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Die aktuelle Situation in Indien beunruhigt viele, die den Bestand der liberalen Demokratie gefährdet sehen. Der nachfolgende Text behandelt diesen Sachverhalt, übersteigt allerdings den gewohnten Umfang eines Artikels in SÜDASIEN. Er bettet das Regierungshandeln und dagegen gerichtete Proteste so in ein politisches Umfeld ein, dass sich Konturen einer illiberalen Strukturbildung erkennen lassen. Der Artikel grundiert insofern weitere Texte in diesem Heft. Metaphorisch ausgedrückt ist der längere Atem für das Lesen wohl auch darüber hinaus in Bezug auf Indien angebracht., Südasien, Bd. 40 Nr. 1 (2020): Umwelt - Konzept und Realität

Front Matter, Table of Contents, Preface, Conference Organization

We consider the problem of sketching the $p$-th frequency moment of a vector, $p>2$, with multiplicative error at most $1\pm \epsilon$ and \emph{with high confidence} $1-\delta$. Despite the long sequence of work on this problem, tight bounds on this quantity are only known for constant $\delta$. While one can obtain an upper bound with error probability $\delta$ by repeating a sketching algorithm with constant error probability $O(\log(1/\delta))$ times in parallel, and taking the median of the outputs, we show this is a suboptimal algorithm! Namely, we show optimal upper and lower bounds of $\Theta(n^{1-2/p} \log(1/\delta) + n^{1-2/p} \log^{2/p} (1/\delta) \log n)$ on the sketching dimension, for any constant approximation. Our result should be contrasted with results for estimating frequency moments for $1 \leq p \leq 2$, for which we show the optimal algorithm for general $\delta$ is obtained by repeating the optimal algorithm for constant error probability $O(\log(1/\delta))$ times and taking the median output. We also obtain a matching lower bound for this problem, up to constant factors., Comment: Extended Abstract to appear in Proceedings of ICALP 2018

LIPIcs, Volume 122, FSTTCS'18, Complete Volume, LIPIcs, Vol. 122, 38th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2018), pages 0-0

This summer, India deployed troops to prevent China's People's Liberation Army (PLA) from constructing a road on the Doklam plateau near the contested Bhutan-China-India border. Indian forces stood in the path of the construction crews, blocking their work and at times even tussling with Chinese troops. Despite increasingly harsh warnings from Beijing, including the threat of "all-out confrontation ," the Indians held fast. After a nearly two-month standoff, both sides disengaged and the PLA stopped its road-building activity, though China made clear that it would "continue fulfilling its sovereign rights" by stationing troops and patrolling in the area.

India is in the market for a new fighter plane—actually, about 200 new fighter planes. The country's fleet of MiG 21s is aging and increasingly prone to accidents, so it is seeking a replacement capable of air superiority and ground-attack missions [1]. Initial reports suggested that the indigenously produced Tejas light combat aircraft (LCA) might play this role. The Tejas, however, has been plagued with problems; a government investigation identified 53 design flaws, including underpowered engines, excess weight, poor maneuverability, lack of fuel capacity, underperforming radar, and maintenance shortcomings. Thus, despite a development process spanning more than 30 years, the Tejas remains unfit for combat duty. A Tejas Mark II will supposedly address many of the first edition’s shortcomings, but flight testing is not expected to commence until late 2018.

