Prepared for Bioterrorism Events? A Study of the Grain and Oilseed Sector

One of the most crucial problems facing the U.S. economy is the possibility of a terrorist attack on its food sector. The implications can be profound for its stakeholders, who are highly dependent on this sector for their economic livelihood as well as their food supplies. The U.S. Bioterrorism Act of 2002 was enacted to improve the ability of the United States to prevent, prepare for and respond to bioterrorism and other public health emergencies. One of the important features of the U.S. Bioterrorism Act of 2002 is its emphasis on prevention, a change from prior legislation that focused on punishments after an incidence had occurred. The U.S. Bioterrorism Act does not address food safety issues in general; its focus is to prevent intentional contamination. The objective of this study was to assess the preparedness to potential bioterrorism in the grain and oilseed sector based on facility security expenditures and history of security breaches. The study was conducted as a research activity under the multistate project NC-1016 “Economic Assessment of Changes in Trade Arrangements, Bio-terrorism Threats and Renewable Fuels Requirements on the U.S. Grain and Oilseed Sector.” In addition to assessing preparedness, the study investigated the relationship between adoption of security measures and breaches in facility security. Finally the study documents, for a small sample, the extent to which grain and oilseed facilities appear to be following regulations that implement the U.S. Bioterrorism Act.bioterrorism, grain and oilseed sector, Bioterrorism Act of 2002, NC-1016, Agribusiness, Agricultural and Food Policy, Marketing, Political Economy, I18, K23, Q13, Q18,

EXFI: a low cost Fault Injection System for embedded Microprocessor-based Boards

Evaluating the faulty behavior of low-cost embedded microprocessor-based boards is an increasingly important issue, due to their adoption in many safety critical systems. The architecture of a complete Fault Injection environment is proposed, integrating a module for generating a collapsed list of faults, and another for performing their injection and gathering the results. To address this issue, the paper describes a software-implemented Fault Injection approach based on the Trace Exception Mode available in most microprocessors. The authors describe EXFI, a prototypical system implementing the approach, and provide data about some sample benchmark applications. The main advantages of EXFI are the low cost, the good portability, and the high efficienc

Attitudes of College Students towards Agriculture, Food and the Role of Government

In 2002 and 2007 we surveyed Agribusiness students’ attitudes about agriculture, farming, food and agricultural policies. Responses were analyzed by year and student characteristics including farm background, citizenship and gender. Citizenship was a significant variable explaining differences in agreement with statements. Year and interactions with year were not significant.agricultural policy, farming, logistic regression, student attitudes, Agricultural and Food Policy, Teaching/Communication/Extension/Profession, A13, A22, C42, Q18,

Are visual cortex maps optimized for coverage?

The elegant regularity of maps of variables such as ocular dominance, orientation, and spatial frequency in primary visual cortex has prompted many people to suggest that their structure could be explained by an optimization principle. Up to now, the standard way to test this hypothesis has been to generate artificial maps by optimizing a hypothesized objective function and then to compare these artificial maps with real maps using a variety of quantitative criteria. If the artificial maps are similar to the real maps, this provides some evidence that the real cortex may be optimizing a similar function to the one hypothesized. Recently, a more direct method has been proposed for testing whether real maps represent local optima of an objective function (Swindale, Shoham, Grinvald, Bonhoeffer, & Hilbener, 2000). In this approach, the value of the hypothesized function is calculated for a real map, and then the real map is perturbed in certain ways and the function recalculated. If each of these perturbations leads to a worsening of the function, it is tempting to conclude that the real map is quite likely to represent a local optimum of that function. In this article, we argue that such perturbation results provide only weak evidence in favor of the optimization hypothesis

Inverse Ising inference using all the data

We show that a method based on logistic regression, using all the data, solves the inverse Ising problem far better than mean-field calculations relying only on sample pairwise correlation functions, while still computationally feasible for hundreds of nodes. The largest improvement in reconstruction occurs for strong interactions. Using two examples, a diluted Sherrington-Kirkpatrick model and a two-dimensional lattice, we also show that interaction topologies can be recovered from few samples with good accuracy and that the use of $l_1$-regularization is beneficial in this process, pushing inference abilities further into low-temperature regimes.Comment: 5 pages, 2 figures. Accepted versio

Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation

We introduce a new loss function for the weakly-supervised training of semantic image segmentation models based on three guiding principles: to seed with weak localization cues, to expand objects based on the information about which classes can occur in an image, and to constrain the segmentations to coincide with object boundaries. We show experimentally that training a deep convolutional neural network using the proposed loss function leads to substantially better segmentations than previous state-of-the-art methods on the challenging PASCAL VOC 2012 dataset. We furthermore give insight into the working mechanism of our method by a detailed experimental study that illustrates how the segmentation quality is affected by each term of the proposed loss function as well as their combinations.Comment: ECCV 201

Mantle-derived carbon in Hercynian granites. Stable isotopes signatures and C/He associations in the thermomineral waters, N-Portugal

Na–HCO3–CO2-rich thermomineral waters issue in the N of Portugal, within the Galicia-Trás-os-Montes region, linked to a major NNE-trending fault, the so-called Penacova-Régua-Verin megalineament. Along this tectonic structure different occurrences of CO2-rich thermomineral waters are found: Chaves hot waters (67 °C) and also several cold (16.1 °C) CO2-rich waters. The δ2H and δ18O values of the thermomineral waters are similar to those of the local meteoric waters. The chemical composition of both hot and cold mineral waters suggests that water–rock reactions are mainly controlled by the amount of dissolved CO2 (g) rather than by the water temperature. Stable carbon isotope data indicate an external CO2 inorganic origin for the gas. δ13CCO2 values ranging between −7.2‰ and −5.1‰ are consistent with a two-component mixture between crustal and mantle-derived CO2. Such an assumption is supported by the 3He/4He ratios measured in the gas phase, are between 0.89 and 2.68 times the atmospheric ratio (Ra). These ratios which are higher than that those expected for a pure crustal origin (≈0.02 Ra), indicating that 10 to 30% of the He has originated from the upper mantle. Release of deep-seated fluids having a mantle-derived component in a region without recent volcanic activity indicates that extensive neo-tectonic structures originating during the Alpine Orogeny are still active (i.e., the Chaves Depression)

Self-supervised video pretraining yields human-aligned visual representations

Humans learn powerful representations of objects and scenes by observing how they evolve over time. Yet, outside of specific tasks that require explicit temporal understanding, static image pretraining remains the dominant paradigm for learning visual foundation models. We question this mismatch, and ask whether video pretraining can yield visual representations that bear the hallmarks of human perception: generalisation across tasks, robustness to perturbations, and consistency with human judgements. To that end we propose a novel procedure for curating videos, and develop a contrastive framework which learns from the complex transformations therein. This simple paradigm for distilling knowledge from videos, called VITO, yields general representations that far outperform prior video pretraining methods on image understanding tasks, and image pretraining methods on video understanding tasks. Moreover, VITO representations are significantly more robust to natural and synthetic deformations than image-, video-, and adversarially-trained ones. Finally, VITO's predictions are strongly aligned with human judgements, surpassing models that were specifically trained for that purpose. Together, these results suggest that video pretraining could be a simple way of learning unified, robust, and human-aligned representations of the visual world.Comment: Technical repor