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MicroCloud Hologram Inc. announces optimization of stacked sparse autoencoders through DeepSeek model

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SHENZHEN, China, Feb. 14, 2025 /PRNewswire/ — MicroCloud Hologram Inc. (NASDAQ: HOLO), (“HOLO” or the “Company”), a technology service provider, they Announced the deep optimization of stacked sparse autoencoders through the DeepSeek open-source model, injecting new vitality into anomaly detection technology and providing an efficient solution.

Data quality is crucial for model performance, so the behavioral data collected in the data preprocessing stage typically contains multiple features with different dimensions and numerical ranges. In order to eliminate the dimensional influence between different features and improve the effectiveness of model training, HOLO uses normalization processing method.

Normalization is a common data preprocessing technique that scales the data to a specific range, typically between 0 and 1 or -1 and 1. By doing so, data from different features can be compared and analyzed on the same scale, avoiding the situation where certain features dominate model training due to their large value ranges. In HOLO’s detection project, normalization not only improved the efficiency of model training but also laid a solid foundation for subsequent feature extraction. The data processed through normalization is more aligned with the input requirements of deep learning models, enable the model to learn intrinsic patterns more accurately.

After the data preprocessing is completed, the next step is to input the processed data into the stacked sparse autoencoder model. The stacked sparse autoencoder is a powerful deep learning architecture composed of multiple autoencoder layers, with each layer responsible for extracting features at different levels. HOLO utilizes the DeepSeek model to dynamically adjust the strength and manner of the sparsity constraint, ensuring that the features learned by each layer of the autoencoder are sparse and representative. By appropriately setting the sparsity constraint, the model can better capture key information in the data and reduce redundant features. An autoencoder is an unsupervised learning model designed to encode input data into a lower-dimensional feature representation through the encoder, and then reconstruct the original input data as accurately as possible through the decoder. Between the encoder and decoder, the autoencoder learns the feature representation of the data through a hidden layer.

HOLO has innovated and optimized the stacked sparse autoencoder by utilizing the DeepSeek model. This technique employs a greedy, layer-wise training approach, optimizing the parameters of each autoencoder layer step by step. The core of this layered training strategy is to first train the lower layers of the autoencoder to learn the basic features of the input data, then use the output of the lower-layer autoencoder as the input for the next layer, continuing training and progressively extracting deeper features. In this way, the model is able to gradually capture the complex relationships within the data, enhancing its expressive power. Each layer of the autoencoder is constrained by sparsity, ensuring that the learned features are sparse, meaning that only a few neurons are activated, allowing the model to learn more compact and effective feature representations.

HOLO’s stacked sparse autoencoder, trained with the DeepSeek model, adds noise to the input data and requires the model to reconstruct the original input despite the noise interference. This denoising training approach encourages the model to learn more robust feature representations, enabling it to perform accurate anomaly detection even when faced with noisy data in real-world scenarios, thus improving the model’s robustness. Specifically, during training, random noise is added to the input data, and the model is tasked with reconstructing the original input. This process forces the model to learn more resilient feature representations, ensuring high accuracy even in the presence of various types of noise in real-world conditions.

In addition to denoising, HOLO also applies Dropout during the training process. Dropout is a commonly used regularization technique primarily aimed at reducing model overfitting. In deep learning models, overfitting refers to the phenomenon where a model performs well on training data but poorly on unseen samples. To avoid this, HOLO randomly drops a subset of neurons during the training of the stacked sparse autoencoder. In each training iteration, the model randomly selects a portion of neurons and sets their outputs to zero. The benefit of this approach is that the model cannot rely on any specific neuron to learn the features of the data, but must instead learn more general and robust feature representations.

In addition, the DeepSeek model utilizes a distributed computing framework, which allocates training tasks across multiple computational nodes for parallel execution. This significantly shortens training time and improves training efficiency. Using the DeepSeek model, pretraining can first be conducted on the stacked sparse autoencoder to learn general feature representations. This pretraining + fine-tuning strategy can greatly accelerate model convergence and improve performance. By introducing the DeepSeek model, HOLO has injected new vitality into optimizing stacked sparse autoencoders. The DeepSeek model provides comprehensive support in areas such as architecture design, training, strategic feature learning, and generalization ability.

About MicroCloud Hologram Inc.

