Multi-Shot Processing For Better Velocity Determination

We perform a technique called multi-shot processing on a section of 12-channel sonic logs in order to better resolve compressional and shear velocities. The data are from the ODP Leg 102 cruise, which occupied drill site 418A near the Bermuda Rise in 1985. Multi-shot processing has been done on a 9 meter section of this data, using different combinations of numbers of shots vs. numbers of receivers in an attempt to compare the vertical resolution and stability of this processing method. The method is stable only with certain shot-to-receiver subarray combinations. This paper demonstrates that the optimum combinations using this set of data are 4 shots with 6 receivers apiece, and 3 shots with 8 receivers each. While a combination using 5 shots with 4 receivers is possible, the method produces spurious results. This may be because of spatial aliasing over too few receivers, or it may be a result of poor outside control over the entire experiment (ship heave, etc.). It is hoped that an optimum subarray combination can be used to resolve velocities over shorter array lengths using the redundancy in the sonic data. This would result in a greater ability to characterize fracturing and alteration in the oceanic crust, since velocity variations have been shown to correlate with fracture zones.Massachusetts Institute of Technology. Full Waveform Acoustic Logging ConsortiumNational Science Foundation (U.S.) (Grant OCE89-00316

Discovering Latent Knowledge in Language Models Without Supervision

Existing techniques for training language models can be misaligned with the truth: if we train models with imitation learning, they may reproduce errors that humans make; if we train them to generate text that humans rate highly, they may output errors that human evaluators can't detect. We propose circumventing this issue by directly finding latent knowledge inside the internal activations of a language model in a purely unsupervised way. Specifically, we introduce a method for accurately answering yes-no questions given only unlabeled model activations. It works by finding a direction in activation space that satisfies logical consistency properties, such as that a statement and its negation have opposite truth values. We show that despite using no supervision and no model outputs, our method can recover diverse knowledge represented in large language models: across 6 models and 10 question-answering datasets, it outperforms zero-shot accuracy by 4\% on average. We also find that it cuts prompt sensitivity in half and continues to maintain high accuracy even when models are prompted to generate incorrect answers. Our results provide an initial step toward discovering what language models know, distinct from what they say, even when we don't have access to explicit ground truth labels.Comment: ICLR 202

Dennis Hopper's The Last Movie: Beginning of the End

The best film criticism heightens the viewer's pleasure by enriching his response to the film as a work of art. Thus, I am indebted to the pioneer critics Dennis DeNitto and William Herman, whose Film and the Critical ~(New York: Macmillan, 1975) provides a good model for archetypal analysis and which inspired the pattern used in the 11 explication11 section of this paper. I am also indebted to Northrop Frye, whose essay on 11 Archetypa1 Criticism: Theory of Myths 11 from Anatomy of Criticism introduced me to concepts that permeate the present work. I would like to thank Dennis Hopper, Stewart Stern, and Satya DelaManitou, whose gracious cooperation and assistance added a personal dimension to this study.Englis

Space Environments and Effects Concept: Transitioning Research to Operations and Applications

The National Aeronautics and Space Administration (NASA) is embarking on a course to expand human presence beyond Low Earth Orbit (LEO) while expanding its mission to explore the solar system. Destinations such as Near Earth Asteroids (NEA), Mars and its moons, and the outer planets are but a few of the mission targets. NASA has established numerous offices specializing in specific space environments disciplines that will serve to enable these missions. To complement these existing discipline offices, a concept focusing on the development of space environment and effects application is presented. This includes space climate, space weather, and natural and induced space environments. This space environment and effects application is composed of 4 topic areas; characterization and modeling, engineering effects, prediction and operation, and mitigation and avoidance. These topic areas are briefly described below. Characterization and modeling of space environments will primarily focus on utilization during Program mission concept, planning, and design phases. Engineering effects includes materials testing and flight experiments producing data to be used in mission planning and design phases. Prediction and operation pulls data from existing sources into decision-making tools and empirical data sets to be used during the operational phase of a mission. Mitigation and avoidance will develop techniques and strategies used in the design and operations phases of the mission. The goal of this space environment and effects application is to develop decision-making tools and engineering products to support the mission phases of mission concept through operations by focusing on transitioning research to operations. Products generated by this space environments and effects application are suitable for use in anomaly investigations. This paper will outline the four topic areas, describe the need, and discuss an organizational structure for this space environments and effects application

