Device Independence and the Quest towards Physical Limits of Privacy

There is a looming threat over current methods of data encryption through advances in quantum computation. Interestingly, this potential threat can be countered through the use of quantum resources such as coherent superposition, entanglement and inherent randomness. These, together with non-clonability of arbitrary quantum states, offer provably secure means of sharing encryption keys between two parties. This physically assured privacy is however provably secure only in theory but not in practice. Device independent approaches seek to provide physically assured privacy of devices of untrusted origin. The quest towards realization of such devices is predicated on conducting loop-hole-free Bell tests which require the use of certified quantum random number generators. The experimental apparatuses for conducting such tests themselves use non-ideal sources, detectors and optical components making such certification extremely difficult. This expository chapter presents a brief overview (not a review) of Device Independence and the conceptual and practical difficulties it entails

Automotive Waste Heat Recovery by Thermoelectric Generator Technology

Automotive exhaust thermoelectric generators (AETEG) are gaining significant importance wherein a direct conversion of exhaust waste heat into electricity allows for a reduction in fuel consumption. Over the past two decades, extensive progress has been made in materials research, modules and thermoelectric generator (TEG) system. Many prototypes using BiTe, CoSb3 and half Heusler materials have been developed and tested for efficiency in different engines. The role of exhaust flow rate, temperature and heat exchanger type on the performance of AETEG is investigated deeply. This chapter reviews the progress made so far in the AETEG technology. Section 1 gives a brief introduction; section 2 gives a description of the technology and section 3, the construction details of a typical AETEG. The performance evaluation of AETEG is discussed in Section 4, application of TEG using engine coolant heat is discussed in Section 5 and TEGs for hybrid vehicles are described in Section 6. The parasitic losses due to AETEG and the conditioning of the power produced for practical applications using the maximum power point tracking technique are discussed in Sections 7 and 8, respectively. Finally, in Section 9, cost analysis and the challenges associated with the commercialization of AETEG is presented

Combined node and link partitions method for finding overlapping communities in complex networks

Community detection in complex networks is a fundamental data analysis task in various domains, and how to effectively find overlapping communities in real applications is still a challenge. In this work, we propose a new unified model and method for finding the best overlapping communities on the basis of the associated node and link partitions derived from the same framework. Specifically, we first describe a unified model that accommodates node and link communities (partitions) together, and then present a nonnegative matrix factorization method to learn the parameters of the model. Thereafter, we infer the overlapping communities based on the derived node and link communities, i.e., determine each overlapped community between the corresponding node and link community with a greedy optimization of a local community function conductance. Finally, we introduce a model selection method based on consensus clustering to determine the number of communities. We have evaluated our method on both synthetic and real-world networks with ground-truths, and compared it with seven state-of-the-art methods. The experimental results demonstrate the superior performance of our method over the competing ones in detecting overlapping communities for all analysed data sets. Improved performance is particularly pronounced in cases of more complicated networked community structures

FastFlow: AI for Fast Urban Wind Velocity Prediction

Data-driven approaches, including deep learning, have shown great promise as surrogate models across many domains. These extend to various areas in sustainability. An interesting direction for which data-driven methods have not been applied much yet is in the quick quantitative evaluation of urban layouts for planning and design. In particular, urban designs typically involve complex trade-offs between multiple objectives, including limits on urban build-up and/or consideration of urban heat island effect. Hence, it can be beneficial to urban planners to have a fast surrogate model to predict urban characteristics of a hypothetical layout, e.g. pedestrian-level wind velocity, without having to run computationally expensive and time-consuming high-fidelity numerical simulations. This fast surrogate can then be potentially integrated into other design optimization frameworks, including generative models or other gradient-based methods. Here we present the use of CNNs for urban layout characterization that is typically done via high-fidelity numerical simulation. We further apply this model towards a first demonstration of its utility for data-driven pedestrian-level wind velocity prediction. The data set in this work comprises results from high-fidelity numerical simulations of wind velocities for a diverse set of realistic urban layouts, based on randomized samples from a real-world, highly built-up urban city. We then provide prediction results obtained from the trained CNN, demonstrating test errors of under 0.1 m/s for previously unseen urban layouts. We further illustrate how this can be useful for purposes such as rapid evaluation of pedestrian wind velocity for a potential new layout. It is hoped that this data set will further accelerate research in data-driven urban AI, even as our baseline model facilitates quantitative comparison to future methods

Magnetic properties of mechanically milled Sm-Co permanent magnetic materials with the structure

The magnetic properties of Sm(Co,Fe,Cu,Zr) 7 compound with the TbCu 7 structure are studied for the mechanically milled samples. The coercivity could be varied, without affecting the saturation magnetization, from 44 kA/m for the micron sized particles to 280 kA/m by reducing the particle size to sub-micron size (600-900 nm) using high-energy ball milling. The enhancement in the coercivity is attributed to the particles approaching single domain size. The presence of dipolar coupling suggests that the grain sizes are well above the exchange length for the milled samples. The thermal measurements indicate that the compound with the TbCu 7 structure is not stable at high temperatures beyond 743 K

Big Domains Are Novel Ca2+-Binding Modules: Evidences from Big Domains of Leptospira Immunoglobulin-Like (Lig) Proteins

binds to a Big domains, which would provide a novel functional role of the proteins containing Big fold. with dissociation constants of 2–4 µM. Lig A9 and Lig A10 domains fold well with moderate thermal stability, have β-sheet conformation and form homodimers. Fluorescence spectra of Big domains show a specific doublet (at 317 and 330 nm), probably due to Trp interaction with a Phe residue. Equilibrium unfolding of selected Big domains is similar and follows a two-state model, suggesting the similarity in their fold. binding

The epidemiology of coronary heart disease : A review

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/31999/1/0000041.pd

Breast cancer management pathways during the COVID-19 pandemic: outcomes from the UK ‘Alert Level 4’ phase of the B-MaP-C study

Abstract: Background: The B-MaP-C study aimed to determine alterations to breast cancer (BC) management during the peak transmission period of the UK COVID-19 pandemic and the potential impact of these treatment decisions. Methods: This was a national cohort study of patients with early BC undergoing multidisciplinary team (MDT)-guided treatment recommendations during the pandemic, designated ‘standard’ or ‘COVID-altered’, in the preoperative, operative and post-operative setting. Findings: Of 3776 patients (from 64 UK units) in the study, 2246 (59%) had ‘COVID-altered’ management. ‘Bridging’ endocrine therapy was used (n = 951) where theatre capacity was reduced. There was increasing access to COVID-19 low-risk theatres during the study period (59%). In line with national guidance, immediate breast reconstruction was avoided (n = 299). Where adjuvant chemotherapy was omitted (n = 81), the median benefit was only 3% (IQR 2–9%) using ‘NHS Predict’. There was the rapid adoption of new evidence-based hypofractionated radiotherapy (n = 781, from 46 units). Only 14 patients (1%) tested positive for SARS-CoV-2 during their treatment journey. Conclusions: The majority of ‘COVID-altered’ management decisions were largely in line with pre-COVID evidence-based guidelines, implying that breast cancer survival outcomes are unlikely to be negatively impacted by the pandemic. However, in this study, the potential impact of delays to BC presentation or diagnosis remains unknown