Bay Area Smart Growth Scorecard

The Bay Area Smart Growth Scorecard is a landmark assessment of the planning policies of all 110 cities and counties of the San Francisco Bay Area.Although a city's current development is apparent to anyone who visits it, the policies that guide a city's future development are not so obvious. The Smart Growth Scorecard provides the first view into these policies and the first comparison among them.The Smart Growth Scorecard evaluated 101 cities in seven policy areas:preventing sprawl; making sure parks are nearby; creating homes people can afford; encouraging a mix of uses; encouraging density in the right places; requiring less land for parking; defining standards for good development. On average, Bay Area cities scored 34% (of a possible 100%), meaning cities are doing only a third of what they could be to achieve smart growth.The Smart Growth Scorecard evaluated eight counties (San Francisco is treated as a city) in five policy areas:managing growth; permanently protecting open space; preserving agricultural land; conserving natural resources; and offering transportation choices. On average, Bay Area counties scored 51%.The scores are low overall. But in every policy area, at least one city or county is doing well, whether it is a city that is encouraging walkable neighborhoods, or a county that is preserving its agricultural land. The Association of Bay Area Governments estimates that Bay Area will have one million additional residents by 2020; the Smart Growth Scorecard evaluates how well all the region's jurisdictions are planning for that growth, and how they can do better

Development of new processes to protect zinc against corrosion, suitable for on-site use

Protection against corrosion of metals is well known as an important issue in numerous fields. In all cases, the improvement of durability of these metals has to be connected to the development of environmentally friendly processes. Sol–gel protective coatings have shown excellent chemical stability and enhanced corrosion resistance for zinc substrates. Further, the sol–gel method, used as technique of surface protection, showed the potential for the replacement of toxic pre-treatments. This paper highlights the recent developments and applications of silane based sol–gel coatings on zinc substrates. Then, the challenges for industrial transfer of the developed process are also discussed because this process presents a disadvantage for on-site use, which is the too time-consuming thermal treatment. So, the goal of this study was to determine the convenient experimental conditions to reduce the duration of heat treatment of the hybrid sol–gel layer, compatible with the severe industrial requirements, without reducing the protection against corrosion. To reach this objective, a correlation between the results of chemical analyses and the protection against corrosion efficiency was established

Fault Injection and Safe-Error Attack for Extraction of Embedded Neural Network Models

Model extraction emerges as a critical security threat with attack vectors exploiting both algorithmic and implementation-based approaches. The main goal of an attacker is to steal as much information as possible about a protected victim model, so that he can mimic it with a substitute model, even with a limited access to similar training data. Recently, physical attacks such as fault injection have shown worrying efficiency against the integrity and confidentiality of embedded models. We focus on embedded deep neural network models on 32-bit microcontrollers, a widespread family of hardware platforms in IoT, and the use of a standard fault injection strategy - Safe Error Attack (SEA) - to perform a model extraction attack with an adversary having a limited access to training data. Since the attack strongly depends on the input queries, we propose a black-box approach to craft a successful attack set. For a classical convolutional neural network, we successfully recover at least 90% of the most significant bits with about 1500 crafted inputs. These information enable to efficiently train a substitute model, with only 8% of the training dataset, that reaches high fidelity and near identical accuracy level than the victim model.Comment: Accepted at SECAI Workshop, ESORICS 202

A Closer Look at Evaluating the Bit-Flip Attack Against Deep Neural Networks

Deep neural network models are massively deployed on a wide variety of hardware platforms. This results in the appearance of new attack vectors that significantly extend the standard attack surface, extensively studied by the adversarial machine learning community. One of the first attack that aims at drastically dropping the performance of a model, by targeting its parameters (weights) stored in memory, is the Bit-Flip Attack (BFA). In this work, we point out several evaluation challenges related to the BFA. First of all, the lack of an adversary's budget in the standard threat model is problematic, especially when dealing with physical attacks. Moreover, since the BFA presents critical variability, we discuss the influence of some training parameters and the importance of the model architecture. This work is the first to present the impact of the BFA against fully-connected architectures that present different behaviors compared to convolutional neural networks. These results highlight the importance of defining robust and sound evaluation methodologies to properly evaluate the dangers of parameter-based attacks as well as measure the real level of robustness offered by a defense.Comment: Extended version from IEEE IOLTS'2022 short pape

Evaluation of Parameter-based Attacks against Embedded Neural Networks with Laser Injection

Upcoming certification actions related to the security of machine learning (ML) based systems raise major evaluation challenges that are amplified by the large-scale deployment of models in many hardware platforms. Until recently, most of research works focused on API-based attacks that consider a ML model as a pure algorithmic abstraction. However, new implementation-based threats have been revealed, emphasizing the urgency to propose both practical and simulation-based methods to properly evaluate the robustness of models. A major concern is parameter-based attacks (such as the Bit-Flip Attack, BFA) that highlight the lack of robustness of typical deep neural network models when confronted by accurate and optimal alterations of their internal parameters stored in memory. Setting in a security testing purpose, this work practically reports, for the first time, a successful variant of the BFA on a 32-bit Cortex-M microcontroller using laser fault injection. It is a standard fault injection means for security evaluation, that enables to inject spatially and temporally accurate faults. To avoid unrealistic brute-force strategies, we show how simulations help selecting the most sensitive set of bits from the parameters taking into account the laser fault model.Comment: Accepted at 42nd International Conference on Computer Safety, Reliability and Security, SafeComp 202

