Performance and limitations of the QAOA at constant levels on large sparse hypergraphs and spin glass models

The Quantum Approximate Optimization Algorithm (QAOA) is a general purpose quantum algorithm designed for combinatorial optimization. We analyze its expected performance and prove concentration properties at any constant level (number of layers) on ensembles of random combinatorial optimization problems in the infinite size limit. These ensembles include mixed spin models and Max-$q$-XORSAT on sparse random hypergraphs. To enable our analysis, we prove a generalization of the multinomial theorem which is a technical result of independent interest. We then show that the performance of the QAOA at constant levels for the pure $q$-spin model matches asymptotically the ones for Max-$q$-XORSAT on random sparse Erd\H{o}s-R\'{e}nyi hypergraphs and every large-girth regular hypergraph. Through this correspondence, we establish that the average-case value produced by the QAOA at constant levels is bounded away from optimality for pure $q$-spin models when $q\ge 4$ is even. This limitation gives a hardness of approximation result for quantum algorithms in a new regime where the whole graph is seen.Comment: 12+46 page

Structure and Aggregation of a Helix-Forming Polymer

We have studied the competition between helix formation and aggregation for a simple polymer model. We present simulation results for a system of two such polymers, examining the potential of mean force, the balance between inter and intramolecular interactions, and the promotion or disruption of secondary structure brought on by the proximity of the two molecules. In particular, we demonstrate that proximity between two such molecules can stabilize secondary structure. However, for this model, observed secondary structure is not stable enough to prevent collapse of the system into an unstructured globule.Comment: Accepted to the Journal of Chemical Physic

The entrepreneurial process and online social networks : forecasting survival rate

To launch a new business, entrepreneurs search for information and resources through their networks. We are concerned with collaboration among entrepreneurs with a network, and with the impact this has on new venture survival. Using entrepreneurs’ network data extracted from their respective online social networks, our paper develops a simulation model of the entrepreneurial process and its outcomes in terms of growth and survival. Findings from 273 entrepreneurs reveal that initial wealth at start-up, network density, and time to first collaboration have an impact on the probability of survival. We show that using numerical simulation, and based on one’s social network, the survival time of a start-up can be forecasted

Comparative exergy analysis of direct alcohol fuel cells using fuel mixtures

Within the last years there has been increasing interest in direct liquid fuel cells as power sources for portable devices and, in the future, power plants for electric vehicles and other transport media as ships will join those applications. Methanol is considerably more convenient and easy to use than gaseous hydrogen and a considerable work is devoted to the development of direct methanol fuel cells. But ethanol has much lower toxicity and from an ecological viewpoint ethanol is exceptional among all other types of fuel as is the only chemical fuel in renewable supply. The aim of this study is to investigate the possibility of using direct alcohol fuel cells fed with alcohol mixtures. For this purpose, a comparative exergy analysis of a direct alcohol fuel cell fed with alcohol mixtures against the same fuel cell fed with single alcohols is performed. The exergetic efficiency and the exergy loss and destruction are calculated and compared in each case. When alcohol mixtures are fed to the fuel cell, the contribution of each fuel to the fuel cell performance is weighted attending to their relative proportion in the aqueous solution. The optimum alcohol composition for methanol/ethanol mixtures has been determined

Electrolyzer Design for Flexible Decoupled Water Splitting and Organic Upgrading with Electron Reservoirs

The Bigger Picture Electrocatalytic water splitting is a green approach to producing clean H2 fuel, especially when it is driven by renewable energy sources. Conventional water electrolysis always produces H2 and O2 simultaneously under corrosive acidic or alkaline conditions with large voltage inputs, posing safety concerns of H2/O2 mixing. Therefore, it is desirable to develop a new electrolyzer design for decoupled water splitting in an eco-friendly neutral solution with small voltage inputs to enable separated H2 and O2 evolution. Herein, we report (ferrocenylmethyl)trimethylammonium chloride and Na4[Fe(CN)6] as proton-independent electron reservoirs for achieving separated H2 and O2 evolution in near-neutral solution driven by electricity or solar cells under sunlight irradiation. Na4[Fe(CN)6] can also integrate H2 evolution with organic oxidation to yield H2 and high-value organic products. This work offers promising economic and safety advantages for sustainable H2 production and organic transformation

TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance

We propose a geometric deep-learning-based framework, TractGeoNet, for performing regression using diffusion magnetic resonance imaging (dMRI) tractography and associated pointwise tissue microstructure measurements. By employing a point cloud representation, TractGeoNet can directly utilize pointwise tissue microstructure and positional information from all points within a fiber tract. To improve regression performance, we propose a novel loss function, the Paired-Siamese Regression loss, which encourages the model to focus on accurately predicting the relative differences between regression label scores rather than just their absolute values. In addition, we propose a Critical Region Localization algorithm to identify highly predictive anatomical regions within the white matter fiber tracts for the regression task. We evaluate the effectiveness of the proposed method by predicting individual performance on two neuropsychological assessments of language using a dataset of 20 association white matter fiber tracts from 806 subjects from the Human Connectome Project. The results demonstrate superior prediction performance of TractGeoNet compared to several popular regression models. Of the twenty tracts studied, we find that the left arcuate fasciculus tract is the most highly predictive of the two studied language performance assessments. The localized critical regions are widespread and distributed across both hemispheres and all cerebral lobes, including areas of the brain considered important for language function such as superior and anterior temporal regions, pars opercularis, and precentral gyrus. Overall, TractGeoNet demonstrates the potential of geometric deep learning to enhance the study of the brain's white matter fiber tracts and to relate their structure to human traits such as language performance.Comment: 28 pages, 7 figure

Effect of caponization on muscle, liver and adipose tissue fatty acid composition, lipid peroxidation, and cholesterol in breast muscle of Cobb 500 broilers

Capon is the surgical removal of testes from roosters which promotes the accumulation of intramuscular fat and affects fatty acid composition of poultry meat. We report effect of caponization on muscle, liver and adipose tissue fatty acid composition, lipid peroxidation and cholesterol content in breast muscle of broilers. Groups of 30 male 1-d old commercial Cobb 500 broilers were caponized at 21 days of age. Controls were 30 intact birds. Each treatment group consisted of 6 replicates (cages) with 5 birds in each cage. Broilers were fed commercial feed and slaughtered at 40 days. Major fatty acids in breast muscle and adipose tissue were oleic (C18:1, n-9), palmitic (C16:0) and linoleic (C18:2, n-6) and stearic (C18:0). Caponization did not affect fatty acid composition of muscle, liver or adipose tissues or lipid peroxidation of meat. However, we did find lower (P < 0.018) cholesterol (0.66 ± 0.02mg/ml) in breast muscle from caponized birds (n=9) compared with 9 intact controls (0.77 ± 0.04mg/ml). Our findings suggest that caponization does not affect free fatty acid composition or lipid peroxidation but may reduce cholesterol in tissues of broilers

Linear approaches to intramolecular Förster Resonance Energy Transfer probe measurements for quantitative modeling

Numerous unimolecular, genetically-encoded Forster Resonance Energy Transfer (FRET) probes for monitoring biochemical activities in live cells have been developed over the past decade. As these probes allow for collection of high frequency, spatially resolved data on signaling events in live cells and tissues, they are an attractive technology for obtaining data to develop quantitative, mathematical models of spatiotemporal signaling dynamics. However, to be useful for such purposes the observed FRET from such probes should be related to a biological quantity of interest through a defined mathematical relationship, which is straightforward when this relationship is linear, and can be difficult otherwise. First, we show that only in rare circumstances is the observed FRET linearly proportional to a biochemical activity. Therefore in most cases FRET measurements should only be compared either to explicitly modeled probes or to concentrations of products of the biochemical activity, but not to activities themselves. Importantly, we find that FRET measured by standard intensity-based, ratiometric methods is inherently non-linear with respect to the fraction of probes undergoing FRET. Alternatively, we find that quantifying FRET either via (1) fluorescence lifetime imaging (FLIM) or (2) ratiometric methods where the donor emission intensity is divided by the directly-excited acceptor emission intensity (denoted R&lt;sub&gt;alt&lt;/sub&gt;) is linear with respect to the fraction of probes undergoing FRET. This linearity property allows one to calculate the fraction of active probes based on the FRET measurement. Thus, our results suggest that either FLIM or ratiometric methods based on R&lt;sub&gt;alt&lt;/sub&gt; are the preferred techniques for obtaining quantitative data from FRET probe experiments for mathematical modeling purpose

Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices

Federated Learning (FL) is a technique to train models using data distributed across devices. Differential Privacy (DP) provides a formal privacy guarantee for sensitive data. Our goal is to train a large neural network language model (NNLM) on compute-constrained devices while preserving privacy using FL and DP. However, the DP-noise introduced to the model increases as the model size grows, which often prevents convergence. We propose Partial Embedding Updates (PEU), a novel technique to decrease noise by decreasing payload size. Furthermore, we adopt Low Rank Adaptation (LoRA) and Noise Contrastive Estimation (NCE) to reduce the memory demands of large models on compute-constrained devices. This combination of techniques makes it possible to train large-vocabulary language models while preserving accuracy and privacy