Scalable Microfabrication Procedures for Adhesive-Integrated Flexible and Stretchable Electronic Sensors.

New classes of ultrathin flexible and stretchable devices have changed the way modern electronics are designed to interact with their target systems. Though more and more novel technologies surface and steer the way we think about future electronics, there exists an unmet need in regards to optimizing the fabrication procedures for these devices so that large-scale industrial translation is realistic. This article presents an unconventional approach for facile microfabrication and processing of adhesive-peeled (AP) flexible sensors. By assembling AP sensors on a weakly-adhering substrate in an inverted fashion, we demonstrate a procedure with 50% reduced end-to-end processing time that achieves greater levels of fabrication yield. The methodology is used to demonstrate the fabrication of electrical and mechanical flexible and stretchable AP sensors that are peeled-off their carrier substrates by consumer adhesives. In using this approach, we outline the manner by which adhesion is maintained and buckling is reduced for gold film processing on polydimethylsiloxane substrates. In addition, we demonstrate the compatibility of our methodology with large-scale post-processing using a roll-to-roll approach

Adeno-Associated Virus Toolkit to Target Diverse Brain Cells

Recombinant adeno-associated viruses (AAVs) are commonly used gene delivery vehicles for neuroscience research. They have two engineerable features: the capsid (outer protein shell) and cargo (encapsulated genome). These features can be modified to enhance cell type or tissue tropism and control transgene expression, respectively. Several engineered AAV capsids with unique tropisms have been identified, including variants with enhanced central nervous system transduction, cell type specificity, and retrograde transport in neurons. Pairing these AAVs with modern gene regulatory elements and state-of-the-art reporter, sensor, and effector cargo enables highly specific transgene expression for anatomical and functional analyses of brain cells and circuits. Here, we discuss recent advances that provide a comprehensive (capsid and cargo) AAV toolkit for genetic access to molecularly defined brain cell types

Pretext Tasks selection for multitask self-supervised speech representation learning

Through solving pretext tasks, self-supervised learning leverages unlabeled data to extract useful latent representations replacing traditional input features in the downstream task. In audio/speech signal processing, a wide range of features where engineered through decades of research efforts. As it turns out, learning to predict such features (a.k.a pseudo-labels) has proven to be a particularly relevant pretext task, leading to useful self-supervised representations which prove to be effective for downstream tasks. However, methods and common practices for combining such pretext tasks for better performance on the downstream task have not been explored and understood properly. In fact, the process relies almost exclusively on a computationally heavy experimental procedure, which becomes intractable with the increase of the number of pretext tasks. This paper introduces a method to select a group of pretext tasks among a set of candidates. The method we propose estimates calibrated weights for the partial losses corresponding to the considered pretext tasks during the self-supervised training process. The experiments conducted on automatic speech recognition, speaker and emotion recognition validate our approach, as the groups selected and weighted with our method perform better than classic baselines, thus facilitating the selection and combination of relevant pseudo-labels for self-supervised representation learning

InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback

Humans write code in a fundamentally interactive manner and rely on constant execution feedback to correct errors, resolve ambiguities, and decompose tasks. While LLMs have recently exhibited promising coding capabilities, current coding benchmarks mostly consider a static instruction-to-code sequence transduction process, which has the potential for error propagation and a disconnect between the generated code and its final execution environment. To address this gap, we introduce InterCode, a lightweight, flexible, and easy-to-use framework of interactive coding as a standard reinforcement learning (RL) environment, with code as actions and execution feedback as observations. Our framework is language and platform agnostic, uses self-contained Docker environments to provide safe and reproducible execution, and is compatible out-of-the-box with traditional seq2seq coding methods, while enabling the development of new methods for interactive code generation. We use InterCode to create two interactive code environments with Bash and SQL as action spaces, leveraging data from the static Spider and NL2Bash datasets. We demonstrate InterCode's viability as a testbed by evaluating multiple state-of-the-art LLMs configured with different prompting strategies such as ReAct and Plan & Solve. Our results showcase the benefits of interactive code generation and demonstrate that InterCode can serve as a challenging benchmark for advancing code understanding and generation capabilities. InterCode is designed to be easily extensible and can even be used to incorporate new tasks such as Capture the Flag, a popular coding puzzle that is inherently multi-step and involves multiple programming languages. Project site with code and data: https://intercode-benchmark.github.ioComment: Project site with code and data: https://intercode-benchmark.github.i

Topics in construction safety and health : welding fumes : an interdisciplinary annotated bibliography