MicroCloud is committed to providing leading holographic technology services to its customers worldwide. MicroCloud’s holographic technology services include high-precision holographic light detection and ranging (“LiDAR”) solutions, based on holographic technology, exclusive holographic LiDAR point cloud algorithms architecture design, breakthrough technical holographic imaging solutions, holographic LiDAR sensor chip design and holographic vehicle intelligent vision technology to service customers that provide reliable holographic advanced driver assistance systems (“ADAS”). MicroCloud also provides holographic digital twin technology services for customers and has built a proprietary holographic digital twin technology resource library. MicroCloud’s holographic digital twin technology resource library captures shapes and objects in 3D holographic form by utilizing a combination of MicroCloud’s holographic digital twin software, digital content, spatial data-driven data science, holographic digital cloud algorithm, and holographic 3D capture technology. For more information, please visit http://ir.mcholo.com/

Safe Harbor Statement

This press release contains forward-looking statements as defined by the Private Securities Litigation Reform Act of 1995. Forward-looking statements include statements concerning plans, objectives, goals, strategies, future events or performance, and underlying assumptions and other statements that are other than statements of historical facts. When the Company uses words such as “may,” “will,” “intend,” “should,” “believe,” “expect,” “anticipate,” “project,” “estimate,” or similar expressions that do not relate solely to historical matters, it is making forward-looking statements. Forward-looking statements are not guarantees of future performance and involve risks and uncertainties that may cause the actual results to differ materially from the Company’s expectations discussed in the forward-looking statements. These statements are subject to uncertainties and risks including, but not limited to, the following: the Company’s goals and strategies; the Company’s future business development; product and service demand and acceptance; changes in technology; economic conditions; reputation and brand; the impact of competition and pricing; government regulations; fluctuations in general economic; financial condition and results of operations; the expected growth of the holographic industry and business conditions in China and the international markets the Company plans to serve and assumptions underlying or related to any of the foregoing and other risks contained in reports filed by the Company with the Securities and Exchange Commission (“SEC”), including the Company’s most recently filed Annual Report on Form 10-K and current report on Form 6-K and its subsequent filings. For these reasons, among others, investors are cautioned not to place undue reliance upon any forward-looking statements in this press release. Additional factors are discussed in the Company’s filings with the SEC, which are available for review at www.sec.gov. The Company undertakes no obligation to publicly revise these forward-looking statements to reflect events or circumstances that arise after the date hereof.

 

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SOURCE MicroCloud Hologram Inc.

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As ADA Anniversary Approaches, University of Phoenix Survey Highlights AI’s Potential to Advance Accessibility in Work and Learning

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July 24, 2026

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Survey conducted by The Harris Poll on behalf of University of Phoenix finds among those already using AI in the workplace, 60% say AI has improved their knowledge of and ability to use accessibility standards and guidelines.

PHOENIX, July 24, 2026 /PRNewswire/ — As artificial intelligence becomes part of how people work, learn and solve problems, a new University of Phoenix survey conducted by The Harris Poll finds that recent working learners see meaningful opportunities for AI to support accessibility. The survey was designed to understand the impact of AI in the workplace and learning environments on accessibility, defined as ensuring digital content, tools and resources, including AI tools and output, are usable by people with different abilities through inclusive design, use of assistive technology or conformance with accessibility standards, such as the Web Content Accessibility Guidelines (WCAG). The findings are being released ahead of the 36th anniversary of the Americans with Disabilities Act (ADA) on July 26.

The survey, conducted among 1,019 U.S. employed adults who completed a professionally presented training or school course in the past 12 months (“recent working learners”), found that, among workers already using AI in the workplace, 3 in 5 (60%) say AI has improved their knowledge of and ability to use accessibility standards and guidelines, including nearly 1 in 5 (19%) who report significant improvement. 

While the findings point to optimism about AI’s accessibility potential, they also reveal an opportunity for clearer organizational guidance: 45% of respondents say accessibility is absent from, unclear in, or they are uncertain whether it is covered by their workplace AI policies.

“The reality is that accessibility benefits everyone,” shares Kelly Hermann, Vice President of Accessibility and Student Affairs at University of Phoenix. “If accessibility is built in from the beginning, organizations are more likely to create AI-enabled environments that are universally usable. Clearer content, better summaries, accurate captions, and multiple formats can help workers and learners with disabilities, but they also help busy adults, multilingual learners, mobile users, and anyone trying to absorb information quickly.”

