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Artificial Intelligence

Biocept Reports Second Quarter 2019 Financial Results



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Biocept, Inc. (NASDAQ: BIOC), a leading commercial provider of liquid biopsy tests designed to provide physicians with clinically actionable information to improve the outcomes of cancer patients, reports financial results for the three and six months ended June 30, 2019, and provides an update on its business progress.

“I’m pleased to report another quarter of strong performance with revenues increasing 45% over the prior-year quarter, as we continue to execute on our new commercial strategy,” said Michael Nall, President and CEO of Biocept.  “Growth was driven by a 26% year-over-year increase in commercial samples, as we focused our commercial efforts on segments of the liquid biopsy oncology market where Target Selector™ can help the most patients, namely in prostate, breast, and lung cancers. Most importantly, we are helping more patients as our billable samples accessioned per sales day entering the third quarter increased approximately 50% from the beginning of the year.

“We have now launched two tumor-specific panels developed in collaboration with Thermo Fisher Scientific,” he added.  “These products, Target Selector™ NGS Lung Panel and Target Selector™ NGS Breast Panel, combine Thermo Fishers’ state-of-the-art Ion Torrent™ next-generation sequencing (NGS) platform with our CLIA laboratory and commercial infrastructure, as well as our expertise in blood sample preservation and DNA/RNA isolation.  Biocept is the only commercial liquid biopsy company offering both circulating tumor cell (CTC) and circulating tumor DNA (ctDNA) analysis with both single-gene and multi-gene offerings.

“I’m also pleased to report that our initiative with Prognos has advanced to the next phase as we are beginning to supply them with de-identified information in real time.  We believe this partnership will allow us to commercialize data generated from our liquid biopsy testing, with Prognos applying its artificial intelligence technology to its repository of more than 20 billion laboratory records to help life science and pharmaceutical companies develop and market targeted therapies.  We are pleased to be the first liquid biopsy company to strike a partnership with Prognos,” he concluded.

Review of Second Quarter and Recent Highlights

Commercial Business

  • Launched Target Selector™ NGS Lung Panel and Target Selector™ NGS Breast Panel, the Company’s first two multi-gene liquid biopsy panels, differentiating Biocept as the only commercial liquid biopsy provider of single-biomarker testing, tumor-specific panels and CTCs analysis. The NGS Panels run on Thermo Fisher Scientific’s Ion Torrent™ NGS platform and are being marketed to physicians and researchers for the detection and monitoring of actionable biomarkers associated with these tumor-specific cancers.

Commercial Agreements

  • Announced an agreement with Beacon Laboratory Benefit Solutions, Inc. designating Biocept as a BeaconLBS® Lab-of-Choice. Beacon Laboratory is a nationally recognized provider of laboratory benefit management technology solutions to U.S.-based health and managed care companies, and the designation increases patient access to Biocept’s liquid biopsy testing platforms.

Intellectual Property

  • Awarded a patent in China covering methods and devices for the capture of rare cells of interest, including CTCs, that are shed into the bloodstream by solid tumors in which an antibody or mixture of antibodies and a microchannel are used for cell capture, detection and analysis. This patent covers the use of any biological sample type of interest.

Second Quarter Financial Results

Revenues for the second quarter of 2019 were $1.2 million, a 45% increase from $822,000 for the second quarter of 2018.  Revenues for the second quarter of 2019 included $1.1 million in commercial test revenue, $45,000 in development services test revenue, $28,000 in revenue for Target Selector RUO kits, which were commercially launched in early 2019, and CEE-Sure blood collection tubes.  Revenues for the second quarter of 2018 included $771,000 in commercial test revenue and $51,000 in development services test revenue.

Biocept accessioned 1,066 commercial samples during the second quarter of 2019, a 26% increase compared with 849 commercial samples accessioned during the second quarter of 2018.  The Company accessioned 1,211 billable samples in the second quarter of 2019, compared to 996 billable samples for the second quarter of 2018.

