Unconditionally Secure Oblivious Transfer from Real Network Behavior

Secure multi-party computation (MPC) deals with the problem of shared computation between parties that do not trust each other: they are interested in performing a joint task, but they also want to keep their respective inputs private. In a world where an ever-increasing amount of computation is outsourced, for example to the cloud, MPC is a subject of crucial importance. However, unconditionally secure MPC protocols have never found practical application: the lack of realistic noisy channel models, that are required to achieve security against computationally unbounded adversaries, prevents implementation over real-world, standard communication protocols. In this paper we show for the first time that the inherent noise of wireless communication can be used to build multi-party protocols that are secure in the information-theoretic setting. In order to do so, we propose a new noisy channel, the Delaying-Erasing Channel (DEC), that models network communication in both wired and wireless contexts. This channel integrates erasures and delays as sources of noise, and models reordered, lost and corrupt packets. We provide a protocol that uses the properties of the DEC to achieve Oblivious Transfer (OT), a fundamental primitive in cryptography that implies any secure computation. In order to show that the DEC reflects the behavior of wireless communication, we run an experiment over a 802.11n wireless link, and gather extensive experimental evidence supporting our claim. We also analyze the collected data in order to estimate the level of security that such a network can provide in our model. We show the flexibility of our construction by choosing for our implementation of OT a standard communication protocol, the Real-time Transport Protocol (RTP). Since the RTP is used in a number of multimedia streaming and teleconference applications, we can imagine a wide variety of practical uses and application settings for our construction

Sphagnum physiology in the context of changing climate: emergent influences of genomics, modelling and host-microbiome interactions on understanding ecosystem function.

Peatlands harbour more than one-third of terrestrial carbon leading to the argument that the bryophytes, as major components of peatland ecosystems, store more organic carbon in soils than any other collective plant taxa. Plants of the genus Sphagnum are important components of peatland ecosystems and are potentially vulnerable to changing climatic conditions. However, the response of Sphagnum to rising temperatures, elevated CO2 and shifts in local hydrology have yet to be fully characterized. In this review, we examine Sphagnum biology and ecology and explore the role of this group of keystone species and its associated microbiome in carbon and nitrogen cycling using literature review and model simulations. Several issues are highlighted including the consequences of a variable environment on plant-microbiome interactions, uncertainty associated with CO2 diffusion resistances and the relationship between fixed N and that partitioned to the photosynthetic apparatus. We note that the Sphagnum fallax genome is currently being sequenced and outline potential applications of population-level genomics and corresponding plant photosynthesis and microbial metabolic modelling techniques. We highlight Sphagnum as a model organism to explore ecosystem response to a changing climate and to define the role that Sphagnum can play at the intersection of physiology, genetics and functional genomics

Implementing Information-Theoretically Secure Oblivious Transfer from Packet Reordering

If we assume that adversaries have unlimited computational capabilities, secure computation between mutually distrusting players can not be achieved using an error-free communication medium. However, secure multi-party computation becomes possible when a noisy channel is available to the parties. For instance, the Binary Symmetric Channel (BSC) has been used to implement Oblivious Transfer (OT), a fundamental primitive in secure multi-party computation. Current research is aimed at designing protocols based on real-world noise sources, in order to make the actual use of information-theoretically secure computation a more realistic prospect for the future. In this paper, we introduce a modified version of the recently proposed Binary Discrete-time Delaying Channel (BDDC), a noisy channel based on communication delays. We call our variant Reordering Channel (RC), and we show that it successfully models packet reordering, the common behavior of packet switching networks that results in the reordering of the packets in a stream during their transit over the network. We also show that the protocol implementing oblivious transfer on the BDDC can be adapted to the new channel by using a different sending strategy, and we provide a functioning implementation of this modified protocol. Finally, we present strong experimental evidence that reordering occurrences between two remote Internet hosts are enough for our construction to achieve statistical security against honest-but-curious adversaries

Oak forest carbon and water simulations:Model intercomparisons and evaluations against independent data

