Event-related potentials elicited by spoken relative clauses

Sentence-length event-related potential (ERP) waveforms were obtained from 23 scalp sites as 24 subjects listened to normally spoken sentences of various syntactic structures. The critical materials consisted of 36 sentences each containing one of 2 types of relative clauses that differ in processing difficulty, namely Subject Object (SO) and Subject Subject (SS) relative clauses. Sentence-length ERPs showed several differences in the slow scalp potentials elicited by SO and SS sentences that were similar in their temporal dynamics to those elicited by the same stimuli in a word-by-word reading experiment, although the effects in the two modalities have non identical distributions. Just as for written sentences, there was a large, fronto-central negativity beginning at the linguistically defined "gap" in the SO sentences; this effect was largest for listeners with above-median comprehension rates, and is hypothesized to index changes in on-line processing demands during comprehension

THE 2001 SUPERMARKET PANEL ANNUAL REPORT

The Supermarket Panel collects data annually from individual supermarkets on store characteristics, operations, and performance. It was established in 1998 by the Food Industry Center as the basis for ongoing study of the supermarket industry. The Panel is unique because the unit of analysis is the individual store and the same stores are tracked over time. This makes it possible to analyze the processes by which new technologies, business practices, and competitive forces are changing the industry. The 2001 Supermarket Panel consists of 563 stores selected at random from the nearly 32,000 supermarkets in the U.S. or invited to participate through their affiliation with IGA. These 563 stores are located in forty-seven states and the District of Columbia. They are a representative cross section of the industry, including stores from all formats that belong to ownership groups ranging from single stores to the country's largest chains.Agribusiness, Industrial Organization, Marketing,

THE 2002 SUPERMARKET PANEL ANNUAL REPORT

The Supermarket Panel collects data annually from individual supermarkets on store characteristics, operations, and performance. It was established in 1998 by the Food Industry Center as the basis for ongoing study of the supermarket industry. The Panel is unique because the unit of analysis is the individual store and the same stores are tracked over time. This makes it possible to analyze the processes by which new technologies, business practices, and competitive forces are changing the industry. The 2002 Supermarket Panel consists of 866 stores selected at random from the nearly 32,000 supermarkets in the U.S. or invited to participate through their affiliation with cooperating retail companies or IGA. These 866 stores are located in forty-nine states. They are a representative cross section of the industry, including stores from all formats that belong to ownership groups ranging from single stores to the countrys largest chains.Industrial Organization, Marketing,

THE SUPERMARKET INDUSTRY AT THE START OF THE 21st CENTURY: KEY FINDINGS FROM THE 2000 SUPERMARKET PANEL

The 2000 Supermarket Panel gathered data on store characteristics, management practices, and operating performance from a representative, nation-wide sample of supermarkets. The Panel is unique because the unit of analysis is the individual store, and the same stores will be surveyed over time. Linking information on management practices and store and market characteristics with measures for key performance measures provides useful information for both strategic and tactical decisions. Descriptive findings are presented for stores groups by ownership group size and format. Results from a multivariate analysis of relationships between store performance and key performance drivers also are presented.Agribusiness,

Decoding Neural Activity to Assess Individual Latent State in Ecologically Valid Contexts

There exist very few ways to isolate cognitive processes, historically defined via highly controlled laboratory studies, in more ecologically valid contexts. Specifically, it remains unclear as to what extent patterns of neural activity observed under such constraints actually manifest outside the laboratory in a manner that can be used to make an accurate inference about the latent state, associated cognitive process, or proximal behavior of the individual. Improving our understanding of when and how specific patterns of neural activity manifest in ecologically valid scenarios would provide validation for laboratory-based approaches that study similar neural phenomena in isolation and meaningful insight into the latent states that occur during complex tasks. We argue that domain generalization methods from the brain-computer interface community have the potential to address this challenge. We previously used such an approach to decode phasic neural responses associated with visual target discrimination. Here, we extend that work to more tonic phenomena such as internal latent states. We use data from two highly controlled laboratory paradigms to train two separate domain-generalized models. We apply the trained models to an ecologically valid paradigm in which participants performed multiple, concurrent driving-related tasks. Using the pretrained models, we derive estimates of the underlying latent state and associated patterns of neural activity. Importantly, as the patterns of neural activity change along the axis defined by the original training data, we find changes in behavior and task performance consistent with the observations from the original, laboratory paradigms. We argue that these results lend ecological validity to those experimental designs and provide a methodology for understanding the relationship between observed neural activity and behavior during complex tasks

MAGICCARPET: Verified Detection and Recovery for Hardware-based Exploits

Abstract—MAGICCARPET is a new approach to defending systems against exploitable processor bugs. MAGICCARPET uses hardware to detect violations of invariants involving security-critical processor state and uses firmware to correctly push software’s state past the violations. The invariants are specified at run time. MAGICCARPET focuses on dynamically validating updates to security-critical processor state. In this work, (1) we generate correctness proofs for both MAGICCARPET hardware and firmware; (2) we prove that processor state and events never violate our security invariants at runtime; and (3) we show that MAGICCARPET copes with hardware-based exploits discovered post-fabrication using a combination of verified reconfigurations of invariants in the fabric and verified recoveries via reprogrammable software. We implement MAGICCARPET inside a popular open source processor on an FPGA platform. We evaluate MAGICCARPET using a diverse set of hardware-based attacks based on escaped and exploitable commercial processor bugs. MAGICCARPET is able to detect and recover from all tested attacks with no software run-time overhead in the attack-free case