Foothill: A Quasiconvex Regularization for Edge Computing of Deep Neural Networks

Deep neural networks (DNNs) have demonstrated success for many supervised learning tasks, ranging from voice recognition, object detection, to image classification. However, their increasing complexity might yield poor generalization error that make them hard to be deployed on edge devices. Quantization is an effective approach to compress DNNs in order to meet these constraints. Using a quasiconvex base function in order to construct a binary quantizer helps training binary neural networks (BNNs) and adding noise to the input data or using a concrete regularization function helps to improve generalization error. Here we introduce foothill function, an infinitely differentiable quasiconvex function. This regularizer is flexible enough to deform towards $L_1$ and $L_2$ penalties. Foothill can be used as a binary quantizer, as a regularizer, or as a loss. In particular, we show this regularizer reduces the accuracy gap between BNNs and their full-precision counterpart for image classification on ImageNet.Comment: Accepted in 16th International Conference of Image Analysis and Recognition (ICIAR 2019

Tackling the Tibetan Plateau in a down suit: Insights into thermoregulation by bar-headed geese during migration

This is the final version. Available from Company of Biologists via the DOI in this recordData accessibility: Following the manuscript being accepted data will be uploaded to a public repository such as Dryad.Birds migrating through extreme environments can experience a range of challenges while matching the demands of flight, including highly variable ambient temperatures, humidity and oxygen levels. However, there has been limited research into avian thermoregulation during migration in extreme environments. This study aimed to investigate the effect of flight performance and high-altitude on body temperature (Tb) of free flying bar-headed geese (Anser indicus), a species that completes a high-altitude trans-Himalayan migration through very cold, hypoxic environments. We measured abdominal Tb, along with altitude (via changes in barometric pressure), heart rate and body acceleration of bar-headed geese during their migration across the Tibetan Plateau. Bar-headed geese vary the circadian rhythm of Tb in response to migration, with peak daily Tb during daytime hours outside of migration but early in the morning or overnight during migration, reflecting changes in body acceleration. However, during flights changes in Tb were not consistent with changes in flight performance (as measured by heart rate or rate of ascent) or altitude. Overall, our results suggest that bar-headed geese are able to thermoregulate during high-altitude migration, maintaining Tb within a relatively narrow range despite appreciable variation in flight intensity and environmental conditions.Biotechnology and Biological Sciences Research Council (BBSRC)Natural Sciences and Engineering Research Council of Canada (NSERC)Max Planck Institute for OrnithologyUS Geological SurveyWestern Ecological and Patuxent Wildlife Research Centers, Avian Influenza Programm

Bayesian and Markov chain Monte Carlo methods for identifying nonlinear systems in the presence of uncertainty

In this paper, the authors outline the general principles behind an approach to Bayesian system identification and highlight the benefits of adopting a Bayesian framework when attempting to identify models of nonlinear dynamical systems in the presence of uncertainty. It is then described how, through a summary of some key algorithms, many of the potential difficulties associated with a Bayesian approach can be overcome through the use of Markov chain Monte Carlo (MCMC) methods. The paper concludes with a case study, where an MCMC algorithm is used to facilitate the Bayesian system identification of a nonlinear dynamical system from experimentally observed acceleration time histories

The influence of α-actinin-3 deficiency on bone remodelling markers in young men

There is a large individual variation in the bone remodelling markers (BRMs) osteocalcin (OC), procollagen 1 N-terminal propeptide (P1NP) and β-isomerized C-terminal telopeptide (β-CTx), as well as undercarboxylated osteocalcin (ucOC), at rest and in response to exercise. α-actinin-3 (ACTN3), a sarcomeric protein, is expressed in skeletal muscle and osteoblasts and may influence BRM levels and the cross-talk between muscle and bone. We tested the levels of serum BRMs in α-actinin-3 deficient humans (ACTN3 XX) at baseline, and following a single bout of exercise. Forty-three healthy Caucasian individuals were divided into three groups (ACTN3 XX, n = 13; ACTN3 RX, n = 16; ACTN3 RR, n = 14). Participants completed a single session of High Intensity Interval Exercise (HIIE) on a cycle ergometer (8 × 2-min intervals at 85% of maximal power). Blood samples were taken before, immediately after, and three hours post exercise to identify the peak changes in serum BRMs. There was a stepwise increase in resting serum BRMs across the ACTN3 genotypes (XX \u3e RX \u3e RR) with significantly higher levels of tOC ~ 26%, P1NP ~ 34%, and β-CTX (~ 33%) in those with ACTN3 XX compared to ACTN3 RR. Following exercise BRMs and ucOC were higher in all three ACTN3 genotypes, with no significant differences between groups. Serum levels of tOC, P1NP and β-CTX are higher in men with ACTN3 XX genotype (α-actinin-3 deficiency) compared to RR and RX. It appears that the response of BRMs and ucOC to exercise is not explained by the ACTN3 genotype

