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74 results for “hardware”
Artifacts for "On Hardware Security Bug Code Fixes By Querying Large Language Models""
<p>This repository contains the benchmarks and results obtained for the work "On Hardware Security Bug Code Fixes<br> By Querying Large Language Models".<br> Follow the README.md file for more information on how to use the tools yourself.</p>
Learning to rank quantum circuits for hardware-optimized performance enhancement
<p>These data were used in the work described in the manuscript <em>Learning to rank quantum circuits for hardware-optimized performance enhancement</em>, <a href="https://arxiv.org/abs/2411.03302">https://arxiv.org/abs/2411.03302</a>. </p> <p>Data was collected throughout a 9 month period. The data for each individual collection run is stored in a separate directory, for example <code>ibmq_guadalupe_2022_11_22_14_49_51</code>.</p> <p>The <code>jobs.pkl</code> file is a pickled dictionary that contains essentially all of the relevant data. Note that the dictionary includes Qiskit <code>QuantumCircuit</code> objects created with an older version of Qiskit, and as such an older Qiskit version must be used to load these files (version 0.43.0 works).</p> <ul> <li>The keys are integers indexing the circuit (0, 1, ..., n-1)</li> <li>The values are themselves dictionaries containing various data fields:</li> <li>'initial_circuit': the initial circuit, before transpilation or layout selection</li> <li>'num_qubits': the number of qubits used in the circuit</li> <li>'circuit_type': a string denoting the type of circuit (qaoa, bv, inv_qft, or clifford)</li> <li>'seed_compiler': the compiler PRNG seed</li> <li>'target_state': the target state for so-called one-hot or deterministic circuits</li> <li>'target_state_bin': the target state represented in binary</li> <li>'transpiled circuit': the transpiled circuit</li> <li>'deflated circuit': the deflated transpiled circuit (unused qubits are dropped)</li> <li>'layouts': a list of layouts (each represented by a list), determining the subgraph isomorphism</li> <li>'mm transpiled circuits (no dd)': the set of transpiled layouts (without dynamical decoupling)</li> <li>'mm transpiled circuits (dd)': the set of transpiled layouts (with dynamical decoupling)</li> <li>'job success': whether the job succeeded (True or False)</li> <li>'success fraction': the Hellinger fidelity, which equals success probability for one-hot circuits</li> <li>'mm fidelity': the Mapomatic score</li> <li>'T1 times': the single-qubit idle times</li> <li>'T1 fidelity': the T1 score</li> <li>'zz phases': the set of ZZ phase accumulations for two-qubit mutually-idle periods</li> <li>'zz fidelity': the ZZ fidelity</li> </ul>
Open source and open hardware: developing tools that are accessible and customisable
<p><strong>The following video describes how ROMI uses open source hardware to support communities through customisation and adaptation. Funded by EU Grant 773875.</strong></p> <p><em>Videos are available in:</em></p> <ul> <li>Hi-res (1080p Apple ProRes)</li> <li>Mid-res (1080p H265)</li> </ul> <p><strong>Video script:</strong></p> <p>(MINCHIN) A key aspect and component of the ROMI project is that it should be open source so there is no proprietary software there's no ownership over the designs in fact it's created to be hacked customised and adapted to any farm.<br> <br> (HANAPPE) One of the aspects of the ROMI project is open hardware and we started from scratch like buying a motor buying a motor-controller programming the motor controller and which is maybe different than many of the projects you might see more industrial projects who might rely more on say high-end standard components from from the industry but i think there's a real advantage in trying to build everything up is that first you understand everything you can explain everything you can fix anything you can you know document anything for you know DIY farmers that they you know if they want to replace one motor with another and understanding that all allows you to quickly experiment stuff without having to depend on someone else who's working with say a closed source component and it allows you to be quite reactive and of course in the end allows you to be fairly cost effective.<br> <br> (BAORI) One of the things we set out to do with this project was to make it accessible and the idea is that at the end of the project everything will be open source so from the frame to the software the designs the cad everything will be open source so if you're a maker if you're somebody who is an engineer and wants to build one of these robots yourself you'll be able to download everything including the software to be able to make it run and because we wanted it to be accessible we started out with one thought that was everything that we put into this rover should also be accessible.<br> <br> (CAMPRODON) So we are trying to bring that philosophy that is embedded within Fab Labs slash (/) maker movement at large the open hardware movement, of how you design for distributed manufacturing. Which means that you take design decisions on the way that you build things which are meant for others replicated in new laboratories using new machines and you do that on top of a network such as a Fab Lab network which is a robust yet heterogeneous.