151 research outputs found
Design and Evaluation of a Discrete Wavelet Transform Based Multi-Signal Receiver
General purpose receivers of today are designed with a broad bandwidth so that the receiver can accept a wide range of signal frequencies. These receivers usually accept one signal along with any interference that is included. To increase the signal detection capabilities of the wideband receiver, a design for a receiver that can detect two signals is needed. One of the requirements for this receiver is that the second weak signal needs to be processed in a timely manner so that the receiver can recognize it. To remedy the problem, a module was developed using wavelet-based techniques to remove spurs from the incoming signals to allow easier detection. The main basis for this concentration on wavelets comes from the way wavelets break down signals into portions (called resolutions) that allow easier determination of detail importance. Utilizing the multi-resolution attributes of the discrete wavelet transform, a way to remove signal spurs is made possible. When removing the signal noise from the signal, the two signal dynamic range of the system is increased, as this module is applied to multiple receiver systems for comparison of performance. Implementation of this system was originally done in C as well as MATLAB, but later is being implemented in VHDL with simulations done for verification of functionality
Foundation Model's Embedded Representations May Detect Distribution Shift
Distribution shifts between train and test datasets obscure our ability to
understand the generalization capacity of neural network models. This topic is
especially relevant given the success of pre-trained foundation models as
starting points for transfer learning (TL) models across tasks and contexts. We
present a case study for TL on a pre-trained GPT-2 model onto the Sentiment140
dataset for sentiment classification. We show that Sentiment140's test dataset
is not sampled from the same distribution as the training dataset , and
hence training on and measuring performance on does not actually
account for the model's generalization on sentiment classification.Comment: 14 pages, 8 figures, 5 table
Coverage and error models of protein-protein interaction data by directed graph analysis
Directed graph and multinomial error models were used to assess and characterize the error statistics in all published large-scale datasets for Saccharomyces cerevisia
Efficient kernel surrogates for neural network-based regression
Despite their immense promise in performing a variety of learning tasks, a
theoretical understanding of the effectiveness and limitations of Deep Neural
Networks (DNNs) has so far eluded practitioners. This is partly due to the
inability to determine the closed forms of the learned functions, making it
harder to assess their precise dependence on the training data and to study
their generalization properties on unseen datasets. Recent work has shown that
randomly initialized DNNs in the infinite width limit converge to kernel
machines relying on a Neural Tangent Kernel (NTK) with known closed form. These
results suggest, and experimental evidence corroborates, that empirical kernel
machines can also act as surrogates for finite width DNNs. The high
computational cost of assembling the full NTK, however, makes this approach
infeasible in practice, motivating the need for low-cost approximations. In the
current work, we study the performance of the Conjugate Kernel (CK), an
efficient approximation to the NTK that has been observed to yield fairly
similar results. For the regression problem of smooth functions and
classification using logistic regression, we show that the CK performance is
only marginally worse than that of the NTK and, in certain cases, is shown to
be superior. In particular, we establish bounds for the relative test losses,
verify them with numerical tests, and identify the regularity of the kernel as
the key determinant of performance. In addition to providing a theoretical
grounding for using CKs instead of NTKs, our framework provides insights into
understanding the robustness of the various approximants and suggests a recipe
for improving DNN accuracy inexpensively. We present a demonstration of this on
the foundation model GPT-2 by comparing its performance on a classification
task using a conventional approach and our prescription.Comment: 29 pages. software used to reach results available upon request,
approved for release by Pacific Northwest National Laborator
Minibatching Offers Improved Generalization Performance for Second Order Optimizers
Training deep neural networks (DNNs) used in modern machine learning is
computationally expensive. Machine learning scientists, therefore, rely on
stochastic first-order methods for training, coupled with significant
hand-tuning, to obtain good performance. To better understand performance