Cost of revenues for the second quarters of 2019 and 2018 was unchanged at $2.7 million, as we continued to leverage the fixed components of our costs.

Research and development (R&D) expenses for the second quarter of 2019 were $1.1 million compared with $1.0 millionfor the prior-year period, with the increase primarily due to the development and validation of the recently launched Target Selector™ NGS Lung and Target Selector™ NGS Breast liquid biopsy panels, as well as investments in automation.  General and administrative (G&A) expenses for the second quarters of 2019 and 2018 were unchanged at $1.7 million.  As a percentage of revenue, G&A expenses during the quarter were down 67% as compared to the same period last year as the Company continues with its cost containment program. Sales and marketing (S&M) expenses for the second quarter of 2019 were $1.6 million compared with $1.4 million for the second quarter of 2018, with the increase primarily attributed to higher volume and revenue. Despite the increase in costs, S&M expenses as a percentage of revenue were down 39% compared to the same period last year.

Other expenses for the second quarter of 2019 were $1.8 million, which were made up entirely of non-cash warrant inducement expenses associated with recognizing the fair value of the inducement warrants issued in May 2019.

The net loss for the second quarter of 2019 was $7.8 million, inclusive of the previously mentioned non-cash warrant inducement expenses of $1.8 million, or $0.38 per share on 20.5 million weighted-average shares outstanding. This compares with a net loss for the second quarter of 2018 of $6.2 million, or $2.70 per share on 2.3 million weighted-average shares outstanding. The Company conducted a 1-for-30 reverse stock split of its outstanding common stock, which was effective in July 2018.

Six Month Financial Results

Revenues for the first six months of 2019 were $2.2 million, a 36% increase from $1.6 million for the first six months of 2018, and included $2.1 million in commercial test revenues, $87,000 in development services test revenues and $33,000 in revenues for Target Selector RUO kits, which were commercially launched in early 2019, and CEE-Sure blood collection tubes.

Operating expenses for the first six months of 2019 were $14 million, and included cost of revenues of $5.3 million, R&D expenses of $2.4 million, G&A expenses of $3.4 million and S&M expenses of $3.0 million.

The net loss for the first six months of 2019 was $13.8 million, inclusive of the previously mentioned non-cash warrant inducement expenses of $1.8 million, or $0.83 per share on 16.7 million weighted-average shares outstanding.  This compares with a net loss for the first six months of 2018 of $12.5 million, or $5.97 per share, on 2.1 million weighted-average shares outstanding.

Biocept reported cash and cash equivalents as of June 30, 2019 of $12.6 million, compared with $3.4 million as of December 31, 2018.  The increase was due to $17.0 million in net proceeds from equity capital raises conducted in the first quarter of 2019, and $4.9 million from the exercise of common stock warrants in the second quarter of 2019.


SOURCE Biocept, Inc.

Artificial Intelligence

IJCAI 2019 in the Spotlight: WeBank AI Group Shared Remarkable Academic Innovation



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The 28th International Joint Conferences on Artificial Intelligence (IJCAI) was held from August 10th – 16th, in Macao, SAR, China. WeBank, the first private and digital bank in China, contributed to the event with multiple academic research findings, and demonstrated great engagement in IJCAI by organizing a heavy-weight workshop FML’19 (the 1st International Workshop on Federated Machine Learning for User Privacy and Data Confidentiality).

100+ World-leading Scholars Discussed Academic Frontier at the 1st International Workshop on Federated Machine Learning