Models represent our primary method for integration of small-scale, process-level phenomena into a comprehensive description of forest-stand or ecosystem function. They also represent a key method for testing hypotheses about the response of forest ecosystems to multiple changing environmental conditions. This paper describes the evaluation of 13 stand-level models varying in their spatial, mechanistic, and temporal complexity for their ability to capture intra- and interannual components of the water and carbon cycle for an upland, oak-dominated forest of eastern Tennessee. Comparisons between model simulations and observations were conducted for hourly, daily, and annual time steps. Data for the comparisons were obtained from a wide range of methods including: eddy covariance, sapflow, chamber-based soil respiration, biometric estimates of stand-level net primary production and growth, and soil water content by time or frequency domain reflectometry. Response surfaces of carbon and water flux as a function of environmental drivers, and a variety of goodness-of-fit statistics (bias, absolute bias, and model efficiency) were used to judge model performance. A single model did not consistently perform the best at all time steps or for all variables considered. Intermodel comparisons showed good agreement for water cycle fluxes, but considerable disagreement among models for predicted carbon fluxes. The mean of all model outputs, however, was nearly always the best fit to the observations. Not surprisingly, models missing key forest components or processes, such as roots or modeled soil water content, were unable to provide accurate predictions of ecosystem responses to short-term drought phenomenon. Nevertheless, an inability to correctly capture short-term physiological processes under drought was not necessarily an indicator of poor annual water and carbon budget simulations. This is possible because droughts in the subject ecosystem were of short duration and therefore had a small cumulative impact. Models using hourly time steps and detailed mechanistic processes, and having a realistic spatial representation of the forest ecosystem provided the best predictions of observed data. Predictive ability of all models deteriorated under drought conditions, suggesting that further work is needed to evaluate and improve ecosystem model performance under unusual conditions, such as drought, that are a common focus of environmental change discussions

Decoupling with unitary approximate two-designs

Consider a bipartite system, of which one subsystem, A, undergoes a physical evolution separated from the other subsystem, R. One may ask under which conditions this evolution destroys all initial correlations between the subsystems A and R, i.e. decouples the subsystems. A quantitative answer to this question is provided by decoupling theorems, which have been developed recently in the area of quantum information theory. This paper builds on preceding work, which shows that decoupling is achieved if the evolution on A consists of a typical unitary, chosen with respect to the Haar measure, followed by a process that adds sufficient decoherence. Here, we prove a generalized decoupling theorem for the case where the unitary is chosen from an approximate two-design. A main implication of this result is that decoupling is physical, in the sense that it occurs already for short sequences of random two-body interactions, which can be modeled as efficient circuits. Our decoupling result is independent of the dimension of the R system, which shows that approximate 2-designs are appropriate for decoupling even if the dimension of this system is large.Comment: Published versio

Bispecific PD1-IL2v and anti-PD-L1 break tumor immunity resistance by enhancing stem-like tumor-reactive CD8⁺ T cells and reprogramming macrophages.

Immunotherapies have shown remarkable, albeit tumor-selective, therapeutic benefits in the clinic. Most patients respond transiently at best, highlighting the importance of understanding mechanisms underlying resistance. Herein, we evaluated the effects of the engineered immunocytokine PD1-IL2v in a mouse model of de novo pancreatic neuroendocrine cancer that is resistant to checkpoint and other immunotherapies. PD1-IL2v utilizes anti-PD-1 as a targeting moiety fused to an immuno-stimulatory IL-2 cytokine variant (IL2v) to precisely deliver IL2v to PD-1 <sup>+</sup> T cells in the tumor microenvironment. PD1-IL2v elicited substantial infiltration by stem-like CD8 <sup>+</sup> T cells, resulting in tumor regression and enhanced survival in mice. Combining anti-PD-L1 with PD1-IL2v sustained the response phase, improving therapeutic efficacy both by reprogramming immunosuppressive tumor-associated macrophages and enhancing T cell receptor (TCR) immune repertoire diversity. These data provide a rationale for clinical trials to evaluate the combination therapy of PD1-IL2v and anti-PD-L1, particularly in immunotherapy-resistant tumors infiltrated with PD-1 <sup>+</sup> stem-like T cells