The paper C and D of this thesis are not available in Munin. Paper C: Ganguly, S., Renz, D., Giles, L. J., Gagnon, K.J., McCormick, L. J., Conradie, J., Sarangi, R., Ghosh, A.: “Cobalt- and Rhodium-Corrole-Triphenylphosphine Complexes Revisited: The Question of a Noninnocent Corrole». (Manuscript). Published version available in Inorg. Chem.2017 Paper D: Ganguly, S., Conradie, J., Bendix. J., Gagnon, K.J., McCormick, L. J., Ghosh, A.: “Electronic Structure of Cobalt-Corrole-Pyridine Complexes: Noninnocent Five-coordinate Co(II) Corrole-Radical States». (Manuscript). Published version available in J. Phys. Chem. A, 2017. In recent years, first-row metallocorroles have provided some of the most instructive examples of noninnocent ligands. This thesis presents a study of some 50 iron, manganese, and cobalt corrole complexes with different axial ligands (including approximately 30 new compounds and 6 new X-ray structures) with emphasis on their noninnocent/innocent character. UV-vis spectroscopy has for some time provided a convenient empirical probe of ligand noninnocence in transition metal meso-tris(para-X-phenyl)corrole (TpXPC) complexes. Redshifts of the Soret maxima with increasing electron-donating character of the para-substituent X have indicated noninnocent systems, while substituent-insensitive Soret maxima have indicated innocent corrole ligands. I have greatly expanded the scope of this optical probe of noninnocence and used it to identify new classes of noninnocent metallocorroles. The μ-oxo diiron corroles have long been thought of as true Fe(IV) complexes. However, as discussed in Paper A, a study of the optical spectra of {Fe[TpXPC]}2O derivatives along with DFT calculations have indicated a noninnocent description for these complexes. Iron-aryl corroles are classic examples of true Fe(IV) species. Several Fe-aryl corroles were synthesized and examined together with other Fe corrole derivatives by means of UV-vis and NMR spectroscopy and electrochemistry in a detailed study for ligand noninnocence in a wide range of Fe corroles (Paper B). Analogous studies of Mn-aryl corroles were also carried out (Chapter 3), but these have not yet been written up as a manuscript. A collaborative study with the Stanford Synchrotron Radiation Lightsource also provided the first X-ray absorption spectroscopic analysis of the question of ligand noninnocence in Fe corrole complexes. Cobalt-triphenylphosphine (Co-PPh3) corroles have also been thought of as classic low-spin Co(III) complexes until now. An examination of the UV-vis spectra of Co-PPh3 corroles and DFT calculations again indicated a partially noninnocent Co(II) corrole radical description for these complexes (Paper C). In contrast, an innocent description was indicated for Rh-PPh3 corroles. Finally, as discussed in Paper D, Co corrole pyridine adducts were likewise studied with a variety of spectroscopic techniques and the noninnocent behavior of the monopyridine adducts was elucidated in considerable detail.

We present a randomized algorithm for estimating the $p$th moment $F_p$ of the frequency vector of a data stream in the general update (turnstile) model to within a multiplicative factor of $1 \pm \epsilon$, for $p > 2$, with high constant confidence. For $0 < \epsilon \le 1$, the algorithm uses space $O( n^{1-2/p} \epsilon^{-2} + n^{1-2/p} \epsilon^{-4/p} \log (n))$ words. This improves over the current bound of $O(n^{1-2/p} \epsilon^{-2-4/p} \log (n))$ words by Andoni et. al. in \cite{ako:arxiv10}. Our space upper bound matches the lower bound of Li and Woodruff \cite{liwood:random13} for $\epsilon = (\log (n))^{-\Omega(1)}$ and the lower bound of Andoni et. al. \cite{anpw:icalp13} for $\epsilon = \Omega(1)$., Comment: Supercedes arXiv:1104.4552. Extended Abstract of this paper to appear in Proceedings of ICALP 2015

We show an improved lower bound for the Fp estimation problem in a data stream setting for p>2. A data stream is a sequence of items from the domain [n] with possible repetitions. The frequency vector x is an n-dimensional non-negative integer vector x such that x(i) is the number of occurrences of i in the sequence. Given an accuracy parameter Omega(n^{-1/p}) < \epsilon < 1, the problem of estimating Fp is to estimate \norm{x}_p^p = \sum_{i \in [n]} \abs{x(i)}^p correctly to within a relative accuracy of 1\pm \epsilon with high constant probability in an online fashion and using as little space as possible. The current space lower bound for this problem is Omega(n^{1-2/p} \epsilon^{-2/p}+ n^{1-2/p}\epsilon^{-4/p}/ \log^{O(1)}(n)+ (\epsilon^{-2} + \log (n))). The first term in the lower bound expression was proved in \cite{B-YJKS:stoc02,cks:ccc03}, the second in \cite{wz:arxiv11} and the third in \cite{wood:soda04}. In this note, we show an Omega(p^2 n^{1-2/p} \epsilon^{-2}/\log (n)) bits space bound, for Omega(pn^{-1/p}) \le \epsilon \le 1/10., Comment: 5 pages

