after its birth, thus of your work. It is not enough to evalu- to foot soldiers or possibly tanks mov- distorting its path to proper maturity; ate your ideas. You need to keep think- ing across a battlefield; that led to the this is because profit-seeking compa- ing about them to distill principles be- SURAN survival radio network, and the nies and speculators jumped on the fore moving on to the next big thing.

Tobagi bandwagon right away, which may Second, try to bounce ideas among and I started studying CSMA—carrier lead to brittle designs. By contrast, we brilliant people. I have had the fortune sense multiple access—which eventu- had 20 years without commercial in- of doing so with folks like Shannon, ally contributed to Ethernet. Third, aspire like Shan- dustry in both network design pop- helped, I believe, to make the Internet non to combine physical intuition with ular in the s and to media ac- more robust.

While I have cess schemes which continue to be done a fair amount of mathematical popular today because of My and WiFi. As I said before, it is hard to pre- early building of a crystal radio re- That early work caught the atten- dict. We missed the social networking mains a watershed event in my life. So, in some continued studying the behavior of sense, we have created an Internet George Varghese varghese cs.

We were fortu- that is destined to continually sur- USA. Viewpoint Algorithms, Platforms, and Ethnic Bias How computing platforms and algorithms can potentially either reinforce or identify and address ethnic biases.

Al- cause their actions affect outcomes. As gorithms already sift through expand- algorithms that shown in the accompanying figure, we ing datasets to provide credit ratings, make or structure identify nine potential biases. They are serve personalized advertisements, not mutually exclusive, as it is possible match individuals on dating sites, real-world decisions.

In principle, riety of other tasks. Digital platforms occur in an algorithmic process. Algorithms and on- ognized. Some attribute the introduc- tain bias. For example, if the criminal line platforms are not neutral; they are tion of bias into algorithms to the fact justice system is biased, then, absent built to frame and drive actions.

Thus, training sets can be with specific hypotheses about the fairness. Algorith- things. As techniques such as machine expressions. For instance, the exclusion For engineers and policymakers alike, identifying and addressing the sources of gender or race in a health diagnos- understanding how and where bias of bias—conscious or unconscious— tic algorithm can lead to inaccurate or can occur in algorithmic processes can that might infect algorithms. What is even harmful conclusions.

However, help address it. Our contribution is the certain is that without proper mitiga- the inclusion of gender, race, or even introduction of a visual model see the tion, preexisting societal bias will be ZIP codes in a sentencing algorithm. Input Algorithm Output Users 1.

Training Data Bias 3. Algorithmic Processing Bias 4. Transfer Context Bias 7. Automation Bias 2. Algorithm Focus Bias 5. Interpretation Bias 8.

Consumer Bias 6. Outcome Non-Transparency Bias. Feedback loop bias. Source: The first six biases were adapted from Danks, D. The visualization and remaining materials are by Silva and Kenney. This is the Algorithms, particularly artificial intel- bias can occur from either side, or party, conundrum: in certain cases, such ligence and machine learning, often in a digital interaction.

Or, even more variables must intentionally be used to generate opaque results. The reasons deliberately, anonymous online hack- produce less-biased outcomes.

For example, when a ma- for only a few days in , to respond One source of such bias is the inclusion chine-learning program recommends with racially objectionable statements. The which to express their biases. Algorithmic sys- ticular Japanese manga site. For example, embodied in job-candidate-sorting soft- to identify discriminatory outcomes or the Google Search algorithm responds ware, then this seemingly innocuous even the reasons for the outcome. Automation bias customized input for subsequent candidates.

Effectively, a desired proxy results from the belief the output is searches. The algorithm learns from trait inadvertently excludes certain fact, rather than a prediction with a con- user behavior.

For example, in predic- groups that could perform the job. For instance, credit deci- tive policing, the algorithm relies al- Transfer Context Bias. Transfer con- sions are now fully automated and use most entirely on historical crime data. One example is using people lower scores and limits their vent crime.

Not surprisingly, increased credit scores to make hiring decisions. Credit denial means police presence leads to higher crime Bad credit is equated with inferior future their scores cannot improve.

Often, the detection, thereby raising the statisti- job performance, despite little evidence subjects and decision-makers are un- cal crime rate. If the undesirable, but ir- and uncritically accept the decisions. In another example, Google then it might lead to biased outcomes. Interpretation tection by limiting automated algorith- websites are often selected and there- bias arises when users interpret algo- mic decision processes for legal or the fore recommend them more often, rithmic outputs according to their in- equivalent life-affecting decisions.

As smart as ternalized biases. For example, a judge Consumer Bias. The biases that hu- algorithms can be, human monitoring can receive an algorithmically generat- man beings act upon in everyday life are continues to be necessary.

Fur- cide on the punishment or bail amount ther, digital platforms can exacerbate or Benefits of Platforms for the defendant. Because individual give expression to latent bias in online and Algorithms judges may be unconsciously biased, behavior.

Consumer bias. It is well known that members of. For example, Kleinberg bias are too pessimistic. Digital pro- et al. In cerated while also reducing the rate the digital world, the data captured is of reoffending. Although high number of people in certain racial digital technologies have the potential groups are incarcerated.