Key findings from the survey include:

Workers see AI’s accessibility potential: 89% of recent working learners identify workflows that could benefit from AI and accessibility tools, especially creating accessible documents, presentations, websites or learning materials (38%), presenting information in different formats such as plain language, audio, summaries or translations (33%), and training employees or learners on accessibility practices (30%).AI may help build accessibility awareness: Among those already using AI in the workplace, 60% say AI has improved their knowledge of and ability to use accessibility standards and guidelines.Accessibility is not always clear in workplace AI policies: 45% of recent working learners say accessibility is absent from, unclear in, or they are uncertain whether it is covered by their workplace AI policies.AI tools may not yet fully support different access needs: Among those who use workplace AI tools, only about a quarter of survey respondents (27%) say AI tools available through their workplace or professional learning environment support people with disabilities very well.Human oversight remains important: 36% of recent working learners say human review for important decisions or high-impact work should be part of responsible AI use at work or school.Workers also recognize how AI and accessibility can have an impact on their own career journey: 90% of recent working learners identify AI and accessibility skills that would be valuable in their current or desired career field, including 45% who see value in understanding when AI-generated content needs human review.

Why accessibility is essential to responsible AI adoption

As AI tools are used to draft documents, summarize information, generate captions and transcripts, create image descriptions, support learning and assist with workplace tasks, accessibility becomes central to responsible use. Poorly implemented AI can also create or amplify barriers, including inaccessible content, inaccurate summaries, biased outputs and tools that do not work effectively with assistive technologies.

“Responsible AI is not only about productivity,” Hermann said. “It is about whether the technology works for the people who need to use it. AI can help create more accessible materials and more flexible ways to engage with information, but it still requires clear policies, practical training and human judgment to make sure the outputs are accurate, applicable and usable.”

What the findings mean for employers and educators

The survey suggests that organizations have an opportunity to align AI adoption with supportive design, accessibility practices and workforce training. Employers and educators can take immediate steps by:

Naming accessibility directly in AI policies and guidance.Choosing AI tools with accessibility and assistive technology compatibility in mind.Training workers and learners to create, check and improve accessible AI-generated content.Making support pathways clear for people who experience barriers using AI tools.Keeping human review in place for important decisions, high-impact work and accessibility-sensitive outputs.

The survey also found workers want practical AI training. The most helpful resources identified by recent working learners include real-world examples from their field or industry (36%), hands-on practice using realistic workplace scenarios (34%) and step-by-step demonstrations of common tasks (33%).

Accessibility insights from University of Phoenix

Hermann shared the survey findings ahead of the ADA anniversary in recent media interviews. Hermann oversees the University’s accessibility initiative, including evaluation and remediation of curricular resources, the Center for Access, Resources, Engagement and Support Services (CARES), and the Office of Collaborative Learning and Educational Engagement. Her work focuses on fostering accessible and welcoming educational environments for students, faculty and staff.

Hermann’s office at University of Phoenix also convenes accessibility conversations through initiatives such as Access Amplified™, a free, annual virtual event focused on advancing digital accessibility in web development. The event brings together engineers, developers, designers, content authors and digital strategists for practical strategies and human-centered conversations that address the gap between coding practices and how users with assistive technology experience the web.

About the survey

The survey was conducted online within the United States by The Harris Poll on behalf of University of Phoenix from June 22–29, 2026, among 1,019 employed adults ages 18 and older who have taken a professionally presented training or a school course in the past 12 months, referred to as “recent working learners.” Data were weighted where necessary by age, gender, race/ethnicity, region, education, employment, marital status, household size, household income and smoking status to bring them in line with their actual proportions in the population.

Respondents for this survey were selected from among those who have agreed to participate in surveys. The sampling precision of Harris online polls is measured by using a Bayesian credible interval. For this study, the sample data is accurate to within +/- 3.8 percentage points using a 95% confidence level. This credible interval will be wider among subsets of the surveyed population of interest.

Review the complete survey at phoenix.edu/aiaccessibility.