"These referenced articles provide literature on welding fume exposures among construction workers and its harmful health effects to them." - NIOSHTIC-2NIOSHTIC no. 20068260Production of this document was supported by cooperative agreement OH 009762 from the National Institute for Occupational Safety and Health (NIOSH). The contents are solely the responsibility of the authors and do not necessarily represent the official views of NIOSH.Welding-Fumes-annotated-bibliography.pdfcooperative agreement OH 009762 from the National Institute for Occupational Safety and Healt

Data-Driven Representation Learning in Multimodal Feature Fusion

abstract: Modern machine learning systems leverage data and features from multiple modalities to gain more predictive power. In most scenarios, the modalities are vastly different and the acquired data are heterogeneous in nature. Consequently, building highly effective fusion algorithms is at the core to achieve improved model robustness and inferencing performance. This dissertation focuses on the representation learning approaches as the fusion strategy. Specifically, the objective is to learn the shared latent representation which jointly exploit the structural information encoded in all modalities, such that a straightforward learning model can be adopted to obtain the prediction. We first consider sensor fusion, a typical multimodal fusion problem critical to building a pervasive computing platform. A systematic fusion technique is described to support both multiple sensors and descriptors for activity recognition. Targeted to learn the optimal combination of kernels, Multiple Kernel Learning (MKL) algorithms have been successfully applied to numerous fusion problems in computer vision etc. Utilizing the MKL formulation, next we describe an auto-context algorithm for learning image context via the fusion with low-level descriptors. Furthermore, a principled fusion algorithm using deep learning to optimize kernel machines is developed. By bridging deep architectures with kernel optimization, this approach leverages the benefits of both paradigms and is applied to a wide variety of fusion problems. In many real-world applications, the modalities exhibit highly specific data structures, such as time sequences and graphs, and consequently, special design of the learning architecture is needed. In order to improve the temporal modeling for multivariate sequences, we developed two architectures centered around attention models. A novel clinical time series analysis model is proposed for several critical problems in healthcare. Another model coupled with triplet ranking loss as metric learning framework is described to better solve speaker diarization. Compared to state-of-the-art recurrent networks, these attention-based multivariate analysis tools achieve improved performance while having a lower computational complexity. Finally, in order to perform community detection on multilayer graphs, a fusion algorithm is described to derive node embedding from word embedding techniques and also exploit the complementary relational information contained in each layer of the graph.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Engineering Systems to Study Cancer Metastasis.

Metastasis causes most late relapses and fatalities from cancer. Investigating mechanisms of metastasis and designing therapies to limit metastatic disease are challenging due to difficulties studying key events that occur before clinical detection, frequently involving small numbers of cells and intercellular interactions within three metastatic compartments (primary tumor, intravascular, and metastatic sites). By combining microfluidic, tissue engineering, and molecular imaging tools to screen therapies and analyze individual steps of metastasis, we further bridge the physiological gap between standard in vitro models, animal models, and human disease. Firstly, we model how multiple cell types form and respond to chemotactic gradients that drive cell migration in a primary tumor. Using microfluidic tools to robustly pattern chemokine CXCL12 secreting cells, CXCR7+ cells that scavenge this chemokine, and CXCR4+ cells that migrate towards resulting gradients, we performed sensitivity analysis to identify functional combinations that are refractory to therapeutic inhibition. We found one high-matrix binding isoform (CXCL12-γ) to robustly promote chemotaxis even at low chemokine levels in the absence of scavenging cells and in the presence of a clinically approved inhibitor. Linking these findings to clinical physiology, we found this high-matrix-binding isoform to only be expressed in late stage breast cancer. Secondly, we combine tissue engineering and molecular imaging tools to study the response of cancer cells in 3D tissue spheroids. We designed a platform to facilitate handling and high resolution imaging of 384 well spheroids. Using this platform we developed a bone marrow spheroid model to recreate breast cancer quiescence and resistance to therapies. Using dual-colored bioluminescence imaging we simultaneously measured response of quiescent cancer and bone stromal cells to standard cytotoxic and targeted therapies. Using this strategy we identified therapeutic combinations that selectively eliminated quiescent cancer cells in vitro and entirely eliminated bone marrow metastases in mice. We also used this system to visualize metabolic gradients in bone marrow spheroids and measure cancer and stromal response to metabolic perturbations. Combining microfluidic, tissue engineering, and molecular imaging tools as described herein can improve development of better models that recreate in vivo physiology and allow development of patient-specific therapies that prevent or eliminate metastatic disease.PhDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111509/1/spcavnar_1.pd