The point is to reinforce old biases with new tools, that it is possible to design algorithms they can also help identify and monitor with different social goals. Critics ig- progress in addressing ethnic bias.

Public Policy Council: Statement on Algorithmic efficiency and effectiveness. Transparency and Accountability. Ananny, M. New Media outcomes. To illustrate, sales websites the data to increase fairness can be and Society 20, 3 Mar.

Barocas, S. Caliskan, A. Science , bias. For example, ing professional organizations such discrimination-aware classification. Big Data 5, 2 Feb. Danks, D. Algorithmic bias in line behavioral patterns to identify bi- have established principles to guide autonomous systems. In Proceedings of the Twenty- ased behavior. For example, sues. For example, in the ACM 7.

Kleinberg, J. Human decisions and machine it would be easy to assess whether con- Public Policy Council issued a state- predictions. Quarterly Journal of Economics , 1 Jan. Lessig, L. Weapons of Math Destruction: How Big Data dardization algorithm to mitigate such ability that identified potential bias as Increases Inequality and Threatens Democracy.

Broadway Books, New York, Thus, while platforms and algo- a serious issue. Peck, P. The Atlantic rithms can be used in a discriminatory resist transparency, maintaining that Dec.

Key Changes with the General Data manner, they also can be studied to revelation of their data and algorithms Protection Regulation.

Of course, the could allow other actors to game their Silva, S. Algorithms, platforms, and ethnic bias: An integrative essay. Phylon: The Clark Atlanta will to do so is necessary. In many cases, this response University Review of Race and Culture 55, 1—2 Zarsky, T. The trouble with algorithmic decisions: An analytic road map to examine efficiency and fairness Conclusion prevents scrutiny.

Software developers in automated and opaque decision making. Science, Computer scientists have a unique often cannot provide definitive expla- Technology, and Human Values 41, 1 Jan. We contribute may be unable to provide accountabil- Martin Kenney mfkenney ucdavis.

However, even in tween algorithms and humans. The we focus on ethnic bias, it is possible to discover evidence of potential bias. Platforms, algorithms, software, the authors. The model can be particularly useful in data-driven decision-making, and ma- policy discussions to explain to poli- chine learning are shaping choices, Copyright held by authors.

The Indian subcontinent has a population close to 1. The region plays an important role in the global computing landscape with its highly trained manpower, software companies, and top universities that produce students that not only serve local needs, but move around the world and have global impact.

We developed this special section to mirror all these facets. Last year, we publicized the plans for the special section and made an open call for contributions through ACM member channels and the ACM India website. We received 45 proposals through this period and augmented the list by reaching out to others to cover specific topics and areas.

We also received proposals from colleagues in Sri Lanka and Pakistan. A selection of 22 outlines were identified for consideration. A workshop held at Microsoft Research in Bangalore on February 23, converged on the selection of 17 proposals to pursue as full articles. These articles underwent three rounds of reviews and modification. The final section presents nine articles covering Hot Topics and nine articles following Big Trends.

Computing solutions for the India region must deal with the scale of its popu- lation. Chien eic cacm. Another vibrant aspect of India is the resurgence of its start-ups. The gmail.

The section also samples some Sriram Rajamani Jakob Rehof of the research advances and challenges from this region. Watch the co-organizers discuss this section in the exclusive Communications video. Hot Topics Big Trends. Haritsa and S. Jawahar and Venkata N. By Gautam Shroff and K. Ananth Krishnan. Or would you try and multi-label classification Davis, UT Austin, Yahoo, super- predict the next word about algorithms were struggling and others have developed a power to accurately answer, to be uttered by U.

Presi- to pick the correct subset of plethora of algorithms with in a few milliseconds, a dent Donald Trump? Most of these algo- engines, which could pre- rithms with such capabili- classifier1 that could scale to rithms are either based on: dict which of the billions ties. The difficulty of the task 10 million choices, thereby of documents might be can be judged from the fact laying the foundations of relevant to a given query?

The approach was Would you build the next you just a second to read based on the realization that only a handful of choices would be relevant for any Extreme classification given question on average.

The trick was therefore to has found applications in diverse quickly eliminate the mil- areas ranging from information lions of irrelevant choices. The classifier could then retrieval to recommender accurately and efficiently systems to computational choose from the remaining hundred or so options.

Many papers on the feature representation extreme classification have the discovery of new customers. For instance, the Slice tion. For instance, extreme content-based approaches.

Similarly, soft Bing platform where it nical challenges. A number lion choices and can be run extreme classification could has significantly increased of workshops have been or- on a laptop for small prob- also be used to predict which the ability of millions of ganized at Dagstuhl,2 ECML, lems.

Benchmark datasets of the top million videos people around the world to ICML, NeurIPS, and WWW as well as the source code for you might wish to watch discover the products and to discuss these questions.

In certain cases, refor- services they are looking for. Watch the online videos from are publicly available at The mulating such problems as At the same time, extreme these workshops or check out The Extreme Classification Repository3 if you are looking for an extreme challenge and want to help the community build the next generation of search engines and recom- mender systems. References 1. Agrawal, R. Multi-label learning with millions of labels: Recommending advertiser bid phrases for Web pages.