Genomic analysis and comparison of two gonorrhoea outbreaks

© 2016 Didelot et al.Gonorrhea is a sexually transmitted disease causing growing concern, with a substantial increase in reported incidence over the past few years in the United Kingdom and rising levels of resistance to a wide range of antibiotics. Understanding its epidemiology is therefore of major biomedical importance, not only on a population scale but also at the level of direct transmission. However, the molecular typing techniques traditionally used for gonorrhea infections do not provide sufficient resolution to investigate such fine-scale patterns. Here we sequenced the genomes of 237 isolates from two local collections of isolates from Sheffield and London, each of which was resolved into a single type using traditional methods. The two data sets were selected to have different epidemiological properties: the Sheffield data were collected over 6 years from a predominantly heterosexual population, whereas the London data were gathered within half a year and strongly associated with men who have sex with men. Based on contact tracing information between individuals in Sheffield, we found that transmission is associated with a median time to most recent common ancestor of 3.4 months, with an upper bound of 8 months, which we used as a criterion to identify likely transmission links in both data sets. In London, we found that transmission happened predominantly between individuals of similar age, sexual orientation, and location and also with the same HIV serostatus, which may reflect serosorting and associated risk behaviors. Comparison of the two data sets suggests that the London epidemic involved about ten times more cases than the Sheffield outbreak. IMPORTANCE: The recent increases in gonorrhea incidence and antibiotic resistance are cause for public health concern. Successful intervention requires a better understanding of transmission patterns, which is not uncovered by traditional molecular epidemiology techniques. Here we studied two outbreaks that took place in Sheffield and London, United Kingdom. We show that whole-genome sequencing provides the resolution to investigate direct gonorrhea transmission between infected individuals. Combining genome sequencing with rich epidemiological information about infected individuals reveals the importance of several transmission routes and risk factors, which can be used to design better control measures

Quantum Non-demolition Detection of Single Microwave Photons in a Circuit

Thorough control of quantum measurement is key to the development of quantum information technologies. Many measurements are destructive, removing more information from the system than they obtain. Quantum non-demolition (QND) measurements allow repeated measurements that give the same eigenvalue. They could be used for several quantum information processing tasks such as error correction, preparation by measurement, and one-way quantum computing. Achieving QND measurements of photons is especially challenging because the detector must be completely transparent to the photons while still acquiring information about them. Recent progress in manipulating microwave photons in superconducting circuits has increased demand for a QND detector which operates in the gigahertz frequency range. Here we demonstrate a QND detection scheme which measures the number of photons inside a high quality-factor microwave cavity on a chip. This scheme maps a photon number onto a qubit state in a single-shot via qubit-photon logic gates. We verify the operation of the device by analyzing the average correlations of repeated measurements, and show that it is 90% QND. It differs from previously reported detectors because its sensitivity is strongly selective to chosen photon number states. This scheme could be used to monitor the state of a photon-based memory in a quantum computer.Comment: 5 pages, 4 figures, includes supplementary materia

Do Bar-Headed Geese Train for High Altitude Flights?

This is the author accepted manuscript. The final version is available from OUP via the DOI in this recordSYNOPSIS: Exercise at high altitude is extremely challenging, largely due to hypobaric hypoxia (low oxygen levels brought about by low air pressure). In humans, the maximal rate of oxygen consumption decreases with increasing altitude, supporting progressively poorer performance. Bar-headed geese (Anser indicus) are renowned high altitude migrants and, although they appear to minimize altitude during migration where possible, they must fly over the Tibetan Plateau (mean altitude 4800 m) for much of their annual migration. This requires considerable cardiovascular effort, but no study has assessed the extent to which bar-headed geese may train prior to migration for long distances, or for high altitudes. Using implanted loggers that recorded heart rate, acceleration, pressure, and temperature, we found no evidence of training for migration in bar-headed geese. Geese showed no significant change in summed activity per day or maximal activity per day. There was also no significant change in maximum heart rate per day or minimum resting heart rate, which may be evidence of an increase in cardiac stroke volume if all other variables were to remain the same. We discuss the strategies used by bar-headed geese in the context of training undertaken by human mountaineers when preparing for high altitude, noting the differences between their respective cardiovascular physiology.This work was supported by the UK Biotechnology and Biological Sciences Research Council [BBSRC; BB/FO15615/1 to C.M.B. and P.J.B.]. Authors were supported by a Natural Sciences and Engineering Research Council of Canada (NSERC) award [W.K.M.], and the FAO through the Animal Health Service EMPRES surveillance program

Musculoskeletal modelling of the Nile crocodile (Crocodylus niloticus) hindlimb: Effects of limb posture on leverage during terrestrial locomotion

We developed a three-dimensional, computational biomechanical model of a juvenile Nile crocodile (Crocodylus niloticus) pelvis and hindlimb, composed of 47 pelvic limb muscles, to investigate muscle function. We tested whether crocodiles, which are known to use a variety of limb postures during movement, use limb orientations (joint angles) that optimise the moment arms (leverages) or moment-generating capacities of their muscles during different limb postures ranging from a high walk to a sprawling motion. We also describe the three-dimensional (3D) kinematics of the crocodylian hindlimb during terrestrial locomotion across an instrumented walkway and a treadmill captured via X-ray Reconstruction of Moving Morphology (biplanar fluoroscopy; ‘XROMM’). We reconstructed the 3D positions and orientations of each of the hindlimb bones and used dissection data for muscle lines of action to reconstruct a focal, subject-specific 3D musculoskeletal model. Motion data for different styles of walking (a high, crouched, bended and two types of sprawling motion) were fed into the 3D model to identify whether any joints adopted near-optimal poses for leverage across each of the behaviours. We found that (1) the hip adductors and knee extensors had their largest leverages during sprawling postures and (2) more erect postures typically involved greater peak moment arms about the hip (flexion-extension), knee (flexion) and metatarsophalangeal (flexion) joints. The results did not fully support the hypothesis that optimal poses are present during different locomotory behaviours because the peak capacities were not always reached around mid-stance phase. Furthermore, we obtained few clear trends for isometric moment-generating capacities. Therefore, perhaps peak muscular leverage in Nile crocodiles is instead reached either in early/late stance or possibly during swing phase or other locomotory behaviours that were not studied here, such as non-terrestrial movement. Alternatively, our findings could reflect a trade-off between having to execute different postures, meaning that hindlimb muscle leverage is not optimised for any singular posture or behaviour. Our model, however, provides a comprehensive set of 3D estimates of muscle actions in extant crocodiles which can form a basis for investigating muscle function in extinct archosaurs

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