The idea of adopting FML in AI for data confidentiality and user privacy was coined by WeBank in China. In a bid to promote this emerging AI technology, WeBank, IBM and other organizations jointly held the 1st International Workshop on Federated Machine Learning in conjunction with IJCAI 2019. 100+ leading scholars with insight in FML from home and abroad were invited to share cutting-edge academic findings and most advanced applications. President of IJCAI, Chief AI Officer of WeBank Professor Qiang Yang delivered opening remarks. Dr. Shahrokh Daijavad from IBM and Dr. Jakub Konečný from Google delivered keynote addresses. The moderator of panel discussion, AI Principal Scientist of WeBank Dr. Lixin Fan joined panelists including Professor Benny Pinkas from Bar-Ilan University, Dr. Shahrokh Daijavad of IBM Academy of Technology, Chief Architect of Squirrel AI Dr. Richard Tong, Research Scientist of Google Dr. Jakub Konečný, Dr. Baofeng Zhang from CTO Office of CBG Software in Huawei, Executive VP of Clustar Dr. Junxue Zhang, VP of AI Institute in Sinovation Ventures Dr. Ji Feng and other experts in exchanging thoughts on the way ahead for FML.

Take Stock on 40 Years’ Achievement of AI in China  Panel Discussion Featuring Chinese Characteristics

Elements of previous conferences including Traditional AI Session, Industry Day focused on industrial application and Best Paper Awards Session were inherited in this year’s IJCAI. IJCAI-19 also opened panels and workshops under the new agenda with a focus on most-discussed topics e.g., data privacy and AI universality. Chief AI Officer of WeBank Professor Qiang Yang engaged in multiple agendas as the President of IJCAI-19 and chair of two panel discussions namely AI in China, AI and User Privacy.

Panelists of “AI in China” include Academician of Chinese Academy of Sciences Bo Zhang, Academician of Chinese Academy of Engineering Wen Gao, Dean of School of AI of Nanjing University Professor Zhihua Zhou, Professor Pascale Fung of Department of Electronic and Computer Engineering of HKUST, Professor Tong Zhang of HKUST, CEO of 4Paradigm Wenyuan Dai. These leading figures in China’s AI sector shared stories within the industry. This theme, with its unique historic significance, added weight to IJCAI-19.

IJCAI-19 also witnessed the founding of the GuangdongHong KongMacao Greater Bay Area on AI and Robotics Federation. Announced at “AI in China” Panel, the establishment of this new Federation requires the three academic societies to pool leading scholars and experts in AI and robotics within their localities. The merge was widely supported, acclaimed and recognized across the industrial sector and the government as it will further promote cohesion of talents, spawning of scientific innovation, R&D and application of key technologies in China.

“AI and User Privacy” Panel was joined by Director of IEEE Standards Association Victoria Wang, Professor Pedro Domingos of University of Washington, Director of Swiss Re Institute Jeffrey Bohn, Senior Research Scientist of WeBank AI Dr. Yang Liu and a host of experts and scholars to further discussions on how to promote technology development and legislation process simultaneously, which is helpful to addressing the user privacy issue in the current stage of AI development.

WeBank Shared New Insight on AI Safety Workshop

Besides user privacy as discussed in the panel discussions, data confidentiality also represents a common concern in the age of big data. At the AI Safety 2019 Workshop, Senior Research Scientist of WeBank AI Dr. Yang Liu delivered a speech themed Federated Machine Learning (FML), and shared in-depth insight on how to safeguard user privacy and AI safety as well as a number of technologies for privacy protection. She also elaborated on three categories of FML, namely Horizontal Federated Learning, Vertical Federated Learning, Federated Transfer Learning. According to Dr. Liu, data confidentiality and user privacy are the two major challenges in the age of big data, particularly challenging for financial, medical, legal and other data sensitive industries, whereas FML is a great solution to both challenges.

AI Enabling Contextualized Application in Finance  WeBank Shared Insight on Digital Innovative Transformation

While FML serves as the theoretical basis, the application of FML represents a common concern for all walks of life. Challenges before the highly digitalized financial sector manifest even greater complexity and risk. At Industry Day in IJCAI 2019, Deputy Managing Director of WeBank AI Tianjian Chen shared in-depth insight on digital banking business in the financial sector under the theme “AI in Digital Banking”, and elaborated on the important role that AI plays in digital banking. Given the challenges of AI application in the financial sector, he pointed the way ahead for the new generation of AI. “Safety, fairness, data protection are major challenges in the application of AI in the banking sector,” he said. “FML is potentially the new path to take for addressing these challenges.” So far, WeBank AI Group has developed a series of pioneering technologies including FML, which are proven to be contributive to joint modeling for credit risk control and anti-money laundering, etc.