In Proceedings of the Intern. Bengio, S. Extreme classification Dagstuhl Seminar Manik Varma manik microsoft. Publication rights licensed by ACM. In estimated 30, deaths. We explore the least one account for every tion. Based Pakistani Unlike the devel- of SMS text messages and bringing about positive affordability of financial oped world, mobile bank- interactive voice response socioeconomic change services for a popula- ing in Pakistan must ac- IVR system that provides and gains in economic tion and is an important count for phones as shared critical information for growth.

Impact reveal ICTs are one of the and inequality. Similarly, given A key issue faced by women function as social net- the harassment on voice- in Pakistan is the lack of digital works and allow women to based social platforms that anonymously create, enjoy, women face, how do you social connectivity. This is and share content. Ali, A. Baang: A viral and Pakistan is a country with entertainment, education, and health opportunities.

Arun, S. One open chal- India? Grameen of the G7 Countries. Foundation, Technology and Industry Working tions for shared mobile 3.

Bahrini, R. Impact 7. Mustafa, M. In Proceedings of the Maryam Mustafa maryam. Almost all 4. Batool, A. Pradhan, R. In Norman, N. The dynamics of Amna Batool amna. Proceedings of the 9th Intern. Fajembola, T. Abiye: Safemotherhood: countries. Technology in Society 42 is an assistant professor at Information This is not true in Pakistan, A case of leadership in turning the , — Technology University, Lahore, Pakistan.

Schreyer, P. Information and Communication used by an entire family. Mechael, P. A radically different native optimizer. A primary approach that addresses These techniques repre- reason for the popularity of Robust Query Processing the guarantee issue, called sent an important mile- database systems is their A crucial input to generat- PlanBouquet,a has been stone in the history of ro- support for declarative ing efficient query execu- recently developed at IISc bust query processing since queries, typically in the tion strategies, called plans, Bangalore.

In this are the statistical estimates completely abandons quantitative performance programming paradigm, of the output data volumes the classical estimation guarantees, addressing a the user only specifies the for the algebraic predicates process for error-prone critical need of the database end objectives, leaving it to present in the query.

In selectivities—instead, it community. Declara- respect to the actual values plan executions that are tions often suffer from tive specification of queries subsequently encountered progressively capable of poor performance arising is also central to parallel during query execution. An advanced variant access information from optimization have been times that may be worse by of this approach, called a database. A considerable body of relative to the ideal, where D of which requires a signifi- systems.

Nevertheless, literature exists on improv- is the number of predicates cant round-trip time due to important challenges re- ing the statistical qual- whose selectivity estimates latency in the database and main unsolved, and Indian ity of selectivity estimates may be erroneous. Such inefficien- universities have played a through sophisticated sum- Further, empirical evalu- cies cannot be addressed visible role in addressing mary structures, feedback- ations on industry-standard either by traditional these issues.

As exemplars, based adjustments, and benchmarks have shown database query optimizers we highlight recent research on-the-fly re-optimization SpillBound to perform, in or by traditional compiler the worst-case, within a optimizations.

The DBridge factor of 10—20 of the ideal, systemb developed at IIT The DBridge suite of techniques whereas contemporary Bombay therefore tackles database systems may suf- this problem by rewriting brings the powerful benefits fer performance degrada- application code to opti- of declarative query optimization tion factors running to the mize data access.

This perfor- set of potent equivalence- mance robustness of Spill- preserving transforma- Bound is quantified in Fig- tions on imperative code. Native Optimizer Spillbound When will Queue of my turn come? Program Figure 1. Performance robustness profile. Figure 2. Rewrites for optimizing data access.

Productive future A metaphorical depiction More details on these tech- bases with a desired set of work areas include extend- of batching rewrites in niques may be found on the characteristics can be ef- ing the holistic optimization DBridge is shown in Figure DBridge project home page. Ashoke, S. CODD: A a restricted scope and is important to ensure both gression query suites, are dataless approach to big data testing.

Chandra, B. Data generation for testing and grading SQL can successfully rewrite functionally correct. The time and space overheads queries. VLDB J.

Dutt, A. Plan bouquets: grams. ACM Trans. Database Syst. Emani, K. In alternative transformed common errors. Karthik, S. Platform- mization based on alge- the nuances of SQL query An important reason for the independent robust query processing. IEEE Trans. Data Eng. Techniques for holistic correctness; further, the the s was its simplicity, 6. Ramachandra, K. Program transformations for taining imperatively coded tomated grading of incorrect query optimization.

Howev- asynchronous and batched query submission. Data user-defined functions queries by assigning partial er, a host of complex features Engg. First, up for sale, more effectively highlight- to offer, among other services, aerial drones are used to survey fields. In- ing elements that appeal to buyers. Often, the deliveries over large areas. Rayner told German broadcaster DW.

That dynamic has led companies Last December, Joy Nowai of Vanu- Some drones are even used to spray like Swoop Aero to look at use cases atu was, according to the company, fields with pesticides. Drone Doctors thanks to Swoop Aero. The drone trav- In all of these examples, the drones One high-value commodity that makes elled plus miles to deliver the vac- are being piloted remotely by experi- a lot of economic sense to deliver via cine, while keeping it at the optimum enced professionals.