Estimating the first moment of a data stream defined as $F_1 = \sum_{i \in \{1, 2, \ldots, n\}} \abs{f_i}$ to within $1 \pm \epsilon$-relative error with high probability is a basic and influential problem in data stream processing. A tight space bound of $O(\epsilon^{-2} \log (mM))$ is known from the work of [Kane-Nelson-Woodruff-SODA10]. However, all known algorithms for this problem require per-update stream processing time of $\Omega(\epsilon^{-2})$, with the only exception being the algorithm of [Ganguly-Cormode-RANDOM07] that requires per-update processing time of $O(\log^2(mM)(\log n))$ albeit with sub-optimal space $O(\epsilon^{-3}\log^2(mM))$. In this paper, we present an algorithm for estimating $F_1$ that achieves near-optimality in both space and update processing time. The space requirement is $O(\epsilon^{-2}(\log n + (\log \epsilon^{-1})\log(mM)))$ and the per-update processing time is $O( (\log n)\log (\epsilon^{-1}))$., Comment: 12 pages

A data stream is viewed as a sequence of $M$ updates of the form $(\text{index},i,v)$ to an $n$-dimensional integer frequency vector $f$, where the update changes $f_i$ to $f_i + v$, and $v$ is an integer and assumed to be in $\{-m, ..., m\}$. The $p$th frequency moment $F_p$ is defined as $\sum_{i=1}^n \abs{f_i}^p$. We consider the problem of estimating $F_p$ to within a multiplicative approximation factor of $1\pm \epsilon$, for $p \in [0,2]$. Several estimators have been proposed for this problem, including Indyk's median estimator \cite{indy:focs00}, Li's geometric means estimator \cite{pinglib:2006}, an \Hss-based estimator \cite{gc:random07}. The first two estimators require space $\tilde{O}(\epsilon^{-2})$, where the $\tilde{O}$ notation hides polylogarithmic factors in $\epsilon^{-1}, m, n$ and $M$. Recently, Kane, Nelson and Woodruff in \cite{knw:soda10} present a space-optimal and novel estimator, called the log-cosine estimator. In this paper, we present an elementary analysis of the log-cosine estimator in a stand-alone setting. The analysis in \cite{knw:soda10} is more complicated., Comment: Withdrawn due to an error in proof of Lemma 2.2 (acknowledgement: Jelani Nelson, David Woodruff)

For each $p \in (0,2]$, we present a randomized algorithm that returns an $\epsilon$-approximation of the $p$th frequency moment of a data stream $F_p = \sum_{i = 1}^n \abs{f_i}^p$. The algorithm requires space $O(\epsilon^{-2} \log (mM)(\log n))$ and processes each stream update using time $O((\log n) (\log \epsilon^{-1}))$. It is nearly optimal in terms of space (lower bound $O(\epsilon^{-2} \log (mM))$ as well as time and is the first algorithm with these properties. The technique separates heavy hitters from the remaining items in the stream using an appropriate threshold and estimates the contribution of the heavy hitters and the light elements to $F_p$ separately. A key component is the design of an unbiased estimator for $\abs{f_i}^p$ whose data structure has low update time and low variance., Comment: Withdrawn due to error in analysis