<br> <br> (BARBERAN) It seems like someone tinkered with it. The idea when we design this is to constrain ourselves to do it in a way that anyone else can build it easily. So we try to keep the type and the complexity of the tools we use as small and simple as we can, so we use 3d printers that today are really accessible, we use CNC machines that's maybe the the most complex machine we use but it was it was needed because we need to build something that can stand the weather in a harsh environment in a farm. So we use aluminium and plastic and the rest of the components are mostly commercial components so the the process of designing it is a common go between the computer and the farm and the fab lab we tried here with the cable on the office to hang the first versions of the of the cable bot then we come back to redesign the pieces and then we adjust the sizes of the pulleys and the springs and then we do another model. And so this ability to reiterate so fast is also provided by the simplicity of the tools we use. Using a really simple 3d printer to build a robot is allows you to do in one month several versions and iterations of the same robot.<br> <br> (HANAPPE) In the end our goal is sustainability it's not technology, and what is the best way for whatever we build to have an impact on on say farmers in Europe or wider, and if if what we do is good then we would like them to have access to these tools, and there's some sort of a confidence that then there will be some financing flowing back to us.<br> <br> (MINCHIN) When we open source this knowledge and these tools it allows for an abundance of researchers of practitioners to come together to find the solutions, we don't have to depend on a small development team so the the library of tools that are developed are really a public library for others to to use and to adapt.<br> <br> (BARBERAN) At the end I think the the most important part that we don't even realise because we just do it because we are on the on the inertia of doing it is that we are setting rocks for the future. At the end we are not making right now the the the best robot the best tool what we are doing is setting ground to build on the future the best robot and the best thing built by people not by us because we don't we are not farmers we are not going to design the best robot and we are not a big company with resources as crazy to to make the ultra high-tech thing. But we are able to do something to share with people and a lot of heads think better than a couple of heads you know. The the model that that linux a lot of people in the linux community use is we are dumber than them but we are hundreds of thousands. Hundreds of thousands of heads think better than 10 heads so being able to share that knowledge and to include people in in the development of their own tools i think is the best part on a mid and long term way of seeing this.</p>
Data for "Borealis: An Advanced Digital Hardware and Software Design for SuperDARN Radar Systems"
<p>Clyde River SuperDARN data for 2022-02-07, 18:00-20:00UT.</p> <p>Included are the RAWACF and FITACF files, the latter of which was used to produce Figure 6 in the partner paper: "Borealis: An Advanced Digital Hardware and Software Design for SuperDARN Radar Systems".</p> <p>Data can be processed further using the Radar Software Toolkit (RST: https://github.com/SuperDARN/rst), or plotted using pyDARN (https://github.com/SuperDARN/pydarn).</p>
Data Repository for Chip-Chat: Challenges and Opportunities in Conversational Hardware Design
<p><strong># Data Repository for Chip-Chat: Challenges and Opportunities in Conversational Hardware Design</strong></p><p>This repository accompanies the manuscript accepted at MLCAD 2023, titled "Chip-Chat: Challenges and Opportunities in Conversational Hardware Design".</p><p>It contains the following:</p><p>- `free-chat-gpt4-tt03` - this contains the data used for the paper, which examines free-form process when exploring the potential applications for LLMs in hardware design. The task here was to generate the Verilog for a full (albeit small) processor design. Here, the chats are presented (and annotated) in the `/chats` subdirectory, which also includes a python script for extracting metadata (presented in table IV in the manuscript). Note that this directory also includes `/assembler` which provides a basic assembler (also written in Python) to make it easier to write demo programs (examples included) for the processor.</p><p>- `scripted-benchmarks` - this contains additional data not used in the paper, which examines a more rigid process when exploring the potential applications for LLMs in hardware design. Here, each model chats are separated by subdirectory.</p><p>- `scripted-benchmarks-gpt4-tt03` - this contains just the benchmarks not used in the paper, made by the first run of GPT-4, which were used for tapeout in Tiny Tapeout 3.</p><p>The two tt03 directories contain the GitHub action scripts required to invoke OpenLane and produce synthesis files, as well as used to perform simulation tests.</p>
Hardware Preservation in Management of Infected Non United or Delayed United Fractures of Long or Short Bones by Debridement and Local Calcium Sulfate Loaded by Antibiotics
ClinicalTrials.gov study NCT05231148. IPD Sharing: YES. Countries: 1. Publications: 3.
