Scaffoldings For Policy Theories: Context Sensitive Rationality

Which rationality concept is adequate for policy theories? A Received View Rationality (RVR) concept is found in decision and policy theories. RVR is instrumental, formal and analytic (decomposing). The thesis that ideal RVR is adequate for policy theories can be defended either because RVR is context free rationality (CFR) and hence universally applicable, or because it is especially adequate for policy theories. The first defense is rejected as a corollary of an Arrovian Meta-Theoretic Impossibility Theorem (AMTIT). AMTIT claims that a context-free choice theory adequate for finite multi-dimensional choice structures is impossible. If at all, rationality can be theorized by a multitude of concepts each adequate for some context (or domain). The second defense is rejected following a detailed analysis of the current situation in the policy sciences literature. The case of the Strategic Air Command Basing Study is analyzed and insights are drawn from it concerning its rationality. RVR is not capable to account for the rationality of this case. The case involved a change in the investigated problem design of new objectives and alternatives and the emergence of a novel concept. Two families of extreme rationality concepts are articulated against the background of this case. Context-Free Rationality (CFR) which is an idealization of RVR, and Context-Sensitive Rationality (CSR) stand at opposing poles in terms of ontology, methodology and orientation. The notion of a practice is explicated. A practice contains cycles of deliberation, action and product (these are called \u27conduct\u27) and results. Over and above such cycles there are superstructures of traditions and institutions. A heuristic is explicated in contrast to an algorithm along twelve dimensions. The ontology of CSR is practices; its methodology is heuristics; its orientation is synthetic synergistic. The content of the appropriateness of CSR is inherently dependent on states of knowledge and thus it cannot be foretold. The normative force of CSR is related to its expression of that possible intervention of human reason with some particular practice by which the current potential for directing and controlling the practice is exhausted and at the same time a continuous effort is made to enlarge that potential. CSR captures the rationality of the case study and enables conceptualization of policies

RESTORATION OF IMMUNE COMPETENCE IN TOLERANT MICE BY PARABIOSIS TO NORMAL MICE

These studies demonstrate that mice tolerant to human gamma globulin (HGG) regain their ability to make antibody to HGG after parabiosis to normal mice. This can be demonstrated by enumeration of PFC in the spleens of both the normal and tolerant partners. Hemagglutinin titers of normal-tolerant parabionts, however, are exceptionally low; serum antibody appears to be neutralized by circulating HGG present originally in the serum of the tolerant partner. These data support the hypothesis that tolerance to HGG in mice is a "defective" state due to the absence of cells capable of responding to this antigen

Online Dependent Rounding Schemes

We study the abstract problem of rounding fractional bipartite $b$-matchings online. The input to the problem is an unknown fractional bipartite $b$-matching, exposed node-by-node on one side. The objective is to maximize the \emph{rounding ratio} of the output matching $\mathcal{M}$, which is the minimum over all fractional $b$-matchings $\mathbf{x}$, and edges $e$, of the ratio $\Pr[e\in \mathcal{M}]/x_e$. In offline settings, many dependent rounding schemes achieving a ratio of one and strong negative correlation properties are known (e.g., Gandhi et al., J.ACM'06 and Chekuri et al., FOCS'10), and have found numerous applications. Motivated by online applications, we present \emph{online dependent-rounding schemes} (ODRSes) for $b$-matching. For the special case of uniform matroids (single offline node), we present a simple online algorithm with a rounding ratio of one. Interestingly, we show that our algorithm yields \emph{the same distribution} as its classic offline counterpart, pivotal sampling (Srinivasan, FOCS'01), and so inherits the latter's strong correlation properties. In arbitrary bipartite graphs, an online rounding ratio of one is impossible, and we show that a combination of our uniform matroid ODRS with repeated invocations of \emph{offline} contention resolution schemes (CRSes) yields a rounding ratio of $1-1/e\approx 0.632$. Our main technical contribution is an ODRS breaking this pervasive bound, yielding rounding ratios of $0.646$ and $0.652$ for $b$-matchings and simple matchings, respectively. We obtain these results by grouping nodes and using CRSes for negatively-correlated distributions, together with a new method we call \emph{group discount and individual markup}, analyzed using the theory of negative association. We present a number of applications of our ODRSes to online edge coloring, several stochastic optimization problems, and algorithmic fairness

A Randomness Threshold for Online Bipartite Matching, via Lossless Online Rounding

Over three decades ago, Karp, Vazirani and Vazirani (STOC'90) introduced the online bipartite matching problem. They observed that deterministic algorithms' competitive ratio for this problem is no greater than $1/2$, and proved that randomized algorithms can do better. A natural question thus arises: \emph{how random is random}? i.e., how much randomness is needed to outperform deterministic algorithms? The \textsc{ranking} algorithm of Karp et al.~requires $\tilde{O}(n)$ random bits, which, ignoring polylog terms, remained unimproved. On the other hand, Pena and Borodin (TCS'19) established a lower bound of $(1-o(1))\log\log n$ random bits for any $1/2+\Omega(1)$ competitive ratio. We close this doubly-exponential gap, proving that, surprisingly, the lower bound is tight. In fact, we prove a \emph{sharp threshold} of $(1\pm o(1))\log\log n$ random bits for the randomness necessary and sufficient to outperform deterministic algorithms for this problem, as well as its vertex-weighted generalization. This implies the same threshold for the advice complexity (nondeterminism) of these problems. Similar to recent breakthroughs in the online matching literature, for edge-weighted matching (Fahrbach et al.~FOCS'20) and adwords (Huang et al.~FOCS'20), our algorithms break the barrier of $1/2$ by randomizing matching choices over two neighbors. Unlike these works, our approach does not rely on the recently-introduced OCS machinery, nor the more established randomized primal-dual method. Instead, our work revisits a highly-successful online design technique, which was nonetheless under-utilized in the area of online matching, namely (lossless) online rounding of fractional algorithms. While this technique is known to be hopeless for online matching in general, we show that it is nonetheless applicable to carefully designed fractional algorithms with additional (non-convex) constraints