Quaternary coastal uplift along the Talara Arc (Ecuador, Northern Peru) from new marine terrace data

Marine Geology, v. 228, n. 1-4, p. 73-91, 2006. http://dx.doi.org/10.1016/j.margeo.2006.01.004International audienceMarine terrace sequences have been investigated along the Talara Arc, a 1000-km-long stretch of the coast of Ecuador and northern Peru, characterized by subduction with a concave plan-view. Seven areas were investigated, evidencing flights of up to seven marine terraces with elevations reaching up to 360 m above mean sea level (amsl). Dating of the terraces was made using the Infra Red Stimulated Luminescence (IRSL) technique on sands as old as MIS 9 (∼330 ka). 14 C and U-series dates were obtained from fossil shells for geochronological cross control. Mean uplift rates along the Talara Arc range from about 0.10 up to 0.50 mm/ yr. The strongest uplift is observed in the Manta Peninsula of Ecuador in front of the subduction of the Carnegie Ridge. The uplift rate tends to slow down towards the northern and southern ends of the Talara Arc and then the transition toward the stable or subsiding coasts of central Peru and northern Ecuador and Colombia is sharp. The uplift appears to be homogeneous and related to 1) the map view curvature of the Arc, 2) the concave subduction pattern and 3) the Carnegie Ridge subduction

Phosphate release contributes to the rate-limiting step for unwinding by an RNA helicase

RNA helicases function in numerous aspects of RNA biology. These enzymes are RNA-stimulated ATPases that translocate on RNA and unwind or remodel structured RNA in an ATP-dependent fashion. How ATP and the ATPase cycle fuel the work performed by helicases is not completely clear. The hepatitis C virus RNA helicase, NS3, is an important model system for this class of enzymes. NS3 binding to a single-/double-strand RNA or DNA junction leads to ATP-independent melting of the duplex and formation of a complex capable of ATP-dependent unwinding by using a spring-loaded mechanism. We have established an RNA substrate for NS3 that can be unwound in a single sub-step. Our studies are consistent with a model in which a single ATP binding and/or hydrolysis event sets the unwinding spring and phosphate dissociation contributes to release of the spring, thereby driving the power stroke used for unwinding

Meiotic Regulation of TPX2 Protein Levels Governs Cell Cycle Progression in Mouse Oocytes

Formation of female gametes requires acentriolar spindle assembly during meiosis. Mitotic spindles organize from centrosomes and via local activation of the RanGTPase on chromosomes. Vertebrate oocytes present a RanGTP gradient centred on chromatin at all stages of meiotic maturation. However, this gradient is dispensable for assembly of the first meiotic spindle. To understand this meiosis I peculiarity, we studied TPX2, a Ran target, in mouse oocytes. Strikingly, TPX2 activity is controlled at the protein level through its accumulation from meiosis I to II. By RNAi depletion and live imaging, we show that TPX2 is required for spindle assembly via two distinct functions. It controls microtubule assembly and spindle pole integrity via the phosphorylation of TACC3, a regulator of MTOCs activity. We show that meiotic spindle formation in vivo depends on the regulation of at least a target of Ran, TPX2, rather than on the regulation of the RanGTP gradient itself

Characterizing the spatial distribution of multiple malaria diagnostic endpoints in a low-transmission setting in Lao PDR.

The epidemiology of malaria changes as prevalence falls in low-transmission settings, with remaining infections becoming more difficult to detect and diagnose. At this stage active surveillance is critical to detect residual hotspots of transmission. However, diagnostic tools used in active surveillance generally only detect concurrent infections, and surveys may benefit from sensitive tools such as serological assays. Serology can be used to interrogate and characterize individuals' previous exposure to malaria over longer durations, providing information essential to the detection of remaining foci of infection. We ran blood samples collected from a 2016 population-based survey in the low-transmission setting of northern Lao PDR on a multiplexed bead assay to characterize historic and recent exposures to Plasmodium falciparum and vivax. Using geostatistical methods and remote-sensing data we assessed the environmental and spatial associations with exposure, and created predictive maps of exposure within the study sites. We additionally linked the active surveillance PCR and serology data with passively collected surveillance data from health facility records. We aimed to highlight the added information which can be gained from serology as a tool in active surveillance surveys in low-transmission settings, and to identify priority areas for national surveillance programmes where malaria risk is higher. We also discuss the issues faced when linking malaria data from multiple sources using multiple diagnostic endpoints

Jean Briggs's Never in Anger as an Ethnography of Experience

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66542/2/10.1177_0308275X9301300406.pd