Benefit of Surgical Hardware Removal Following Clavicle Fracture
ClinicalTrials.gov study NCT04343118. IPD Sharing: NO. Countries: 1. Publications: 1.
Long-term Outcome After Removal of Rib Stabilization Hardware in Patients With Blunt Chest Trauma
ClinicalTrials.gov study NCT06003595. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Healthy Lung Study- Development of 3T MRI Hardware and Software for 3He Gas Imaging of the Lung: Healthy Volunteer Development Study
ClinicalTrials.gov study NCT03169673. IPD Sharing: NO. Countries: 1. Publications: 1.
An open-source and easily replicable hardware for Electrical Impedance Tomography
<p>Supplementary materials of the paper.</p>
Result of Show View Updates to animation without hardware layers
<p>Result of Show View Updates to animation without hardware layers</p>
Open Hardware Tribometer "Tribofish"
<p>The data set contains all the CAD models, codes and construction plans required to build the open hardware tribometer kit "tribofish" published for the first time in the scientific journal "Wear". The contribution to this dataset is available under the folowing link: https://doi.org/10.1016/j.wear.2024.205437</p>
Ultra robust negative differential resistance memristor for hardware neuron circuit implementation
Open the record for dataset details and reuse information.
Binary Decision Diagrams on Modern Hardware
<p>The reproducibility artefact for the "Binary Decision Diagrams on Modern Hardware" paper.</p> <p> </p> <p>Follow the instructions in the README document. Depending on the concrete steps you want to reproduce, you may not need to download all files in the artefact. However, at the very least, you'll need either `bin.zip` (pre-compiled binaries) or `src.zip` (source code) and one of the benchmark datasets (`bdd-X-pruned.zip` and `benchmarks-X.tsv`).</p>
Using the IBM analog in-memory hardware acceleration kit for neural network training and inference - Supplementary Material
<p>Analog In-Memory Computing (AIMC) is a promising approach to reduce the latency and energy consumption of Deep Neural Network (DNN) inference and training. However, the noisy and non-linear device characteristics and the non-ideal peripheral circuitry in AIMC chips require adapting DNNs to be deployed on such hardware to achieve equivalent accuracy to digital computing. In this Tutorial, we provide a deep dive into how such adaptations can be achieved and evaluated using the recently released IBM Analog Hardware Acceleration Kit (AIHWKit), freely available at https://github.com/IBM/aihwkit. AIHWKit is a Python library that simulates inference and training of DNNs using AIMC. We present an in-depth description of the AIHWKit design, functionality, and best practices to properly perform inference and training. We also present an overview of the Analog AI Cloud Composer, a platform that provides the benefits of using the AIHWKit simulation in a fully managed cloud setting along with physical AIMC hardware access, freely available at https://aihw-composer.draco.res.ibm.com. Finally, we show examples of how users can expand and customize AIHWKit for their own needs. This Tutorial is accompanied by comprehensive Jupyter Notebook code examples that can be run using AIHWKit, which can be downloaded from <a href="https://github.com/IBM/aihwkit/tree/master/notebooks/tutorial" target="_blank" rel="noopener">https://github.com/IBM/aihwkit/tree/master/notebooks/tutorial</a>.</p> <p> </p>
Transcriptional analysis of soleus from mice preserved with the Rapid Freeze hardware
Data from the NASA Rodent Research-1 (RR-1) mission showed that gene-expression levels in mouse livers are different depending on what tissue preservation protocol is used and that slow freezing is not an effective method for preserving signals in gene-expression data. In response to these and other observations the Rapid Freeze hardware was built for use on the International Space Station. The Rapid Freeze hardware freezes mouse tissues (Glovebox freezer) and whole carcasses (Cryochiller) at rates closely mimicking those attained with immersion in liquid nitrogen. Because this hardware will be used extensively on future rodent research missions it is crucial to understand whether or not it preserves signals in gene expression data in order to maximize the value of these rare and expensive spaceflight experiments. Therefore this study was designed with three goals: 1) To evaluate the temperature profile of the Cryochiller and Glovebox freezer cartridges (Rapid Freeze hardware) over time during mock on-orbit procedures; 2) To determine the freezing profiles of tissues and carcasses using Rapid Freeze hardware at both optimal and sub-optimal temperatures (to mimic on-orbit operations) compared with those frozen in liquid nitrogen (the laboratory gold standard) or frozen at -80 C (the current standard method); 3) To identify gene expression changes in a) tissues that were frozen via the Glovebox freezer and b) tissues dissected from whole or partial carcasses that were frozen via the Cryochiller versus tissues that were frozen via control methods (liquid nitrogen or -80C slow freeze) to assess how the Rapid Freeze hardware compares with laboratory gold standard practices and our current standard methods.
