Do Localization Methods Actually Localize Memorized Data in LLMs?

Large language models (LLMs) can memorize many pretrained sequences verbatim. This paper studies if we can locate a small set of neurons in LLMs responsible for memorizing a given sequence. While the concept of localization is often mentioned in prior work, methods for localization have never been systematically and directly evaluated; we address this with two benchmarking approaches. In our INJ Benchmark, we actively inject a piece of new information into a small subset of LLM weights and measure whether localization methods can identify these "ground truth" weights. In the DEL Benchmark, we study localization of pretrained data that LLMs have already memorized; while this setting lacks ground truth, we can still evaluate localization by measuring whether dropping out located neurons erases a memorized sequence from the model. We evaluate five localization methods on our two benchmarks, and both show similar rankings. All methods exhibit promising localization ability, especially for pruning-based methods, though the neurons they identify are not necessarily specific to a single memorized sequence

Linked optical and gene expression profiling of single cells at high-throughput.

Single-cell RNA sequencing has emerged as a powerful tool for characterizing cells, but not all phenotypes of interest can be observed through changes in gene expression. Linking sequencing with optical analysis has provided insight into the molecular basis of cellular function, but current approaches have limited throughput. Here, we present a high-throughput platform for linked optical and gene expression profiling of single cells. We demonstrate accurate fluorescence and gene expression measurements on thousands of cells in a single experiment. We use the platform to characterize DNA and RNA changes through the cell cycle and correlate antibody fluorescence with gene expression. The platform's ability to isolate rare cell subsets and perform multiple measurements, including fluorescence and sequencing-based analysis, holds potential for scalable multi-modal single-cell analysis

Wave Energy Converter Effects on the Nearshore Environment

This poster presents efforts to use predictive modeling tools to evaluate the impacts of Wave Energy Converter (WEC) arrays on nearshore processes, investigate effects of different WEC devices on wave propagation, examine wave propagation in the lee of a WEC array over different wave conditions, and accelerate the realization of commercial-scale wave power

A Magnetic Bead-Based Sensor for the Quantification of Multiple Prostate Cancer Biomarkers.

Novel biomarker assays and upgraded analytical tools are urgently needed to accurately discriminate benign prostatic hypertrophy (BPH) from prostate cancer (CaP). To address this unmet clinical need, we report a piezeoelectric/magnetic bead-based assay to quantitate prostate specific antigen (PSA; free and total), prostatic acid phosphatase, carbonic anhydrase 1 (CA1), osteonectin, IL-6 soluble receptor (IL-6sr), and spondin-2. We used the sensor to measure these seven proteins in serum samples from 120 benign prostate hypertrophy patients and 100 Gleason score 6 and 7 CaP using serum samples previously collected and banked. The results were analyzed with receiver operator characteristic curve analysis. There were significant differences between BPH and CaP patients in the PSA, CA1, and spondin-2 assays. The highest AUC discrimination was achieved with a spondin-2 OR free/total PSA operation--the area under the curve was 0.84 with a p value below 10(-6). Some of these data seem to contradict previous reports and highlight the importance of sample selection and proper assay building in the development of biomarker measurement schemes. This bead-based system offers important advantages in assay building including low cost, high throughput, and rapid identification of an optimal matched antibody pair

Deep learning as a tool for neural data analysis: Speech classification and cross-frequency coupling in human sensorimotor cortex.

A fundamental challenge in neuroscience is to understand what structure in the world is represented in spatially distributed patterns of neural activity from multiple single-trial measurements. This is often accomplished by learning a simple, linear transformations between neural features and features of the sensory stimuli or motor task. While successful in some early sensory processing areas, linear mappings are unlikely to be ideal tools for elucidating nonlinear, hierarchical representations of higher-order brain areas during complex tasks, such as the production of speech by humans. Here, we apply deep networks to predict produced speech syllables from a dataset of high gamma cortical surface electric potentials recorded from human sensorimotor cortex. We find that deep networks had higher decoding prediction accuracy compared to baseline models. Having established that deep networks extract more task relevant information from neural data sets relative to linear models (i.e., higher predictive accuracy), we next sought to demonstrate their utility as a data analysis tool for neuroscience. We first show that deep network's confusions revealed hierarchical latent structure in the neural data, which recapitulated the underlying articulatory nature of speech motor control. We next broadened the frequency features beyond high-gamma and identified a novel high-gamma-to-beta coupling during speech production. Finally, we used deep networks to compare task-relevant information in different neural frequency bands, and found that the high-gamma band contains the vast majority of information relevant for the speech prediction task, with little-to-no additional contribution from lower-frequency amplitudes. Together, these results demonstrate the utility of deep networks as a data analysis tool for basic and applied neuroscience

How the European Union Is Embracing Cross-border Telemedicine and what the U.S. State Medical Boards Can Learn From It