For instance, drone is medicine. Swoop Aero is now preparing to capture cost advantages that offset the Swoop Aero operates drone net- deploy additional drone networks to expense of human pilots. The company says health workers would normally be sense of visual information, particu- placing orders for medications via text larly at high speeds, in part because can expect the requested medicine to delivered by mail of shortcomings in the sensors they be delivered within 30 minutes.

Earlier this year, cess images at the speed required to logistics company UPS, in partnership recognize and avoid obstacles. Compa- Thompson, F. The goal was to find the remains of ships manned or semiautonomous drones, Wilke, J. The company last-mile logistics at scale requires A drone program taking flight, Amazon, Jun.

Businessweek, Jun. He has written for over 60 major company control centers. Instead of ing into delivery drones. These AI- publications. Dear Colleague,.

Without computing professionals like you, the world might not know the modern operating system, digital cryptography, or smartphone technology to name an obvious few. ACM offers the resources, access and tools to invent the future. No one has a larger global network of professional peers. No one has more exclusive content. No one presents more forward-looking events. Or confers more prestigious awards. Or provides a more comprehensive learning center.

Here are just some of the ways ACM Membership will support your professional growth and keep you informed of emerging trends and technologies:. Joining ACM means you dare to be the best computing professional you can be.

It means you believe in advancing the computing profession as a force for good. Box Global acmhelp acm. There is still reason copyright owners about where such In some respects, the DSM Direc- materials were located. How to ensure the unavailability of specific much harm will depend on how mem- and information works.

Previous drafts of ences between earlier drafts of the the Directive were more pointed about DSM Directive and the final version the need to use filtering technologies. One is cism, review; b use for the purpose of mandatory new exception to copyright for nonprofit services, such as online caricature, parody, or pastiche.

A second sharing sites will meaningfully limit the for scientific research purposes. The liability of these of filtering technologies to refine their data miners out in the cold. Because two types of services for user infringe- algorithms so that all parodies, critical EU policymakers aspire to foster the ments are subject to compliance with comments, and other privileged uses growth of artificial intelligence and the existing notice and takedown rules.

Yet, other data-intensive businesses, they Critics have charged that the DSM this seems to be the only way to ensure came to recognize restricting text and strict liability rules will interfere with privileged uses can be preserved.

To this end, they proposed general mandatory exception to copy-. Second, the Ar- by individual users. Ambiguities about this and The final version of the DSM Directive other terms in Article 15 makes it un- directs member states in Article 15 likely that member states of the EU will previously Article 11 to grant press implement this new right in a harmo- publishers two years of exclusive rights nious way. Opponents charged it story in support of the new liability would impede the free flow of news and and exclusive rights rules that the Di- other information vital to a democratic rective has now established.

Critics also thought this new U. Both Eu- to the detriment of smaller players. If such licensing occurs, then about the press publisher right, the EU the stricter rules will not need to be ap- plied, and worries about harms to free- dom of expression and other social val- Earlier versions ues expressed by critics of the stricter liability rules will not come to pass.

Whether the licensing Calendar It is not even remotely possible for online content-sharing services to get licenses from every copyright owner goals of the DSM of Events of European works available in digi- Directive will be October tal form. The aspiration of Article 17 fulfilled also Oct. Feigenbaum yale.

Ne- tors will coordinate efforts to resolve Contact: Jeffrey Philip Bigham, Email: jeffreybigham gmail. You- licensing on fair and reasonable terms. Some online Email: wwzhu tsinghua.

Both countries Nov. The to license press publisher news these on Advances in Geographic goal of Article 15 is to enable licensing services provided to their users. Kashani, toring services, and search engines Maybe the EU-wide nature of the Email: farnoush. One thing is for sure: U. Pamela Samuelson pam law. Copyright held by author. Email: rganti us. Quickly, teachers, cur- riculum and standards writers, and other education specialists were pro- posing what children needed to know about computation and how to devel- op a computational mindset.

The computational thinking move- ment puts the onus on the student and on the education system. They argue that if we change humans to think in ways that are informed by how we now work with computers, that will have prob- lem-solving advantages for the humans. It is our job to penter thinks. Before drawing understanding the purpose and actively Thinking Is Just Thinking the obvious conclusion, consider the participating, so there is parallel devel- Tool use shapes thinking.

While we following vignettes. We put the onus can learn to see what is possible with as many students as professors at this. Use S for engineering, medical, mathematical, the number of students and P for the environment allows students to en- and literary fields. Computing simula- number of professors. We can ask if these are hon- fields. So, there is a bird to be caught dents provided the correct equation what they are doing, and if they are de- if we can sprinkle salt on its tail.

Vignette 2 shows us it is not just all processes. A wide variety of computational ture, but to deal with the large range dents using pencil or pen and paper. It is honest use of puter programming. The users in those Vignettes are us- This is a much larger—and in our Vignette 3: Now, consider the follow- ing computational tools to do compu- opinion—much more useful charac- ing comment: tational thinking.