variability of different stochastic algorithms, including second-order methods,
we conduct an empirical study that treats performance as a response variable
across multiple training sessions of the same model. Using 2-factor Analysis of
Variance (ANOVA) with interactions, we show that batch size used during
training has a statistically significant effect on the peak accuracy of the
methods, and that full batch largely performed the worst. In addition, we found
that second-order optimizers (SOOs) generally exhibited significantly lower
variance at specific batch sizes, suggesting they may require less
hyperparameter tuning, leading to a reduced overall time to solution for model
training.Comment: 14 pages, 6 figures, 5 table
Global 1-Mbps Peer-Assisted Streaming: Fine-Grain Measurement of a Configurable Platform
Computational and Systems Biology Advances to Enable Bioagent-Agnostic Signatures
Enumerated threat agent lists have long driven biodefense priorities. The
global SARS-CoV-2 pandemic demonstrated the limitations of searching for known
threat agents as compared to a more agnostic approach. Recent technological
advances are enabling agent-agnostic biodefense, especially through the
integration of multi-modal observations of host-pathogen interactions directed
by a human immunological model. Although well-developed technical assays exist
for many aspects of human-pathogen interaction, the analytic methods and
pipelines to combine and holistically interpret the results of such assays are
immature and require further investments to exploit new technologies. In this
manuscript, we discuss potential immunologically based bioagent-agnostic
approaches and the computational tool gaps the community should prioritize
filling
A Colorimetric Chemosensor Based on a Nozoe Azulene That Detects Fluoride in Aqueous/Alcoholic Media
Colorimetry is an advantageous method for detecting fluoride in drinking water in a resource-limited context, e. g., in parts of the developing world where excess fluoride intake leads to harmful health effects. Here we report a selective colorimetric chemosensor for fluoride that employs an azulene as the reporter motif and a pinacolborane as the receptor motif. The chemosensor, NAz-6-Bpin, is prepared using the Nozoe azulene synthesis, which allows for its rapid and low-cost synthesis. The chemosensor gives a visually observable response to fluoride both in pure organic solvent and also in water/alcohol binary solvent mixtures
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Estimating survival in patients with gastrointestinal cancers and brain metastases: An update of the graded prognostic assessment for gastrointestinal cancers (GI-GPA).
BackgroundPatients with gastrointestinal cancers and brain metastases (BM) represent a unique and heterogeneous population. Our group previously published the Diagnosis-Specific Graded Prognostic Assessment (DS-GPA) for patients with GI cancers (GI-GPA) (1985-2007, n = 209). The purpose of this study is to update the GI-GPA based on a larger contemporary database.MethodsAn IRB-approved consortium database analysis was performed using a multi-institutional (18), multi-national (3) cohort of 792 patients with gastrointestinal (GI) cancers, with newly-diagnosed BM diagnosed between 1/1/2006 and 12/31/2017. Survival was measured from date of first treatment for BM. Multiple Cox regression was used to select and weight prognostic factors in proportion to their hazard ratios. These factors were incorporated into the updated GI-GPA.ResultsMedian survival (MS) varied widely by primary site and other prognostic factors. Four significant factors (KPS, age, extracranial metastases and number of BM) were used to formulate the updated GI-GPA. Overall MS for this cohort remains poor; 8 months. MS by GPA was 3, 7, 11 and 17 months for GPA 0-1, 1.5-2, 2.5-3.0 and 3.5-4.0, respectively. >30% present in the worst prognostic group (GI-GPA of ≤1.0).ConclusionsBrain metastases are not uncommon in GI cancer patients and MS varies widely among them. This updated GI-GPA index improves our ability to estimate survival for these patients and will be useful for therapy selection, end-of-life decision-making and stratification for future clinical trials. A user-friendly, free, on-line app to calculate the GPA score and estimate survival for an individual patient is available at brainmetgpa.com
Focused Ion Beam Microfabrication
Contains an introduction, reports on x research projects and a list of publications.Defense Advanced Research Projects Agency/U.S. Army Research Office Grant DAAL-03-92-G-0217National Science Foundation Grant ECS 89-21728Defense Advanced Research Projects Agency/U.S. Army Research Office (ASSERT Program) Grant DAAL03-92-G-0305Semiconductor Research CorporationNational Science Foundation Grant DMR 92-02633U.S. Army Research Office Grant DAAL03-90-G-0223U.S. Navy - Naval Research Laboratory/Micrion Contract M0877
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