We consider general update streams, where, the stream is a sequence of updates of the form $(index, i, v)$, where, $i in {1,2 ldots, n}$ and $v in {-1,+1}$, signifying deletion or insertion, respectively of an instance of $i$. The frequency of $i in {1,2,ldots, n}$ is given as the sum of the updates to $i$, that is, $f_i(sigma) = sum_{(index,i,v) in sigma} v $. The $n$-dimensional vector $f(sigma)$ with $i$th coordinate $f_i(sigma)$ is called the frequency vector of the stream $sigma$. We consider the problem of finding an n-dimensional integer vector $hat{f}(sigma)$ that estimates the frequency vector $f(sigma)$ of an input stream $sigma$ in the following sense: orm{hat{f} (sigma)- f(sigma)} le epsilon orm{f(sigma)}_p For $p=1$ and $2$, there are randomized algorithms known with space bound $ ilde{O}(epsilon^{-p})$. A space lower bound of $Omega(epsilon^{-1} log (nepsilon))$ is also known. However, the deterministic space upper bound is $ ilde{O}(epsilon^{-2})$. In this work, we present a deterministic space lower bound of $Omega(n^{2-2/p}epsilon^{-2} log |{sigma}|)$, for $1le p < 2$ and $1/4 le epsilon = Omega(n^{1/2-1/p})$. For $p ge 2$, we show an $Omega(n)$ space lower bound for all $epsilon < 1/4$. The results are obtained using a new characterization of data stream computations, that show that any uniform computation over a data stream may be viewed as an appropriate linear map.

We consider a basic problem in the general data streaming model, namely, to estimate a vector $f \in \Z^n$ that is arbitrarily updated (i.e., incremented or decremented) coordinate-wise. The estimate $\hat{f} \in \Z^n$ must satisfy $\norm{\hat{f}-f}_{\infty}\le \epsilon\norm{f}_1 $, that is, $\forall i ~(\abs{\hat{f}_i - f_i} \le \epsilon \norm{f}_1)$. It is known to have $\tilde{O}(\epsilon^{-1})$ randomized space upper bound \cite{cm:jalgo}, $\Omega(\epsilon^{-1} \log (\epsilon n))$ space lower bound \cite{bkmt:sirocco03} and deterministic space upper bound of $\tilde{\Omega}(\epsilon^{-2})$ bits.\footnote{The $\tilde{O}$ and $\tilde{\Omega}$ notations suppress poly-logarithmic factors in $n, \log \epsilon^{-1}, \norm{f}_{\infty}$ and $\log \delta^{-1}$, where, $\delta$ is the error probability (for randomized algorithm).} We show that any deterministic algorithm for this problem requires space $\Omega(\epsilon^{-2} (\log \norm{f}_1))$ bits., Comment: Revised version

The paper has been withdrawn due to an error in Lemma 1., Comment: Withdrawn

We present the \crprecis structure, that is a general-purpose, deterministic and sub-linear data structure for summarizing \emph{update} data streams. The \crprecis structure yields the \emph{first deterministic sub-linear space/time algorithms for update streams} for answering a variety of fundamental stream queries, such as, (a) point queries, (b) range queries, (c) finding approximate frequent items, (d) finding approximate quantiles, (e) finding approximate hierarchical heavy hitters, (f) estimating inner-products, (g) near-optimal $B$-bucket histograms, etc.., 11 pages

Biology researchers have a pressing need for data management technologies which will make the storage and retrieval of DNA and protein sequence data accurate and efficient. The volume of data generated by DNA sequencing is already massive and will continue to grow rapidly. Even if the current sequence databases are adequate today, they most assuredly will become inadequate in the future when far more sequence data has been determined. The direction of future research in sequence databases needs to be in the organization of information. This is so that the volume of data needing to be searched does not grow linearly with the volume of sequence data being discovered. We propose to develop an index structure and retrieval system called PROXIMAL for biological sequence databases which promises to be efficient and general. This organization of the databases will complement other current efforts at sequence comparison and analysis, by providing an infrastructure in which other methods can be used to efficiently locate desired sequences. Our method relies on the use of reference strings to partition the database of sequences. It is efficient since the use of multiple reference strings for any given distance measure greatly reduces the number of sequences that must be examined, allowing us to quickly locate sequences based on a precomputed metric. It is general since multiple distance measures can be used. These include at least differing gap and mismatch weights for the basic edit distance calculation, or entirely different models of mutation. The only requirement is that there is a metric structure - mainly, that the calculations satisfy the triangle inequality. This is a weak requirement that is satisfied by many interesting measures, including those currently in wide use for sequence comparison.