Despite the fact that there have been many advances in the field of telemedicine, the United States (U.S.) state and federal laws have not kept pace with these technological advancements and may operate as a barrier to growth in the field of telemedicine. On the other hand, the European Union (EU) has developed a robust legal framework for the practice of telemedicine. The aim of this research project is to evaluate what elements of the EU legal experience could be used to support efforts to better align telemedicine law with the practice of telemedicine in the U.S. Based on the 2015 EU Guidelines, by 2020, a French physician may be able to see a German patient online and have instant access to the patient’s medical record, automatically translated into the French language.1 The EU has prioritized the creation of a legal framework that fully supports cross-border telemedicine.2 As early as 2000, the EU broadened medical licensure requirements for telemedicine so that physicians licensed in one nation could provide telemedicine services to patients who reside in other EU nations without needing to obtain medical licenses from these nations.3 Furthermore, linguistic experts from several nations of the EU have been working together to develop ways of automatically translating and instantly delivering patient records to physicians as appropriate.1 The state medical boards of the U.S, however, have struggled with efforts designed to achieve similar legislative changes. In most states, physicians are required to be licensed in both the state where they practice and the state where the patient resides.4 For example, a Texas physician is required to obtain a Georgia medical license in advance of providing telemedicine care to a patient in Georgia to ensure that their services are legal and reimbursable by insurance.4 While some states now provide a telemedicine license or expedite multistate licensing, these measures are insufficient to support the widespread practice of interstate telemedicine.4 With current regulations, obtaining medical licenses in all 50 states for telemedicine practice is impractical and prohibitively expensive for healthcare providers and organizations. U.S. medical licensure requirements for telemedicine practice are comparable to EU regulations before 2000. Furthermore, U.S. telemedicine reimbursement regulations arbitrarily differ across state borders, and electronic medical record systems from various companies do not communicate properly with each other. At this time, physicians in the U.S. cannot retrieve patient records for unscheduled patient encounters in real-time unless the patient was previously treated using the same medical health records system, causing inconvenience to patients, treatment delays and duplicative medical testing.5 Similar to the European approach, we recommend that the state medical boards allow physicians licensed in one state to provide telemedicine services to patients in other states. Furthermore, we recommend collaboration among the state medical boards, industry leaders, and state legislatures to come up with uniform telemedicine reimbursement regulations and to design a uniform electronic medical record inter-operability standard to allow the U.S. telemedicine industry to keep abreast of the global developments in telemedicine

Remote Sensing and Geovisualization of Rock Slopes and Landslides

Over the past two decades, advances in remote sensing methods and technology have enabled larger and more sophisticated datasets to be collected. Due to these advances, the need to effectively and efficiently communicate and visualize data is becoming increasingly important. We demonstrate that the use of mixed- (MR) and virtual reality (VR) systems has provided very promising results, allowing the visualization of complex datasets with unprecedented levels of detail and user experience. However, as of today, such visualization techniques have been largely used for communication purposes, and limited applications have been developed to allow for data processing and collection, particularly within the engineering–geology field. In this paper, we demonstrate the potential use of MR and VR not only for the visualization of multi-sensor remote sensing data but also for the collection and analysis of geological data. In this paper, we present a conceptual workflow showing the approach used for the processing of remote sensing datasets and the subsequent visualization using MR and VR headsets. We demonstrate the use of computer applications built in-house to visualize datasets and numerical modelling results, and to perform rock core logging (XRCoreShack) and rock mass characterization (EasyMineXR). While important limitations still exist in terms of hardware capabilities, portability, and accessibility, the expected technological advances and cost reduction will ensure this technology forms a standard mapping and data analysis tool for future engineers and geoscientists

Proper Thermal Equilibration of Simulations with Drude Polarizable Models: Temperature Grouped Dual-Nosé-Hoover Thermostat

An explicit treatment of electronic polarization is critically important to accurate simulations of highly charged or interfacial systems. Compared to the iterative self consistent field (SCF) scheme, extended Lagrangian approaches are computationally more efficient for simulations that employ a polarizable force field. However, an appropriate thermostat must be chosen to minimize heat flow and ensure an equipartition of kinetic energy among all unconstrained system degrees of freedom. Here we investigate the effects of different thermostats on the simulation of condensed phase systems with the Drude polarizable force field using several examples that include water, NaCl/water, acetone and an ionic liquid (IL) BMIM⁺/BF⁻₄. We show that conventional dual-temperature thermostat schemes often suffer from violation of equipartitioning, leading to considerable errors in both static and dynamic properties. Heat flow from the real degrees of freedom to the Drude degrees of freedom leads to a steady temperature gradient and puts the system at an incorrect effective temperature. Systems with high-frequency internal degrees of freedom such as planar improper dihedrals or C-H bond stretches are most vulnerable; this issue has been largely overlooked in the literature due to the primary focus on simulations of rigid water molecules. We present a new temperature-grouped dual Nose-Hoover thermostat, where the molecular center of mass translations are assigned to a temperature group separated from the rest degrees of freedom. We demonstrate that this scheme predicts correct static and dynamic properties for all the systems tested here, regardless of the thermostat coupling strength. This new thermostat has been implemented into the GPU-accelerated OpenMM simulation package and maintains a significant speed up relative to the SCF scheme

Retrospective Cost Adaptive Flow Control Using a Dielectric Barrier Discharge Actuator with Parameter-Dependent Modeling

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90730/1/AIAA-2011-1302-798.pd