Tight Bounds for Online Weighted Tree Augmentation

The Weighted Tree Augmentation problem (WTAP) is a fundamental problem in network design. In this paper, we consider this problem in the online setting. We are given an n-vertex spanning tree T and an additional set L of edges (called links) with costs. Then, terminal pairs arrive one-by-one and our task is to maintain a low-cost subset of links F such that every terminal pair that has arrived so far is 2-edge-connected in T cup F. This online problem was first studied by Gupta, Krishnaswamy and Ravi (SICOMP 2012) who used it as a subroutine for the online survivable network design problem. They gave a deterministic O(log^2 n)-competitive algorithm and showed an Omega(log n) lower bound on the competitive ratio of randomized algorithms. The case when T is a path is also interesting: it is exactly the online interval set cover problem, which also captures as a special case the parking permit problem studied by Meyerson (FOCS 2005). The contribution of this paper is to give tight results for online weighted tree and path augmentation problems. The main result of this work is a deterministic O(log n)-competitive algorithm for online WTAP, which is tight up to constant factors

Resistance to Timing Attacks for Sampling and Privacy Preserving Schemes

Side channel attacks, and in particular timing attacks, are a fundamental obstacle for secure implementation of algorithms and cryptographic protocols. These attacks and countermeasures have been widely researched for decades. We offer a new perspective on resistance to timing attacks. We focus on sampling algorithms and their application to differential privacy. We define sampling algorithms that do not reveal information about the sampled output through their running time. More specifically: (1) We characterize the distributions that can be sampled from in a "time oblivious" way, meaning that the running time does not leak any information about the output. We provide an optimal algorithm in terms of randomness used to sample for these distributions. We give an example of an efficient randomized algorithm ? such that there is no subexponential algorithm with the same output as ? that does not reveal information on the output or the input, therefore we show leaking information on either the input or the output is unavoidable. (2) We consider the impact of timing attacks on (pure) differential privacy mechanisms. It turns out that if the range of the mechanism is unbounded, such as counting, then any time oblivious pure DP mechanism must give a useless output with constant probability (the constant is mechanism dependent) and must have infinite expected running time. We show that up to this limitations it is possible to transform any pure DP mechanism into a time oblivious one

Towards Knowledge in the Cloud

Knowledge in the form of semantic data is becoming more and more ubiquitous, and the need for scalable, dynamic systems to support collaborative work with such distributed, heterogeneous knowledge arises. We extend the “data in the cloud” approach that is emerging today to “knowledge in the cloud”, with support for handling semantic information, organizing and finding it efficiently and providing reasoning and quality support. Both the life sciences and emergency response fields are identified as strong potential beneficiaries of having ”knowledge in the cloud”

Proline-rich tyrosine kinase 2 mediates gonadotropin-releasing hormone signaling to a specific extracellularly regulated kinase-sensitive transcriptional locus in the luteinizing hormone beta-subunit gene

G protein-coupled receptor regulation of gene transcription primarily occurs through the phosphorylation of transcription factors by MAPKs. This requires transduction of an activating signal via scaffold proteins that can ultimately determine the outcome by binding signaling kinases and adapter proteins with effects on the target transcription factor and locus of activation. By investigating these mechanisms, we have elucidated how pituitary gonadotrope cells decode an input GnRH signal into coherent transcriptional output from the LH β-subunit gene promoter. We show that GnRH activates c-Src and multiple members of the MAPK family, c-Jun NH(2)-terminal kinase 1/2, p38MAPK, and ERK1/2. Using dominant-negative point mutations and chemical inhibitors, we identified that calcium-dependent proline-rich tyrosine kinase 2 specifically acts as a scaffold for a focal adhesion/cytoskeleton-dependent complex comprised of c-Src, Grb2, and mSos that translocates an ERK-activating signal to the nucleus. The locus of action of ERK was specifically mapped to early growth response-1 (Egr-1) DNA binding sites within the LH β-subunit gene proximal promoter, which was also activated by p38MAPK, but not c-Jun NH(2)-terminal kinase 1/2. Egr-1 was confirmed as the transcription factor target of ERK and p38MAPK by blockade of protein expression, transcriptional activity, and DNA binding. We have identified a novel GnRH-activated proline-rich tyrosine kinase 2-dependent ERK-mediated signal transduction pathway that specifically regulates Egr-1 activation of the LH β-subunit proximal gene promoter, and thus provide insight into the molecular mechanisms required for differential regulation of gonadotropin gene expression