Demonstration of FML Visualization  WeBank Introduced Best Practice

WeBank AI Group is dedicated to promoting widespread application of FML across the industry, sharing capabilities, enabling multi-win results. Videos submitted by WeBank AI Group, including Multi-Agent Visualization for Explaining Federated AI, Learning Federated Learning, were accepted by Demonstration Track and AI Video Competition of IJCAI-19, of which, the latter was awarded Most Educational Video. The videos provide straightforward illustration of cases to attendees on how FML and FedAI system works, all designed for more partners within the industry to enhance understanding of FML technologies and become promoters.

As an endeavor to explore “AI + Art” fusion, the man-computer car racing game demo developed by WeBank AI Group based on FML technology will be displayed under the invitation of China Central Academy of Fine Arts, and is expected to be showcased in the Shenzhen and Shanghai Art Exhibition scheduled in late October and early November respectively.

Among 35 papers submitted in Demonstration Track, the research paper on AI empowering flexible staffing by WeBank, HKUST, NTU and BBK Group, titled Fair and Explainable Dynamic Engagement of Crowd Workers, won the Innovation Award.

WeBank “AI+X” Innovation Debut  Exploration of Future AI Ecosystem

In addition to academic research findings, WeBank AI Group also exhibited industry-leading innovations in four main areas namely “AI + Service”, “AI + Marketing”, “AI + Big Data”, “AI + Asset Management”, which drew the attention of government officials from Macao SAR, professors and scholars of universities and research institutions in China and abroad.

In the area of “AI + Big Data”, WeBank established FedAI ecosystem for cooperation, the world’s first industrial-grade framework for Federated Learning (FATE), AI scenario-based rapid modeling platform (QML). In “AI + Service”, WeBank explored new approaches and scenarios for human-computer interaction, developed ubiquitous robots focused on financial services, integrated core technologies such as NLP, TTS, OCR with scenarios, and expanded to a series of business contextualized applications. The robot developed independently by WeBank became a spotlight in the exhibition. In “AI + Marketing”, WeBank AI Group played a leading role in trust marketing development, aimed to promote long-link and long-term effective marketing conversion of high-value products. In “AI + Asset Management”, WeBank used new alternative big data and machine learning technology helped forge a new generation of AI-driven intelligent asset management system.

China’s scientific research capability is among the top in the world thanks to advances in big data, AI and other frontier technologies. As an internet bank dedicated to innovation in fintech, WeBank AI Group demonstrates its commitment in global collaboration for scientific research and sparks technological advances by coupling theory and application. Looking ahead, WeBank will further leverage its strength in AI technology and platform, integrate top-notch resource worldwide, forge high-level network for knowledge and research, and lead the way for global tech innovation.



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Artificial Intelligence

Population Size and Immigration Policy Could Define the Outcome of US-China Tech Race, Says Ctrip Executive Chairman James Liang



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James Liang, Co-founder and Executive Chairman of Ctrip, recently attended the 2019 Yabuli China Entrepreneurs Forum Summer Summit, where he discussed how population and demographic management could be the determining factors in the US-China tech race.

During the forum, Liang spoke about how each nation’s approach to demographics and immigration will play a pivotal role in determining their future innovation and development potential. According to Liang, although China is well-placed to outpace the US in the short term, it will need to tackle the core challenges of population size and demographic make-up to ensure sustainable growth in the long run.

“In simple terms, the more people you have, the more research scientists and engineers will be available to develop world-leading artificial intelligence technologies to overtake your competitors,” said Liang.

Liang observes that China’s current momentum in the tech race stems from both its mammoth population size and the size of its market. With a population of 1.4 billion, China has more human resources to invest in research and development. Coupled with the fact that China is home to the world’s largest e-commerce market, this means domestic technology giants have access to more user application scenarios and innovation opportunities.