TikTok though they may not use those words. Video producers been about identifying the computing ing parts of all kinds, and how to make collaborate around the world to make ideas and practices. Maybe the kids and model dynamic systems for deeper duets, without ever meeting. Using Tik- are already learning those, but on dif- understanding and considerable fun Tok is not about writing like a profes- ferent terms, without our language.

Imagine something as engaging sional. TikTok is an entirely new medi- People of the so-called baby-boom- as Fortnite where the system is inspect- um, enabled by computation. It leads er generation may feel computational able, where users might model their to writing and saying differently than thinking is something special—and strategies and test them in simulation one could without computation.

For the chil- first, so that students might learn to use Vignette 4: Finally consider the fol- dren growing up today, who are in- the power of expanded thinking. In thinking is, well, just thinking! But that guages being taught today. For example, playing Fortnite, players use a broad is just not enough.

Learning to com- none of the common K—12 program- range of computational tools to solve pute should give students a qualitative ming languages today are very good at significant problems, from map navi- leap, so that they can think about new modeling intercommunicating pro- gation, to team collaboration, to man- problems and think about the world in cesses—despite both natural and hu- aging complex ecological systems.

Most of the languages that we put engage in these kinds of activities for never-seen-before problems? We in school today can only handle one in their everyday world outside of might start by redesigning TikTok thread of control without ungraceful ex- the computer. The computational and Fortnite. It might give students access to expanded think- make later learning of a more powerful ing.

We in computing have a powerful version more difficult. We can change the computation. We imagine vironment both in and out of school. If the foundational a comprehensive suite of intellectu- Montessori were alive today, she would development of scientific ally honest computing-based models still want to redesign school, but she rigor in digital security by for understanding systems can lead to would likely want to change the com- much better notion of programming— puting, too.

These whole environment. How would Mon- academic research and will lead to much better programming tessori redesign Fortnite? What would industry practice. It is our job to address extant digital gineering, and computing itself. Clement, J. Algebra word problem solutions: Thought accepted for publication, ture, science, mathematics, and history processes underlying a common misconception. Dewey, J. How We Think. Understanding civiliza- Revised edition ; D.

Heath, Boston, MA, Montessori, M. The Montessori Method. Frederick A. Norris, C. Students write more, write all citizens. In our metaphor, we want better on the computer: Rigorously supported! Journal, Nov. Wing, J. Computational thinking. Students need fluency in order to be able to understand models Mark Guzdial mjguz umich. Alan Kay alan. He is the recipient of the ACM A. Turing Award.

For further information are all best taught in context. Rather than teach com- in alphabetical order. Copyright held by authors. As a doctoral student at MIT in the early s, Kleinrock ex- tended the mathematical discipline of queuing theory to networks, providing a mathematical description of packet switching, in which a data stream is packetized by breaking it into a se- quence of fixed-length segments pack- ets.

ACM Fellow Kleinrock has received many awards for his work, including the National Medal of Science, the highest honor for achievement in sci- ence bestowed by a U. Leonard Kleinrock.

My father fell ill and could not con- in elementary school reading a Super- The clerk helped me select the right tinue to run his grocery store, so I re- man comic whose centerfold showed part. Oh, the magic of listening to mu- alized I could not attend college in how to build a crystal radio out of sic from my newly built radio; and it day session and had no choice but to household items that one could find required no battery or power at all. Af- go to night school and bring home a on the street: a razor blade, some pen- ter that, I kept cannibalizing old radios salary by working full time during the cil lead, a toilet paper roll, and an ear- and used the parts to design new radios day.

That was a big blow. My father phone which I stole from the tele- that I put together. My mother never took me to an electronics firm where phone booth in the candy store down got in my way and allowed me a place I could get a job serving as an elec- the street.

I also needed a variable ca- behind our sofa to make a mess and to tronics technician and eventually as pacitor and had no clue what that was, do my tinkering. I worked point I would be working full time at at night. My day work was, however, in a group at Lincoln run by Wes MIT Lincoln Lab and we needed the wonderfully interesting: we were in- Clark who built arguably one of the money since my wife would have to volved in designing and using photo- first PCs the Linc computer. So care for our newborn.

So, I was not at electric devices in many applications. But The people in night school were an Lincoln; and of course MIT profes- Frank Reintjes was insistent and, interesting bunch—after all, who at- sors would often visit.

Yet, my mas- accepted the Ph. Two mined. I made use of founders of the Internet, see later— ters. I remember a professor bring- the Kerr magneto-optic effect where- Ed. Turing Award recipient—Ed. As a result, one could eliminate the temperature-dependent use polarized light to non-destruc- What were the first years of your MIT variations. My Our Ph. My MS at and insight behind theory. Claude The thesis involved experiments and MIT made it easier since the qualify- Shannon, who was then—and still is— models and I even constructed a spe- ing exam was largely based on the my role model, similarly had great in- cial digital logic using light bouncing MIT MS curriculum, but full of trick sight and physical intuition into why off a sequence of thin films.

