Quantum machine learning: a classical perspective

Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets are motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed-up classical machine learning algorithms. Here we review the literature in quantum machine learning and discuss perspectives for a mixed readership of classical machine learning and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in machine learning are identified as promising directions for the field. Practical questions, like how to upload classical data into quantum form, will also be addressed.Comment: v3 33 pages; typos corrected and references adde

Dimensionality Reduction and Pattern Recognition of Flow Regime Using Acoustic Data

In this study we investigated the novel application of Principal Component Analysis (PCA) in order to reduce the dimensionality of acoustic data. The acoustic data are recorded by fibre optic distributed acoustic sensors which are attached along a 3500 m pipe with a sampling frequency of 10 kHz and for a duration of 24 hours. Data collected from distributed acoustic sensors are very large and we need to identify the part that contains the most informative signals. The algorithm is applied to water, oil and gas datasets. We aimed to form a smaller dataset which preserves the pattern of the original dataset which is more efficient for further analysis. The result of this study will lead to automation of multiphase flow pattern recognition for oil and gas industry applications

The role of mentorship in protege performance

The role of mentorship on protege performance is a matter of importance to academic, business, and governmental organizations. While the benefits of mentorship for proteges, mentors and their organizations are apparent, the extent to which proteges mimic their mentors' career choices and acquire their mentorship skills is unclear. Here, we investigate one aspect of mentor emulation by studying mentorship fecundity---the number of proteges a mentor trains---with data from the Mathematics Genealogy Project, which tracks the mentorship record of thousands of mathematicians over several centuries. We demonstrate that fecundity among academic mathematicians is correlated with other measures of academic success. We also find that the average fecundity of mentors remains stable over 60 years of recorded mentorship. We further uncover three significant correlations in mentorship fecundity. First, mentors with small mentorship fecundity train proteges that go on to have a 37% larger than expected mentorship fecundity. Second, in the first third of their career, mentors with large fecundity train proteges that go on to have a 29% larger than expected fecundity. Finally, in the last third of their career, mentors with large fecundity train proteges that go on to have a 31% smaller than expected fecundity.Comment: 23 pages double-spaced, 4 figure

Manifold Elastic Net: A Unified Framework for Sparse Dimension Reduction

It is difficult to find the optimal sparse solution of a manifold learning based dimensionality reduction algorithm. The lasso or the elastic net penalized manifold learning based dimensionality reduction is not directly a lasso penalized least square problem and thus the least angle regression (LARS) (Efron et al. \cite{LARS}), one of the most popular algorithms in sparse learning, cannot be applied. Therefore, most current approaches take indirect ways or have strict settings, which can be inconvenient for applications. In this paper, we proposed the manifold elastic net or MEN for short. MEN incorporates the merits of both the manifold learning based dimensionality reduction and the sparse learning based dimensionality reduction. By using a series of equivalent transformations, we show MEN is equivalent to the lasso penalized least square problem and thus LARS is adopted to obtain the optimal sparse solution of MEN. In particular, MEN has the following advantages for subsequent classification: 1) the local geometry of samples is well preserved for low dimensional data representation, 2) both the margin maximization and the classification error minimization are considered for sparse projection calculation, 3) the projection matrix of MEN improves the parsimony in computation, 4) the elastic net penalty reduces the over-fitting problem, and 5) the projection matrix of MEN can be interpreted psychologically and physiologically. Experimental evidence on face recognition over various popular datasets suggests that MEN is superior to top level dimensionality reduction algorithms.Comment: 33 pages, 12 figure

Floral temperature and optimal foraging: is heat a feasible floral reward for pollinators?

As well as nutritional rewards, some plants also reward ectothermic pollinators with warmth. Bumble bees have some control over their temperature, but have been shown to forage at warmer flowers when given a choice, suggesting that there is some advantage to them of foraging at warm flowers (such as reducing the energy required to raise their body to flight temperature before leaving the flower). We describe a model that considers how a heat reward affects the foraging behaviour in a thermogenic central-place forager (such as a bumble bee). We show that although the pollinator should spend a longer time on individual flowers if they are warm, the increase in total visit time is likely to be small. The pollinator's net rate of energy gain will be increased by landing on warmer flowers. Therefore, if a plant provides a heat reward, it could reduce the amount of nectar it produces, whilst still providing its pollinator with the same net rate of gain. We suggest how heat rewards may link with plant life history strategies

Elongation, rooting and acclimatization of micropropagated shoots from mature material of hybrid larch

Factors were defined for elongation, rooting and acclimatization of micropropagated shoots of Larix x eurolepis Henry initiated from short shoot buds of plagiotropic stecklings serially propagated for 9 years from an 8-year-old tree. Initiation and multiplication were on Schenk and Hildebrandt (SH) medium supplemented with 5 μM 6-benzyladenine (BA) and 1 μM indole-butyric acid (IBA). Stem elongation was obtained in 36% of the shoots on SH medium containing 0.5 μM BA and 63% of the remaining non-elongated shoots initiated stem elongation after transfer on SH medium devoid of growth regulators. Rooting involved 2 steps: root induction on Campbell and Durzan mineral salts and Murashige and Skoog organic elements, both half-strength (CD-MS/2), supplemented with 1 μM of both naphthaleneacetic acid (NAA) and IBA, and root elongation following transfer to CD-MS/2 medium devoid of growth regulators. Repeating this 2-step sequence yielded up to 67% rooted shoots. Acclimatization of plantlets ranged from 83% to 100%. Over 300 plants were transferred to the greenhouse; some showed plagiotropic growth

Conditional mouse models demonstrate oncogene-dependent differences in tumor maintenance and recurrence

Diversity in the pathophysiology of breast cancer frustrates therapeutic progress. We need to understand how mechanisms activated by specific combinations of oncogenes, tumor suppressors, and hormonal signaling pathways govern response to therapy and prognosis. A recent series of investigations conducted by Chodosh and colleagues offers new insights into the similarities and differences between specific oncogenic pathways. Expression of three oncogenes relevant to pathways activated in human breast cancers (c-myc, activated neu and Wnt1) were targeted to murine mammary epithelial cells using the same transgenic tetracycline-responsive conditional gene expression system. While the individual transgenic lines demonstrate similarly high rates of tumor penetrance, rates of oncogene-independent tumor maintenance and recurrence following initial regression are significantly different, and are modifiable by mutations in specific cooperating oncogenes or loss of tumor suppressor gene expression. The experiments make three notable contributions. First, they illustrate that rates of tumor regression and recurrence following initial regression are dependent upon the pathways activated by the initiating oncogene. The experiments also demonstrate that altered expression or mutation of specific cooperating oncogenes or tumor suppressor genes results in different rates of tumor regression and recurrence. Finally, they exemplify the power of conditional mouse models for elucidating how specific molecular mechanisms give rise to the complexity of human cancer