In addition, the number of undergraduates produced by China is more than triple that of the US, placing China at a definite competitive advantage for R&D over the next two decades.

Despite this, Liang warned that diminishing fertility rates, an aging population and a ‘brain drain’ of talent would put China’s advantage at risk in the future. The average Chinese family has 1.2 children, effectively halving the population every generation. Lingering effects from China’s one-child policy mean that the population above age 65 will continue to grow exponentially, reaching 330 million by 2050, further impeding the pace of innovation. Chinese students are also lured by the prospect of studying overseas, forming almost a third of American international students.

When asked about China’s radical solutions to address population and demographic issues, Liang responded that proactive policies would play the most prominent role in maintaining its competitive edge.

“In the long run, the outcome will come down to foresighted management of population policy,” said Liang. “It’s not wise to wait for the latest technologies to solve severe demographic problems. For China, this would require significant reform by putting in place policies to encourage childbearing, relaxing immigration and visa laws, and reforming the education system.”

Contrary to China’s potential innovation deficit in the future, Liang views the US’ attitude to immigration as the nation’s most significant hurdle in the future. Liang noted that at present, the US population more than doubles when taking migration into account and, at its current pace, is set to surpass China’s by 2100.

In addition, more than half of all US tech companies are built by immigrants, and non-US citizens make up 45% of all Ph.D. candidates in key innovation fields like engineering, and computer sciences are non-US citizens, as well as 35% in mathematics. Ultimately, Liang says, this demonstrates a strong correlation between a diverse population and innovation.

“An open door to the brightest minds of the world means that US technology companies can be assured of their ability to remain at the forefront of innovation,” said Liang. “The restrictive immigration policies pursued by the incumbent US president run the genuine risk of throwing away the strongest advantage the US holds – an openness to overseas talent. If this protectionism persists, it could have devastating effects on America’s ability to innovate technologically.”

In the end, Liang says the future will be decided by the nation that can best tackle their challenges and adopt new policies to maintain their edge in terms of population and demographics.

“This is where I believe the battle for the top will be won or lost – a young, dynamic population with an openness to seeking the best talent from overseas,” says Liang.

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Artificial Intelligence

WeBank, IBM and Other Organizations Jointly Held the 1st International Workshop on Federated Machine Learning in conjunction with IJCAI 2019



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Once a concept, AI is now ushering in a key stage of application. What’s the solution to the data silos among businesses? Given the enhanced regulation on data at home and abroad, what’s the solution to data privacy and security concerns? What’s the status quo of Federated Machine Learning and how to establish an ecosystem for FML in the future?

WeBank, IBM and other organizations jointly held the 1st International Workshop on Federated Machine Learning for User Privacy and Data Confidentiality (FML’19) in conjunction with the 28th International Joint Conference on Artificial Intelligence (IJCAI-19) on Aug. 12, 2019, to further discussion on these issues.

President of IJCAI, Chair of FML Steering Committee, Chief AI Officer of WeBank Professor Qiang Yang delivered opening remarks at the workshop. Dr. Shahrokh Daijavad from IBM and Dr. Jakub Konečný from Google presented keynote addresses. In the panel discussion, top scholars from WeBank, Bar-Ilan University, IBM, Squirrel AI, Google, Huawei, Clustar, Sinovation Ventures and many other renowned enterprises and universities shared and discussed their findings and experience in FML as an emerging AI technology.

This workshop received 40 papers, of which 12 were presented during the workshop, 19 presented via poster. Awards include Best Theory Paper Award, Best Application Paper Award, Best Student Paper Award, Best Presentation Award. Selected high quality papers will be invited for publication in a special issue in the IEEE Intelligent Systems journal. All these attracted numerous scholars to engage in discussions and join efforts for building the FML ecosystem.

Experts from IBM and Google Share Groundbreaking Findings with a Focus on the Theory and Application of FML

Privacy and security are becoming a key concern in our digital age. On 25th May last year, the implementation of General Data Protection Regulation (GDPR) by the EU, the toughest Act on data privacy protection, stressed that user data collection must be open and transparent. A series of laws and regulations from China and overseas also pose new challenges to the traditional way of handling data and model for cooperation. Seeking ways for AI to adapt to this new reality became top priority, a demand that led to this workshop on FML.