My thesis questions. Interestingly Ivan Suther- things happened alongside his mathe- must have impressed my MS supervi- land came in directly from Caltech matical approach to problems. How did you go to But the idea was that after a Lincoln to study; he is one heck of a smart guy. MIT instead? And Ph. Our first child was MIT, and so called up Claude Shan- would provide considerable financial non founder of information theory. I remember the Ed. Shannon wanted me sitting at the back.

That I with each other. MIT as a Lincoln Labs associate? I was looking for a fresh field to work My first supervisor at Lincoln was on. It seemed to me that even Shannon. It seemed to me that the changing messages—whose size and problems that remained seemed I taught while at MIT.

And chess a probability distribution—across a was not my forte. At the same time, be- network of what we would call routers ing surrounded by computers at MIT today. Networks of steady determinis- and at Lincoln Lab, it seemed inevita- tic traffic flows had been studied for ble to me they would eventually need example, Max-flow Min-cut theorems to communicate with each other and had just appeared and one node sys- the existing telephone network was control theory pioneer were part of tems with stochastic arrivals had been woefully inadequate for such a chal- the same suite with me.

This lab well studied queueing theory. How- lenge. I remember the stimulating combination of those two, and this led thurs by the way one other student of conversations in which we engaged to stochastic networks. This was a very Arthurs was Irwin Jacobs of Qual- and taught each other our very differ- hard problem to solve analytically, comm fame. Arthurs mentioned a ent fields information theory, control and to this day, the exact analysis is classified project he had encountered theory, networking, and so forth and still intractable.

However, I was able for a network between computers. How did that happen? Erlang in the early of slow circuit switching, was not up s, then taken up by the mathema- You were inducted into the Internet to the job. Yet queueing systems models networks, the technology underpin- a demand request when that demand had all the ingredients of a mathe- ning the Internet.

This concept is manifest to- since it dealt with ways of analyzing sue was how to support bursty traffic day in so many systems for example, throughput, response time, buffer in a data network, it became clear Uber, AirBnB, seats on an airplane, size, efficiency, and so forth.

Further, that the mathematics needed to rep- and so forth in what we often refer to and importantly, it was a perfect resent the network had to be based as a shared economy. I tried to remedy that later by writ- There were at least two other inde- were especially interesting since, as ing a two-volume textbook called pendent threads that were nearly systems scale up, emergent properties Queueing Systems, which was queueing concurrent.

Tell us that was published in a book. He also pro- mation theory pioneer and inventor of enced many others because of its posed distributed network topolo- Lempel-Ziv coding and Tom Kailath clarity and strong intuition. His work albeit less optimal, protocol where the was classified and so I did not see his dynamics of the network were reflect- papers until my thesis was complet- While we think of the ed in which links of interest out of a ed.

His work was right on! Another Internet today to send node were idle, indicating that the important thread was from Donald route to the destination using a Davies who was working indepen- email and support queued link was not desirable and dently at the National Physical Labo- social networks, that the uncongested links leading ratory in England and who realized out of a node to that destination were that the packet switching was good the motivation then currently better choices.

Larry Roberts, Ivan Sutherland, than were small packets; hence he and I were very close friends and did suggested packets of approximately our thesis defenses at the same time bytes, which was later used in the because we had all heavily used the ARPANET design. The union vantage of allowing small messages to of our three Ph.

These include techniques including Claude Shannon, who was this trade-off mathematically in my such as priority queueing for example on each of our committees, Marvin dissertation years earlier. Is there any- members. The projects were very would reduce delays in overall trans- thing else you want to mention before different; we were just all using the mission. Further, he noted that the we move on from your thesis?

Ivan did this great work on ability to pipeline packets reduced la- Some other aspects include the ef- Sketchpad, Larry did his on machine tency through the network. I showed for the first perception of three-dimensional ob- time that in terms of performance, a jects, and I did mine on communica- So in some ways, Paul, Donald, and single link of capacity C is better than tion networks. Paul fo- an example of the second principle I verify the accuracy of my mathemati- cused on routing resiliency, Donald on mentioned previously.

I investigated cal approximations. How models. Is that accurate? I investigated distributed adaptive coln Labs? But issues whereas I was more focused on dered sequence of favorable routes they were remarkably generous and extracting the underlying principles for each destination and use the first offered to have me look around the and developing a mathematical theory route that was not congested locally.

Among the princi- see what opportunities were there. I ples were: dynamic resource sharing is Interesting! And porting lots of traffic are far more effi- to compute shortest paths. However, then there were academic offers, in- cient than small resources supporting that technique takes longer to respond cluding the one from UCLA for a ten- less traffic; and distributed adaptive to failure.

Some networks today for ex- ure-track position at half the salary control is efficient, stable, robust, ample, MPLS protection use your idea I would get at Lincoln. But I had fault-tolerant, and it works. So with the lyzes other benefits of packet switch- jkstra-type updating procedure dy- West Coast weather, the Wild West ing we rely on today that are comple- namically based on networkwide appeal, and a university position, I mentary to those pointed out by shortest paths.

I introduced a simpler, drove my family all the way across. The need for a more advanced am still here! On October 29, ed the contract. My role in this ARPANET project mercial backbones who had to In my software team, besides Char- was performance evaluation, design, cooperate, and so forth, are all well ley there was Steve Crocker who head- experimentation, and measurement.

