Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Over the past decade, comprehensive sequencing efforts have revealed the genomic landscapes of common forms of human cancer. For most cancer types, this landscape consists of a small number of “mountains” (genes altered in a high percentage of tumors) and a much larger number of “hills” (genes altered infrequently). To date, these studies have revealed ~140 genes that, when altered by intragenic mutations, can promote or “drive” tumorigenesis. A typical tumor contains two to eight of these “driver gene” mutations; the remaining mutations are passengers that confer no selective growth advantage. Driver genes can be classified into 12 signaling pathways that regulate three core cellular processes: cell fate, cell survival, and genome maintenance. A better understanding of these pathways is one of the most pressing needs in basic cancer research. Even now, however, our knowledge of cancer genomes is sufficient to guide the development of more effective approaches for reducing cancer morbidity and mortality.

/pdf/cancer-genome-landscapes-qmge4i5y2h.pdf

Cancer Genome Landscapes

AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.

On the Opportunities and Risks of Foundation Models.

European Organization for Research and Treatment of Cancer

In this paper a design strategy for customized and efficient implementations of high-throughput DSP applications will be discussed. Typical examples of these applications can be found in real-time video processing for HDTV and other improved TV systems. The motivation of the work is that it forms the basis for the development of application driven efficient High-Level Synthesis techniques. More in particular this work is the basis for the synthesis approach in the PHIDEO compiler. For high-throughput applications special attention must be paid to the memory communication bandwidth. In this paper an approach will be presented which allows the combined design of processing units and memories. A design strategy in 5 steps is presented. The design methodology is illustrated using a 8 point IDCT as an example.

A Design Strategy For High-throughput Applications

Random redundant data storage strategies have proven to be a good choice for efficient data storage in multimedia servers. These strategies lead to a retrieval problem in which it is decided for each requested data block which disk to use for its retrieval. In this paper, we give a complexity classification of retrieval problems for random redundant storage.

Random redundant storage in disk arrays: complexity of retrieval problems

Background Treatment with targeted drugs is increasingly used in breast cancer aiming to block the tumor driving pathway(s). Drug choice is often based on a single primary tumor biopsy. It is important to ascertain that biopsied tumor material is representative for the tumor or metastases to treat with respect to tumor driving signaling pathway. Little is known about pathway heterogeneity within primary breast cancer, and between primary and metastatic tumors. A novel analysis method was developed to identify and quantify activity of signal transduction pathways in cancer tissue, based on Bayesian models which infer a pathway activity score from transcription factor target gene mRNA levels (Cancer Res 2014 Jun 1;74(11):2936-45). Methods Originally for AffymetrixU133Plus2.0, the pathway analysis was adapted for RT-qPCR enabling use on FFPE tissue. Calibration was performed using tissue samples with known pathway activity. Functional pathway activity of ER, AR, PI3K-FOXO, Hedgehog (HH), TGFbeta, and Wnt pathways was analyzed to assess (1) intra-tumor heterogeneity: 2-5 FFPE blocks of 17 primary breast cancers (9 luminal A, 4 luminal B, 1 HER2, 3 triple negative (TN), as defined by surrogate immunohistochemistry); (2) heterogeneity between primary and associated metastases: 9 patients with FFPE blocks from primary and 12 metastases. Results Intra-tumor heterogeneity: In 11 out of 13 Luminal A- and B-like tumors the ER pathway was active in all samples, 1 showed ER pathway heterogeneity; 11 had an active TGFbeta pathway, heterogeneic in 3; in one ER inactive tumor one (out of 5) samples was AR active. In 2 TN tumors TGFbeta and PI3K pathways were combined active, in one TN tumor heterogeneic TGFbeta activity was observed. PI3K pathway activity increased with malignancy grade and showed most intra-tumor variation. Overall, pathway heterogeneity was detected in over one third of tumors, least in Luminal A-like (2/9) compared to Luminal B-like (3/4) and triple negative (1/3) tumors. Extensive heterogeneity was found between primary breast cancer and metastases, and between metastatic locations of the same patient. ER pathway activity decreased in 5/9 patients and increased in 4/9 patients in one or more metastatic tumors; PI3K became active in metastasis of 2 patients and inactive in 1; the Wnt pathway became active in 3 patients in bone and ileum metastases; TGFbeta was lost in 5 patients, and became active in 1 bone metastasis; HH became active in 1 ovarian and AR in 1 bone metastasis. Conclusion Intra-tumor heterogeneity was lower in ER active compared to TN breast cancer, suggesting a need for multiple biopsies to adequately characterize TN for neoadjuvant therapy. Between primary tumor and metastases, heterogeneity was extensive indicating the need for pathway analysis on metastatic tumors prior to targeted treatment. Citation Format: Anja Van De Stolpe, Anne van Brussel, Cathy Moelans, Marcia A. Inda, Wim Verhaegh, Eveline den Biezen, Paul van Diest. Measuring functional signal transduction pathway activity on breast cancer tissue samples to determine intra-tumor heterogeneity and heterogeneity between primary and metastatic tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3690.

Abstract 3690: Measuring functional signal transduction pathway activity on breast cancer tissue samples to determine intra-tumor heterogeneity and heterogeneity between primary and metastatic tumors

Naive Bayes is a relatively simple classification method to, e.g., rate TV programs as interesting or uninteresting to a user. In case the training set consists of instances, chosen randomly from the instance space, the posterior probability estimates are random variables. Their statistical properties can be used to calculate confidence intervals around them, enabling more refined classification strategies than the usual argmax-operator. This may alleviate the cold-start problem and provide additional feedback to the user. In this paper, we give an explicit expression to estimate the variances of the posterior probability estimates from the training data and investigate the strategy that refrains from classification in case the confidence interval around the largest posterior probability overlaps with any of the other intervals. We show that the classification error rate can be significantly reduced at the cost of a lower coverage, i.e., the fraction of classifiable instances, in a TV-program recommender.

Incorporating confidence in a naive Bayesiah classifier

In a recommender-based digital video recorder, TV programs are considered for automatic recording on a hard disk. The choice of which programs to record depends on (?) the scores assigned to the programs by the recommender, (ii) the times and channels at which the programs are broadcast, and (Hi) the number of tuners available for recording. For a given set S of n programs that are broadcast in a given time interval, and a given number m of tuners, we consider the problem of determining a subset S' ⊆ S of programs with a maximum sum of scores that can be recorded with the m tuners. We show that this problem can be formulated as a min-cost flow problem and can be solved to optimality in O(mn2) time. In addition, we indicate how the min-cost flow approach can be adapted to take into account practical considerations such as uncertainties in the actual broadcast times of programs and programs that are broadcast multiple times in the given time interval. We present experimental results that suggest that, for realistic settings, near-optimal subsets can be determined on low-cost hardware.

Scheduling TV recordings for a recommender-based DVR

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