Characterizing the nonlinear interaction of S- and P-waves in a rock sample

The nonlinear elastic response of rocks is known to be caused by the rocks' microstructure, particularly cracks and fluids. This paper presents a method for characterizing the nonlinearity of rocks in a laboratory scale experiment with a unique configuration. This configuration has been designed to open up the possibility of using the nonlinear characterization of rocks as an imaging tool in the field. In our experiment, we study the nonlinear interaction of two traveling waves: a low-amplitude 500 kHz P-wave probe and a high-amplitude 50 kHz S-wave pump in a room-dry 15 × 15 × 3 cm slab of Berea sandstone. Changes in the arrival time of the P-wave probe as it passes through the perturbation created by the traveling S-wave pump were recorded. Waveforms were time gated to simulate a semi-infinite medium. The shear wave phase relative to the P-wave probe signal was varied with resultant changes in the P-wave probe arrival time of up to 100 ns, corresponding to a change in elastic properties of 0.2%. In order to estimate the strain in our sample, we also measured the particle velocity at the sample surface to scale a finite difference linear elastic simulation to estimate the complex strain field in the sample, on the order of 10−6, induced by the S-wave pump. We derived a fourth order elastic model to relate the changes in elasticity to the pump strain components. We recover quadratic and cubic nonlinear parameters: β̃=−872 and δ̃=−1.1×10,000,000,000 respectively, at room-temperature and when particle motions of the pump and probe waves are aligned. Temperature fluctuations are correlated to changes in the recovered values of β̃ and δ̃, and we find that the nonlinear parameter changes when the particle motions are orthogonal. No evidence of slow dynamics was seen in our measurements. The same experimental configuration, when applied to Lucite and aluminum, produced no measurable nonlinear effects. In summary, a method of selectively determining the local nonlinear characteristics of rock quantitatively has been demonstrated using traveling sound waves

Nonlinear interaction of seismic waves in the lab: A potential tool for characterizing pore structure and fluids

As more and more resources are extracted from unconventional reservoirs, an understanding of the microstructure of reservoir rocks is of increasing importance. Many conventional techniques struggle to sense variations in the micro-structure and pore-fluids of rock samples. The nonlinear coupling of two elastic waves is known to be sensitive to these parameters, however, and so is a natural candidate to improve our understanding of these structures. Here, we develop an experimental technique to sense the nonlinear interaction of two propagating waves: a strong S-wave pump that changes (minutely) the elastic properties of the sample and a weaker P-wave probe that senses those changes. By measuring the delay in the P-wave probe traveltime induced by the S-wave pump, we show that this signal is significant in a Berea sandstone sample and absent in Aluminum and Plexiglass samples. The polarization of the S-wave (particle motion aligned or perpendicular to the P-wave probe) has a large impact on the measured response; this is evidence that the signal we measure is sensitive to the micro-structure of the rock. We show that the method is sensitive to fluids by imaging the variations in two specific nonlinear parameters, caused by the introduction of fluid into a Berea sandstone sample

Kawasaki disease and ENSO-driven wind circulation

Kawasaki disease (KD) is the most common cause of acquired heart disease in children worldwide. Recently, a climatological study suggested that KD may be triggered by a windborne agent traveling across the north Pacific through the westerly wind flow prevailing at midlatitudes. Here we use KD records to describe the association between enhanced disease activity on opposite sides of the basin and different phases of the El Niño-Southern Oscillation (ENSO) phenomenon, via the linkage to these tropospheric winds. Results show that years with higher-than-normal KD cases in Japan preferentially occur during either El Niño Modoki or La Niña conditions, while in San Diego during the mature phase of El Niño or La Niña events. Given that ENSO offers a degree of predictability at lead times of 6 months, these modulations suggest that seasonal predictions of KD could be used to alert clinicians to periods of increased disease activity

Symplectic geometry and the uniqueness of Grauert tubes

((Without Abstract)).Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/41842/1/39-11-1-1_10110001.pd

Aligning AI With Shared Human Values

We show how to assess a language model's knowledge of basic concepts of morality. We introduce the ETHICS dataset, a new benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality. Models predict widespread moral judgments about diverse text scenarios. This requires connecting physical and social world knowledge to value judgements, a capability that may enable us to steer chatbot outputs or eventually regularize open-ended reinforcement learning agents. With the ETHICS dataset, we find that current language models have a promising but incomplete ability to predict basic human ethical judgements. Our work shows that progress can be made on machine ethics today, and it provides a steppingstone toward AI that is aligned with human values.Comment: ICLR 2021; the ETHICS dataset is available at https://github.com/hendrycks/ethics