It was natural Tomlinson introduced email in Internet luminaries. The backstory that we would be the first node so that and it very quickly took over a major starts with Ivan Sutherland who be- we could begin to conduct experimen- portion of the traffic; that was when came head of IPTO for ARPA in Put another way, we identical IBM s on campus. In- to share their computers, so that net- notations that go along with the Cre- deed, it has been the case over and work was never implemented.

Was this deliberate? We were trying to send the nity has been surprised each time ma- How did it finally happen? Bob convinced Larry to the Internet we know today? While we that the ARPANET would shortly be ternet we did not foresee was the think of the Internet today to send emergence of the dark side in all its email and support social networks, manifestations that plagues us today. Tell us more My first money on this crazy idea.

Most of single-node packet switch and in- my students who followed concen- corporated their ideas in the ARPA- trated on various performance anal- NET design. Mario Ger- tunities for future generations of re- la provided optimal routing design I fear the power searchers.

I think it would be interest- and provided an effective protocol. Facebook grew so popular while oth- is, starting to forward a packet as ers failed so that we can try to be bet- soon as the router read the destina- ter at prediction. Perhaps a lens based tion address, thereby reducing laten- on behavioral economics of the sort cy, which is pervasive today in Local pioneered by Nobel Prize winning Area Networks.

Simon Lam and Fouad Tobagi literally Los Angeles and metaphori- I am concerned about the Bal- initiated the analysis and design of cally the Internet was the new fron- kanization of the Internet as nation- wireless networking and provided the tier, the Wild West of communica- states cut off and censor Internet early analysis of Slotted Aloha Lam tion.

And so on. I fear the power of the Inter- What advice do you have for young re- net is being lost. I realize this is partly And the triumvirate: Gerla, Tobagi, searchers?

For example, areas that are Mario and I worked on network de- in need of innovation, research, and You have had a remarkable career of sign techniques that went beyond my development include IoT, distributed academic excellence network perfor- thesis while Howie Frank and Ivan ledgers, the introduction of biologi- mance evaluation clearly begun with Frisch were concurrently working on cally inspired principles to network- your thesis and your later work with different techniques at Network Analy- ing and engineering in general , dis- your UCLA students , real-world impact sis corporation.

Mario went to work for tributed intelligent systems, advanced you were heavily involved in the evolu- Howie Frank but we later hired him network architectures, network secu- tion of the Internet including helping back at UCLA. The space is write the famous Gore proposal , and of the satellite packet switching meet- awash with great problems to dive entrepreneurship you have started sev- ings we had where he was my right- into.

In the case of distributed led- eral companies including Linkabit hand man. Out of that came his disser- gers, the technology that underlies with Jacobs and Viterbi, which later tation on the analysis of the instability bitcoin, I am excited on the one hand, led to Qualcomm and Nomadix an of Aloha that Abramson had created in but concerned on the other hand. What Hawaii. The application was technologies soon after its birth, thus of your work. It is not enough to evalu- to foot soldiers or possibly tanks mov- distorting its path to proper maturity; ate your ideas.

You need to keep think- ing across a battlefield; that led to the this is because profit-seeking compa- ing about them to distill principles be- SURAN survival radio network, and the nies and speculators jumped on the fore moving on to the next big thing.

Tobagi bandwagon right away, which may Second, try to bounce ideas among and I started studying CSMA—carrier lead to brittle designs. By contrast, we brilliant people.

I have had the fortune sense multiple access—which eventu- had 20 years without commercial in- of doing so with folks like Shannon, ally contributed to Ethernet. Third, aspire like Shan- dustry in both network design pop- helped, I believe, to make the Internet non to combine physical intuition with ular in the s and to media ac- more robust.

While I have cess schemes which continue to be done a fair amount of mathematical popular today because of My and WiFi. As I said before, it is hard to pre- early building of a crystal radio re- That early work caught the atten- dict.

We missed the social networking mains a watershed event in my life. So, in some continued studying the behavior of sense, we have created an Internet George Varghese varghese cs. We were fortu- that is destined to continually sur- USA.

Viewpoint Algorithms, Platforms, and Ethnic Bias How computing platforms and algorithms can potentially either reinforce or identify and address ethnic biases. Al- cause their actions affect outcomes. As gorithms already sift through expand- algorithms that shown in the accompanying figure, we ing datasets to provide credit ratings, make or structure identify nine potential biases.

They are serve personalized advertisements, not mutually exclusive, as it is possible match individuals on dating sites, real-world decisions.

In principle, riety of other tasks. Digital platforms occur in an algorithmic process. Algorithms and on- ognized. Some attribute the introduc- tain bias. For example, if the criminal line platforms are not neutral; they are tion of bias into algorithms to the fact justice system is biased, then, absent built to frame and drive actions. Thus, training sets can be with specific hypotheses about the fairness. Algorith- things. As techniques such as machine expressions.

For instance, the exclusion For engineers and policymakers alike, identifying and addressing the sources of gender or race in a health diagnos- understanding how and where bias of bias—conscious or unconscious— tic algorithm can lead to inaccurate or can occur in algorithmic processes can that might infect algorithms.