Emerging Use of Gene Expression Microarrays in Plant Physiology

Microarrays have become an important technology for the global analysis of gene expression in humans, animals, plants, and microbes. Implemented in the context of a well-designed experiment, cDNA and oligonucleotide arrays can provide highthroughput, simultaneous analysis of transcript abundance for hundreds, if not thousands, of genes. However, despite widespread acceptance, the use of microarrays as a tool to better understand processes of interest to the plant physiologist is still being explored. To help illustrate current uses of microarrays in the plant sciences, several case studies that we believe demonstrate the emerging application of gene expression arrays in plant physiology were selected from among the many posters and presentations at the 2003 Plant and Animal Genome XI Conference. Based on this survey, microarrays are being used to assess gene expression in plants exposed to the experimental manipulation of air temperature, soil water content and aluminium concentration in the root zone. Analysis often includes characterizing transcript profiles for multiple post-treatment sampling periods and categorizing genes with common patterns of response using hierarchical clustering techniques. In addition, microarrays are also providing insights into developmental changes in gene expression associated with fibre and root elongation in cotton and maize, respectively. Technical and analytical limitations of microarrays are discussed and projects attempting to advance areas of microarray design and data analysis are highlighted. Finally, although much work remains, we conclude that microarrays are a valuable tool for the plant physiologist interested in the characterization and identification of individual genes and gene families with potential application in the fields of agriculture, horticulture and forestry

Modeling anaerobic soil organic carbon decomposition in Arctic polygon tundra: insights into soil geochemical influences on carbon mineralization

Rapid warming of Arctic ecosystems exposes soil organic matter (SOM) to accelerated microbial decomposition, potentially leading to increased emissions of carbon dioxide (CO2) and methane (CH4) that have a positive feedback on global warming. Current estimates of the magnitude and form of carbon emissions from Earth system models include significant uncertainties, partially due to the oversimplified representation of geochemical constraints on microbial decomposition. Here, we coupled modeling principles developed in different disciplines, including a thermodynamically based microbial growth model for methanogenesis and iron reduction, a pool-based model to represent upstream carbon transformations, and a humic ion-binding model for dynamic pH simulation to build a more versatile carbon decomposition model framework that can be applied to soils under varying redox conditions. This new model framework was parameterized and validated using synthesized anaerobic incubation data from permafrost-affected soils along a gradient of fine-scale thermal and hydrological variabilities across Arctic polygonal tundra. The model accurately simulated anaerobic CO2 production and its temperature sensitivity using data on labile carbon pools and fermentation rates as model constraints. CH4 production is strongly influenced by water content, pH, methanogen biomass, and presence of competing electron acceptors, resulting in high variability in its temperature sensitivity. This work provides new insights into the interactions of SOM pools, temperature increase, soil geochemical feedbacks, and resulting CO2 and CH4 production. The proposed anaerobic carbon decomposition framework presented here builds a mechanistic link between soil geochemistry and carbon mineralization, making it applicable over a wide range of soils under different environmental settings.</p

Large CO\u3csub\u3e2\u3c/sub\u3e and CH\u3csub\u3e4\u3c/sub\u3e emissions from polygonal tundra during spring thaw in northern Alaska

The few prethaw observations of tundra carbon fluxes suggest that there may be large spring releases, but little is known about the scale and underlying mechanisms of this phenomenon. To address these questions, we combined ecosystem eddy flux measurements from two towers near Barrow, Alaska, with mechanistic soil-core thawing experiment. During a 2 week period prior to snowmelt in 2014, large fluxes were measured, reducing net summer uptake of CO2 by 46% and adding 6% to cumulative CH4 emissions. Emission pulses were linked to unique rain-on-snow events enhancing soil cracking. Controlled laboratory experiment revealed that as surface ice thaws, an immediate, large pulse of trapped gases is emitted. These results suggest that the Arctic CO2 and CH4 spring pulse is a delayed release of biogenic gas production from the previous fall and that the pulse can be large enough to offset a significant fraction of the moderate Arctic tundra carbon sink

Induced pseudoscalar coupling of the proton weak interaction

The induced pseudoscalar coupling $g_p$ is the least well known of the weak coupling constants of the proton's charged--current interaction. Its size is dictated by chiral symmetry arguments, and its measurement represents an important test of quantum chromodynamics at low energies. During the past decade a large body of new data relevant to the coupling $g_p$ has been accumulated. This data includes measurements of radiative and non radiative muon capture on targets ranging from hydrogen and few--nucleon systems to complex nuclei. Herein the authors review the theoretical underpinnings of $g_p$, the experimental studies of $g_p$, and the procedures and uncertainties in extracting the coupling from data. Current puzzles are highlighted and future opportunities are discussed.Comment: 58 pages, Latex, Revtex4, prepared for Reviews of Modern Physic