Immigrant Entrepreneurs in the Massachusetts Biotechnology Industry (2007)

Immigrant entrepreneurs are co-founders in 25.7 percent of Massachusetts Biotechnology firms. In 2006, these immigrant-founded biotechnology companies produced over $7.6 billion dollars in sales and employed over 4,000 workers. The foreign-born founders came from across the globe but in larger numbers from Europe, Canada or Asia. Their firms specialize in the most complex, risky, life science-intensive aspects of biotechnology to seek knowledge directly applicable to human health. Biotechnology is a crucial industry for Massachhusetts and the evidence strongly suggests that immigrants have been key contributors to this industry by establishing new businesses as well as bringing intellectual capital and thereby contributing significantly to the overall economic growth of the Commonwealth

Trajectory-Based Off-Policy Deep Reinforcement Learning

Policy gradient methods are powerful reinforcement learning algorithms and have been demonstrated to solve many complex tasks. However, these methods are also data-inefficient, afflicted with high variance gradient estimates, and frequently get stuck in local optima. This work addresses these weaknesses by combining recent improvements in the reuse of off-policy data and exploration in parameter space with deterministic behavioral policies. The resulting objective is amenable to standard neural network optimization strategies like stochastic gradient descent or stochastic gradient Hamiltonian Monte Carlo. Incorporation of previous rollouts via importance sampling greatly improves data-efficiency, whilst stochastic optimization schemes facilitate the escape from local optima. We evaluate the proposed approach on a series of continuous control benchmark tasks. The results show that the proposed algorithm is able to successfully and reliably learn solutions using fewer system interactions than standard policy gradient methods.Comment: Includes appendix. Accepted for ICML 201

Optimising Spatial and Tonal Data for PDE-based Inpainting

Some recent methods for lossy signal and image compression store only a few selected pixels and fill in the missing structures by inpainting with a partial differential equation (PDE). Suitable operators include the Laplacian, the biharmonic operator, and edge-enhancing anisotropic diffusion (EED). The quality of such approaches depends substantially on the selection of the data that is kept. Optimising this data in the domain and codomain gives rise to challenging mathematical problems that shall be addressed in our work. In the 1D case, we prove results that provide insights into the difficulty of this problem, and we give evidence that a splitting into spatial and tonal (i.e. function value) optimisation does hardly deteriorate the results. In the 2D setting, we present generic algorithms that achieve a high reconstruction quality even if the specified data is very sparse. To optimise the spatial data, we use a probabilistic sparsification, followed by a nonlocal pixel exchange that avoids getting trapped in bad local optima. After this spatial optimisation we perform a tonal optimisation that modifies the function values in order to reduce the global reconstruction error. For homogeneous diffusion inpainting, this comes down to a least squares problem for which we prove that it has a unique solution. We demonstrate that it can be found efficiently with a gradient descent approach that is accelerated with fast explicit diffusion (FED) cycles. Our framework allows to specify the desired density of the inpainting mask a priori. Moreover, is more generic than other data optimisation approaches for the sparse inpainting problem, since it can also be extended to nonlinear inpainting operators such as EED. This is exploited to achieve reconstructions with state-of-the-art quality. We also give an extensive literature survey on PDE-based image compression methods

Combining Monte-Carlo and hyper-heuristic methods for the multi-mode resource-constrained multi-project scheduling problem

Multi-mode resource and precedence-constrained project scheduling is a well-known challenging real-world optimisation problem. An important variant of the problem requires scheduling of activities for multiple projects considering availability of local and global resources while respecting a range of constraints. A critical aspect of the benchmarks addressed in this paper is that the primary objective is to minimise the sum of the project completion times, with the usual makespan minimisation as a secondary objective. We observe that this leads to an expected different overall structure of good solutions and discuss the effects this has on the algorithm design. This paper presents a carefully-designed hybrid of Monte-Carlo tree search, novel neighbourhood moves, memetic algorithms, and hyper-heuristic methods. The implementation is also engineered to increase the speed with which iterations are performed, and to exploit the computing power of multicore machines. Empirical evaluation shows that the resulting information-sharing multi-component algorithm significantly outperforms other solvers on a set of “hidden” instances, i.e. instances not available at the algorithm design phase

Faster Black-Box Algorithms Through Higher Arity Operators

We extend the work of Lehre and Witt (GECCO 2010) on the unbiased black-box model by considering higher arity variation operators. In particular, we show that already for binary operators the black-box complexity of \leadingones drops from $\Theta(n^2)$ for unary operators to $O(n \log n)$. For \onemax, the $\Omega(n \log n)$ unary black-box complexity drops to O(n) in the binary case. For $k$-ary operators, $k \leq n$, the \onemax-complexity further decreases to $O(n/\log k)$.Comment: To appear at FOGA 201