A wealth of solutions and breakthroughs were shared by Dr. Shahrokh Daijavad from IBM and Dr. Jakub Konečný from Google in their speech on FML.

Besides how FML can help tackle challenges in the business world, Dr. Shahrokh Daijavad also shared the concept of Fusion AI, which means to train models on widely distributed data sets, but fuse them to produce one equivalent to what centralized training would yield. “Unlike traditional machine learning, in Fusion AI, model parameters are shared and data is not transferred, which makes Fusion AI model better than models that moving data centrally.” Given the widely distributed data, the development of Fusion AI and FML became ever important and imminent.

“FML enables machine learning engineers and data scientists to work productively with decentralized data with privacy by default,” said Dr. Jakub Konečný from Google. He also shared with us how FML works and its use cases at Google. In the case of Gboard, as on-device data is privacy sensitive or large or is more relevant than server-side proxy data, and labels can be inferred naturally via user interaction, the application of Federated RNN compared to prior n-gram model can increase the accuracy of next-word prediction by 24%, and the click rate of prediction strip by 10%.

Major Figure Panelists Discuss the Way Ahead for FML

The moderator of the panel discussion, AI Principal Scientist of WeBank Dr. Lixin Fan joined panelists including Professor Benny Pinkas from Bar-Ilan University, Dr. Shahrokh Daijavad of IBM Academy of Technology, Chief Architect of Squirrel AI Dr. Richard Tong, Research Scientist of Google Dr. Jakub Konečný, Dr. Baofeng Zhang from CTO Office of CBG Software in Huawei, Executive VP of Clustar Dr. Junxue Zhang, VP of AI Institute in Sinovation Ventures Dr. Ji Feng and other experts in a host of in-depth exchanges with attendees, to shed light on the way ahead for FML.

Experts shared thoughts in the panel discussion on questions including but not limited to: How to meet the security and compliance requirements? Is there a way to extend the value of data while observing user privacy and data security? Given the classic trade-off between data regulation and development of AI, how to achieve the long-term goal of establishing a stable and win-win business ecosystem?

List of Award-Winners

Best Theory Paper Award, Best Application Paper Award, Best Student Paper Award and Best Presentation Award selected by all attendees were announced at the closing of the workshop.

Best Theory Paper Award: 
Preserving User Privacy for Machine Learning: Local Differential Privacy or Federated Machine Learning? By Huadi Zheng, Haibo Hu & Ziyang Han;

Best Application Paper Award: 
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare. By Yiqiang ChenJindong WangChaohui YuWen Gao & Xin Qin;

Best Student Paper Award: 
Quantifying the Performance of Federated Transfer Learning. By Qinghe JingWeiyan Wang, Junxue Zhang, Han Tian & Kai Chen;

Best Presentation Award: 
Federated Generative Privacy. By Aleksei Triastcyn and Boi Faltings.

President of IJCAI, Chief AI Officer of WeBank Professor Qiang Yang, Chief Architect of Squirrel AI Dr. Richard TongandVP of AI Institute in Sinovation Ventures Dr. Ji Feng presented the awards.

“The mission of this International Federated Machine Learning Workshop is to facilitate further understanding in the academia, business community as well as legal and regulatory institutions by promoting the establishment of FML ecosystem in the hope that more businesses will join and build a platform for students aspired to work in FML to find research teams that suit them,” said Professor Qiang Yang.

Held Aug. 10-16, 2019 in Macao, China, IJCAI-19 is one of the leading International Academic Conference on AI, attracting over 3000 AI research personnel and experts. The 1st International Workshop on Federated Machine Learning (FML’19) was a highlight for experts joining this event. Visionaries in the academia and industrial sector expressed the willingness to be part of the effort for academic research, application of FML in the future, and the development and boom of AI ecosystem.

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