Transcriptional analysis of livers from mice preserved with the Rapid Freeze hardware
Data from the NASA Rodent Research-1 (RR-1) mission showed that gene-expression levels in mouse livers are different depending on what tissue preservation protocol is used and that slow freezing is not an effective method for preserving signals in gene-expression data. In response to these and other observations the Rapid Freeze hardware was built for use on the International Space Station. The Rapid Freeze hardware freezes mouse tissues (Glovebox freezer) and whole carcasses (Cryochiller) at rates closely mimicking those attained with immersion in liquid nitrogen. Because this hardware will be used extensively on future rodent research missions it is crucial to understand whether or not it preserves signals in gene expression data in order to maximize the value of these rare and expensive spaceflight experiments. Therefore this study was designed with three goals: 1) To evaluate the temperature profile of the Cryochiller and Glovebox freezer cartridges (Rapid Freeze hardware) over time during mock on-orbit procedures; 2) To determine the freezing profiles of tissues and carcasses using Rapid Freeze hardware at both optimal and sub-optimal temperatures (to mimic on-orbit operations) compared with those frozen in liquid nitrogen (the laboratory gold standard) or frozen at -80 C (the current standard method); 3) To identify gene expression changes in a) tissues that were frozen via the Glovebox freezer and b) tissues dissected from whole or partial carcasses that were frozen via the Cryochiller versus tissues that were frozen via control methods (liquid nitrogen or -80C slow freeze) to assess how the Rapid Freeze hardware compares with laboratory gold standard practices and our current standard methods.
Transcriptional analysis of spleens from mice preserved with the Rapid Freeze hardware
Data from the NASA Rodent Research-1 (RR-1) mission showed that gene-expression levels in mouse livers are different depending on what tissue preservation protocol is used and that slow freezing is not an effective method for preserving signals in gene-expression data. In response to these and other observations the Rapid Freeze hardware was built for use on the International Space Station. The Rapid Freeze hardware freezes mouse tissues (Glovebox freezer) and whole carcasses (Cryochiller) at rates closely mimicking those attained with immersion in liquid nitrogen. Because this hardware will be used extensively on future rodent research missions it is crucial to understand whether or not it preserves signals in gene expression data in order to maximize the value of these rare and expensive spaceflight experiments. Therefore this study was designed with three goals: 1) To evaluate the temperature profile of the Cryochiller and Glovebox freezer cartridges (Rapid Freeze hardware) over time during mock on-orbit procedures; 2) To determine the freezing profiles of tissues and carcasses using Rapid Freeze hardware at both optimal and sub-optimal temperatures (to mimic on-orbit operations) compared with those frozen in liquid nitrogen (the laboratory gold standard) or frozen in at -80 C (the current standard method); 3) To identify gene expression changes in a) tissues that were frozen via the Glovebox freezer and b) tissues dissected from whole or partial carcasses that were frozen via the Cryochiller versus tissues that were frozen via control methods (liquid nitrogen or -80C slow freeze) to assess how the Rapid Freeze hardware compares with laboratory gold standard practices and our current standard methods.
THE HARDWARE PART OF THE AUTOMATIC SYSTEM WITH FIBER OPTIC SENSOR UNDER DEVELOPMENT
Open the record for dataset details and reuse information.
Xenon-129 Magnetic Resonance Imaging of Healthy Subjects: Hardware and Software Development and Reproducibility
ClinicalTrials.gov study NCT02484885. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.