What is even harmful conclusions. However, help address it. Our contribution is the certain is that without proper mitiga- the inclusion of gender, race, or even introduction of a visual model see the tion, preexisting societal bias will be ZIP codes in a sentencing algorithm. Input Algorithm Output Users 1. Training Data Bias 3. Algorithmic Processing Bias 4.

Transfer Context Bias 7. Automation Bias 2. Algorithm Focus Bias 5. Interpretation Bias 8. Consumer Bias 6. Outcome Non-Transparency Bias. Feedback loop bias. Source: The first six biases were adapted from Danks, D. The visualization and remaining materials are by Silva and Kenney. This is the Algorithms, particularly artificial intel- bias can occur from either side, or party, conundrum: in certain cases, such ligence and machine learning, often in a digital interaction.

Or, even more variables must intentionally be used to generate opaque results. The reasons deliberately, anonymous online hack- produce less-biased outcomes. For example, when a ma- for only a few days in , to respond One source of such bias is the inclusion chine-learning program recommends with racially objectionable statements. The which to express their biases. Algorithmic sys- ticular Japanese manga site. For example, embodied in job-candidate-sorting soft- to identify discriminatory outcomes or the Google Search algorithm responds ware, then this seemingly innocuous even the reasons for the outcome.

Automation bias customized input for subsequent candidates. Effectively, a desired proxy results from the belief the output is searches. The algorithm learns from trait inadvertently excludes certain fact, rather than a prediction with a con- user behavior. For example, in predic- groups that could perform the job. For instance, credit deci- tive policing, the algorithm relies al- Transfer Context Bias. Transfer con- sions are now fully automated and use most entirely on historical crime data.

One example is using people lower scores and limits their vent crime. Not surprisingly, increased credit scores to make hiring decisions. Credit denial means police presence leads to higher crime Bad credit is equated with inferior future their scores cannot improve.

Often, the detection, thereby raising the statisti- job performance, despite little evidence subjects and decision-makers are un- cal crime rate. If the undesirable, but ir- and uncritically accept the decisions.

In another example, Google then it might lead to biased outcomes. Interpretation tection by limiting automated algorith- websites are often selected and there- bias arises when users interpret algo- mic decision processes for legal or the fore recommend them more often, rithmic outputs according to their in- equivalent life-affecting decisions.

As smart as ternalized biases. For example, a judge Consumer Bias. The biases that hu- algorithms can be, human monitoring can receive an algorithmically generat- man beings act upon in everyday life are continues to be necessary.

Fur- cide on the punishment or bail amount ther, digital platforms can exacerbate or Benefits of Platforms for the defendant.

Because individual give expression to latent bias in online and Algorithms judges may be unconsciously biased, behavior. Consumer bias.

It is well known that members of. For example, Kleinberg bias are too pessimistic. Digital pro- et al. In cerated while also reducing the rate the digital world, the data captured is of reoffending. Although high number of people in certain racial digital technologies have the potential groups are incarcerated. The point is to reinforce old biases with new tools, that it is possible to design algorithms they can also help identify and monitor with different social goals.

Critics ig- progress in addressing ethnic bias. Public Policy Council: Statement on Algorithmic efficiency and effectiveness. Transparency and Accountability. Ananny, M. New Media outcomes. To illustrate, sales websites the data to increase fairness can be and Society 20, 3 Mar. Barocas, S. Caliskan, A. Science , bias. For example, ing professional organizations such discrimination-aware classification.

Big Data 5, 2 Feb. Danks, D. Algorithmic bias in line behavioral patterns to identify bi- have established principles to guide autonomous systems. In Proceedings of the Twenty- ased behavior. For example, sues. For example, in the ACM 7. Kleinberg, J. Human decisions and machine it would be easy to assess whether con- Public Policy Council issued a state- predictions. Quarterly Journal of Economics , 1 Jan. Lessig, L.

Weapons of Math Destruction: How Big Data dardization algorithm to mitigate such ability that identified potential bias as Increases Inequality and Threatens Democracy. Broadway Books, New York, Thus, while platforms and algo- a serious issue.

Peck, P. The Atlantic rithms can be used in a discriminatory resist transparency, maintaining that Dec. Key Changes with the General Data manner, they also can be studied to revelation of their data and algorithms Protection Regulation. Of course, the could allow other actors to game their Silva, S. Algorithms, platforms, and ethnic bias: An integrative essay. Phylon: The Clark Atlanta will to do so is necessary. In many cases, this response University Review of Race and Culture 55, 1—2 Zarsky, T.

The trouble with algorithmic decisions: An analytic road map to examine efficiency and fairness Conclusion prevents scrutiny. Software developers in automated and opaque decision making.

 
 

 

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Even raw, clean corpora are of great value for language computing. Impact 7. Shannon wanted me sitting at the back. The companies govern,ent matured from providing cost-effective back office solving Y2K issues for their customers support to driving the digital transformation agenda and further extended their приведу ссылку to. Sengupta, D. I was very undecided about which bike to buy. The section also samples some Sriram Rajamani Jakob Rehof of the research advances and challenges from this region.

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