About University of Phoenix

University of Phoenix is Built for Real Life. 50 Years Strong. The University innovates to help working adults enhance their careers and develop skills in a rapidly changing world through flexible online learning, relevant courses, academic AI pillars, and skills-mapped curriculum for associate, bachelor’s and master’s degree programs. Active students and alumni have access to Career Services for Life® resources including career guidance and tools. For more information, visit phoenix.edu. 

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Mastech Digital to Announce Second Quarter 2026 Financial Results; Participate in Upcoming Investor Conference

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July 24, 2026

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PITTSBURGH, July 24, 2026 /PRNewswire/ — Mastech Digital, Inc. (NYSE American: MHH) (“Mastech Digital”), a leading provider of Digital Transformation IT Services, today announced the date for the release of its financial results for the second quarter ended June 30, 2026, and its participation in an upcoming investor conference.

Second Quarter 2026 Earnings:

Mastech Digital will report its financial results for the second quarter 2026 before the market opens on Thursday, August 6, 2026. Management will host a live conference call and webcast at 9:00 a.m. Eastern Time on that day to discuss the Company’s financial performance and operating results.  The conference call will be hosted by Nirav Patel, President and CEO, and Kannan Sugantharaman, Chief Financial and Operations Officer.

Those wishing to participate via webcast should access the call through Mastech Digital’s Investor Relations website at https://investors.mastechdigital.com. Those wishing to participate via telephone may dial in at 1-800-715-9871 (USA) or 1-646-307-1963 (International) with the passcode 7506988. The replay will be available via webcast through Mastech Digital’s Investor Relations website.

Upcoming Investor Conference:

Mr. Sugantharaman will host a fireside chat at the Sidoti Micro-Cap Investor Conference on Wednesday, August 19, 2026, at 9:15 a.m. Eastern Time.

Mastech Digital management is scheduled to host virtual one-on-one and small group meetings with investors during the conference on August 19-20, 2026. Investors interested in arranging a meeting should contact their Sidoti representative or reach out to the Mastech Digital investor relations team at investors@mastechdigital.com.

About Mastech Digital, Inc.

Mastech Digital (NYSE American: MHH) is a leading provider of Digital Transformation IT Services. The Company offers Data Management, Analytics & AI Solutions, and IT Staffing Services with a digital-first approach. A minority-owned enterprise, Mastech Digital is headquartered in Pittsburgh, PA, with offices across the U.S., Canada, Europe, and India. Visit us at www.mastechdigital.com.

Investor Relations Contact:
investors@mastechdigital.com 

 

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SOLAI Limited Announces Extraordinary General Meeting

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July 24, 2026

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AKRON, Ohio, July 24, 2026 /PRNewswire/ — SOLAI Limited (NYSE: SLAI) (“SOLAI” or the “Company”) (previously known as “BIT Mining Limited”), a technology-driven personal AI and digital infrastructure provider, today announced that it will hold its extraordinary general meeting of shareholders at 428 South Seiberling Street, Akron, Ohio, US on August 14, 2026 at 10:00 a.m., New York time.

Holders of record of ordinary shares and preference shares of the Company at the close of business on July 20, 2026, New York time (the “Record Date”) are entitled to receive notice of, and to attend and vote at, the extraordinary general meeting or any adjournment thereof. Holders of the Company’s American Depositary Shares (“ADSs”) who wish to exercise their voting rights for the underlying ordinary shares must act through the depositary of the Company’s ADS program, Deutsche Bank Trust Company Americas.

The notice of the extraordinary general meeting, which sets forth the resolutions to be submitted to shareholder approval at the extraordinary general meeting is available on the Investor Relations section of the Company’s website at https://ir.solai.com. 

About SOLAI Limited

SOLAI Limited (previously known as “BIT Mining Limited”) (NYSE: SLAI) (previously traded under “BTCM”) is a technology-driven personal AI and digital infrastructure provider. Building upon its historical legacy in digital asset mining and blockchain network operations, the Company is leveraging extensive experience in large-scale hardware deployment, data center operations, and high-performance computing to build the foundational infrastructure for personal AI computing and digital asset ecosystems globally.

For more information:

SOLAI Limited
ir@solai.com
ir.solai.com
www.solai.com 

Christensen Advisory
Jason Ng
Tel: +852-2117-0861
Email: solai@christensencomms.com 

 

View original content:https://www.prnewswire.com/news-releases/solai-limited-announces-extraordinary-general-meeting-302834034.html

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