We consider the issue of optimizing queries for distributed processing in mobile environment. An interesting characteristic of mobile machines is that they depend on battery as a source of energy which may not be substantial enough. Hence, the appropriate optimization criterion in a mobile environment considers both resource utilization and energy consum- ption at the mobile client. In this scenario, the optimal plan for a query depends on the residual battery level of the mobile client and the load at the server. We approach this problem by compiling a query into a sequence of candidate plans, such that for any state of the client-server system, the optimal plan is one of the candidate plans. A general solution is proposed by adapting the partial order dynamic programming search algorithm (p.o dp) such that the coverset of the query is the set of candidate plans. We propose two novel algorithms, namely, the linear combinations algorithm and the linearset algorithm (referred to as the linear algorithms) that compute the linearset of a query. The linear- set of a query is an approximation to the coverset returned by p.o. dp. We show, by means of simulation, that (1) the linearset is an excellent approximation of the coverset, (2) query compilation using the linear algorithms outperform query compilation using p.o. dp by factors ranging from 2 to 9, (3) the time taken to compile queries using the linear algorithms for the general optimization criterion is at most twice the time taken by a System R* like standard query optimizer search algorithm, and (4) the run time overhead incurred by the linear algorithms technique is minimal. The techniques presented in the paper are of general applicability to multi-criterion optimization problems in distributed databases, where each criterion is an additive metric.

We consider the problem of optimizing select-project-join relational queries for minimum response time on parallel machines. The design of the optimizer is based on three ideas: (1) the concept and quantification of degree of coarse grain parallelism for an execution tree, (2) the design of a parallelizing scheduler for a tree of coarse grain operations which is provably near optimal, and (3) the analysis of the scheduling algorithm to obtain a cost formula for parallel execution time. The search algorithm of the optimizer is presented as a multi-dimensional dynamic programming algorithm. We present two three- dimensional search algorithms for the case when placement of relations in the parallel machine do not overlap. We propose the tree placement strategy and demonstrate, by means of examples, how the number of dimensions in the search can be significantly reduced, thereby increasing the efficiency of the search algorithm.

Summarization: There is growing interest in algorithms for processing and querying continuous data streams (i.e., data that is seen only once in a fixed order) with limited memory resources. Providing (perhaps approximate) answers to queries over such streams is a crucial requirement for many application environments; examples include large IP network installations where performance data from different parts of the network needs to be continuously collected and analyzed. The ability to estimate the number of distinct (sub)tuples in the result of a join operation correlating two data streams (i.e., the cardinality of a projection with duplicate elimination over a join) is an important requirement for several data-analysis scenarios. For instance, to enable real-time traffic analysis and load balancing, a network-monitoring application may need to estimate the number of distinct (source, destination) IP-address pairs occurring in the stream of IP packets observed by router R1, where the source address is also seen in packets routed through a different router R2. Earlier work has presented solutions to the individual problems of distinct counting and join-size estimation (without duplicate elimination) over streams. These solutions, however, are fundamentally different and extending or combining them to handle our more complex “Join-Distinct” estimation problem is far from obvious. In this paper, we propose the first space-efficient algorithmic solution to the general Join-Distinct estimation problem over continuous data streams (our techniques can actually handle general update streams comprising tuple deletions as well as insertions). Our estimators are probabilistic in nature and rely on novel algorithms for building and combining a new class of hash-based synopses (termed “JD sketches”) for individual update streams. We demonstrate that our algorithms can provide low error, high-confidence Join-Distinct estimates using only small space and small processing time per update. In fact, we present lower bounds showing that the space usage of our estimators is within small factors of the best possible for the Join-Distinct problem. Preliminary experimental results verify the effectiveness of our approach. Presented on

The issue of policing lies at the heart of the Hukbalahap Rebellion (1948–1954), in large part because the indiscriminate and heavy-handed tactics employed by the country’s national police force, the Philippine Constabulary (PC), was a leading factor driving support for the Huk movement. A key turning point in the campaign came with the reform and reorganization of the PC, as a result of which the bulk of the PC’s personnel were transferred into the Army, which was given the lead for the COIN campaign. Although the idea of a military-led COIN campaign, with the police in a supporting role, would appear to run counter to the assumptions that inspired this volume, the example of the Philippine Constabulary illustrates the damage that an ineffectual police agency can do in counterinsurgency and the lengths that a country may have to go to ameliorate the situation.