Probabilistic Recurrent State-Space Models

State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification. Deterministic versions of SSMs (e.g. LSTMs) proved extremely successful in modeling complex time series data. Fully probabilistic SSMs, however, are often found hard to train, even for smaller problems. To overcome this limitation, we propose a novel model formulation and a scalable training algorithm based on doubly stochastic variational inference and Gaussian processes. In contrast to existing work, the proposed variational approximation allows one to fully capture the latent state temporal correlations. These correlations are the key to robust training. The effectiveness of the proposed PR-SSM is evaluated on a set of real-world benchmark datasets in comparison to state-of-the-art probabilistic model learning methods. Scalability and robustness are demonstrated on a high dimensional problem

Training an automated circulating tumor cell classifier when the true classification is uncertain

Circulating tumor cell (CTC) and tumor-derived extracellular vesicle (tdEV) loads are prognostic factors of survival in patients with carcinoma. The current method of CTC enumeration relies on operator review and, unfortunately, has moderate interoperator agreement (Fleiss’ kappa 0.60) due to difficulties in classifying CTC-like events. We compared operator review, ACCEPT automated image processing, and refined the output of a deep-learning algorithm to identify CTC and tdEV for the prediction of survival in patients with metastatic and nonmetastatic cancers. Operator review is only defined for CTC. Refinement was performed using automatic contrast maximization CM-CTC of events detected in cancer and in benign samples (CM-CTC). We used 418 samples from benign diseases, 6,293 from nonmetastatic breast, 2,408 from metastatic breast, and 698 from metastatic prostate cancer to train, test, optimize, and evaluate CTC and tdEV enumeration. For CTC identification, the CM-CTC performed best on metastatic/nonmetastatic breast cancer, respectively, with a hazard ratio (HR) for overall survival of 2.6/2.1 vs. 2.4/1.4 for operator CTC and 1.2/0.8 for ACCEPT-CTC. For tdEV identification, CM-tdEV performed best with an HR of 1.6/2.9 vs. 1.5/1.0 with ACCEPT-tdEV. In conclusion, contrast maximization is effective even though it does not utilize domain knowledge

Función de los confórmeros de ataque cercano en la acilación enantioselectiva del (R,S)-propranolol catalizada por lipasa B de Candida antarctica

La lipasa B de Candida antarctica (CalB) se ha utilizado en la acilación quimio- y enantioselectiva del racemato (R,S)-propranolol. CalB tiene enantioselectividad moderada (E=63) por el R-propranolol. La enantioselectividad, se origina en la reacción de transferencia del grupo acilo desde la serina catalítica, acilada, al propranolol. La fase inicial de esta reacción involucra la formación de complejos de Michaelis y posteriormente conformaciones de ataque cercano. El análisis de las conformaciones de ataque cercano ha permitido en varios casos explicar el origen de la catálisis o reproducir el efecto catalítico. En este trabajo se profundiza en la comprensión la función de las conformaciones de ataque cercano en la enantioselectividad de la acilación del (R,S)-propranolol catalizada por CalB. Para lo anterior se realizó un estudio detallado de los complejos de Michaelis y de las conformaciones de ataque cercano del paso enantioselectivo de la reacción de acilación del (R,S)-propranolol utilizando un protocolo de dinámica molecular QM/MM (SCCDFTB/CHARMM) utilizando 6 distribuciones de velocidades iniciales y simulaciones de 2,5 ns. Se estudiaron 7 complejos CalB-propranolol. Los enlaces de hidrógeno del sitio activo de CalB acilada relevantes para la actividad catalítica fueron estables en todas las simulaciones. Las poblaciones de los complejos de Michaelis y de las conformaciones de ataque cercano son dependientes de la distribución de las velocidades iniciales de la dinámica molecular. La enantioselectividad moderada de CalB acilada, encontrada experimentalmente, puede ser parcialmente atribuida a la alta población de conformaciones de ataque cercano observada para el S-propranolol.Candida antarctica lipase B (CalB) has been used for chemo- and enantioselective acylation of racemic (R,S)-propranolol, with moderate enantioselectivity (E=63) for R-propranolol. The enantioselective step in this reaction is the transfer of an acyl group from the catalytic acylated serine to propranolol. The initial phase of this reaction involves the formation of Michaelis complexes, followed by the formation of near-attack complexes. The analysis of the near-attack complexes has in several cases permitted to explain the origin of the catalysis or to reproduce the catalytic effect. The aim of this study was improve the understanding of the role of the near-attack complexes for the enantioselectivity of the acylation of (R,S)-propranolol, catalyzed by CalB. To this purpose a detailed investigation of the Michaelis and near-attack complexes of the enantioselective step of the acylation of (R,S)-propranolol using QM/MM molecular dynamics was performed. Several simulations (each 2,5 ns) with different initial velocity distributions were performed. In total seven CalB-propranolol complexes were studied. The hydrogen bonds in the active site of CalB, which are relevant for the catalytic activity, are stable in all simulations. The lifetime of the Michaelis complexes is considerably shorter than the simulation time. Conclusions: The populations of the Michaelis and near-attack complexes depend on the initial velocity distribution in the molecular dynamics simulations. The experimentally observed moderate enantioselectivity may be partially attributed to the high population of near-attack conformations of S-propranolol