10,924 research outputs found

    Search-time Efficient Device Constraints-Aware Neural Architecture Search

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    Edge computing aims to enable edge devices, such as IoT devices, to process data locally instead of relying on the cloud. However, deep learning techniques like computer vision and natural language processing can be computationally expensive and memory-intensive. Creating manual architectures specialized for each device is infeasible due to their varying memory and computational constraints. To address these concerns, we automate the construction of task-specific deep learning architectures optimized for device constraints through Neural Architecture Search (NAS). We present DCA-NAS, a principled method of fast neural network architecture search that incorporates edge-device constraints such as model size and floating-point operations. It incorporates weight sharing and channel bottleneck techniques to speed up the search time. Based on our experiments, we see that DCA-NAS outperforms manual architectures for similar sized models and is comparable to popular mobile architectures on various image classification datasets like CIFAR-10, CIFAR-100, and Imagenet-1k. Experiments with search spaces -- DARTS and NAS-Bench-201 show the generalization capabilities of DCA-NAS. On further evaluating our approach on Hardware-NAS-Bench, device-specific architectures with low inference latency and state-of-the-art performance were discovered.Comment: Accepted to 10th International Conference on Pattern Recognition and Machine Intelligence (PReMI) 202

    Thermophoresis of electrolyte solutions and protein-ligand systems

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    Thermophoresis or thermodiffusion is the mass transport driven by a temperature gradient. This thesis focuses on the thermophoretic motion of ionic compounds in a biological context and is motivated by a practical application, in which thermodiffusion is used to monitor protein-ligand reactions. Proteins are complex molecules containing non-ionic and ionic groups. While recent studies of non-ionic compounds found a strong correlation between thermodiffusion and hydration, it is unclear how this correlation changes when molecules are charged. To separate ionic from non-ionic contributions, it is reasonable to look first into the thermophoretic motion of simple salts without large organic side groups and to study in the next step complex protein-ligand systems, which typically contain hydrophobic and hydrophilic groups. The systematic studies of aqueous solutions of simple salts should reveal differences between ionic and non-ionic systems and should give further information about ion and ion specific effects. Due to the high complexity of protein-ligand systems, complementary methods should be used to gain a better understanding of the interactions between different components that are present in the system. This will help to understand how the thermophoretic behavior of the free protein differs from that of the protein-ligand complex formed. Study of the thermophoretic behavior of ionic systems indicates that several correlations, which were found for aqueous solutions of non-ionic solutes are no longer valid for ionic solutes. For non-ionic solutes hydrogen bonds primarily influence the thermophoretic behavior. In case of ionic solutes, although both electrostatic interactions and hydrogen bonds are present, it is found that thermophoretic behavior is influenced by electrostatic interactions. Focusing on the specific ion effects for ionic systems in the context of the Hofmeister series, a change of the anion is found to influence the thermophoretic behavior more than a change of the cation. Further, a correlation between thermophoretic behavior and hydrophilicity of the ionic solutes is found, which underlines the sensitivity of thermodiffusion to changes in hydration. Based on this sensitivity, a preliminary model is developed for describing the non-monotonous variation of Soret coefficient ST with concentration for aqueous solutions of alkali iodide salts. To study the thermodiffusion of binding reactions, we also use complementary methods such as Isothermal Titration Calorimetry (ITC) and a thermophoretic microfluidic cell. As systems, we have chosen EDTA-CaCl2 and protein-ligand systems (binding of Bovine Carbonic Anhydrase I (BCA I) with two aryl sulfonamide ligands). To gain deeper insight into the complex formation reactions thermophoretic data (non-equilibrium process) are compared with thermodynamic data (equilibrium process) to establish a mathematical relation between ST and Gibb’s free energy ΔG. For EDTA-CaCl2 and protein-ligand systems, the derived relation holds valid, which enables calculation of ΔG at a particular temperature from ST

    Development of Smart Polymeric Nanocontainers for the Therapy of Head and Brain Malignancies

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    Head and brain tumours account for 2% to 4% of all cancers globally. Despite progress made in diagnosis and adjuvant therapies, they remain a global burden with unsatisfactory survival rates, reduced treatment outcome, poor prognosis and high risks of recurrence. First line chemotherapy drugs used in current regimens lack specificity, which ensues long term and unpleasant side effects for the patient. Encapsulating the chemotherapy drugs within nanocontainers (NCs) is one approach to improving their efficacy and therapeutic outcome as well as reducing side effects. Due to their nano-size and suitable modified surface, polymeric NCs can reach critical areas in the head and brain without causing any damage to the healthy tissue. The aim was to synthesize polymeric NCs capable of carrying and delivering chemotherapy drugs in tumour cells to increase their efficacy and reduce their side effects. Hollow P(MAA-co-MBA-co-NIPAM-co-EGDMA) NCs with dual sensitivity were synthesised and characterized structurally and morphologically throughout the synthesis steps. Daunorubicin, cisplatin, and temozolomide loaded NCs, and free NCs were assessed by haemolysis assay, MTT assay, fluorescence microscopy, western blot, and flow cytometry in rhabdomyosarcoma TE671 cell line and glioblastoma U87 MG cell line. The free NCs showed high biocompatibility and non-toxicity trait with good cellular uptake. Also, the loading capacities were between 27% and 63%, and the release studies showed a sustained release profile for up to 72h. Treatment of rhabdomyosarcoma and glioblastoma cells with different drugs loaded in NCs showed high cancer cell cytotoxicity, variation in induced DNA damage levels, induced apoptosis and cell cycles arrest for 24h and 72h. Overall, smart polymeric NCs showed reliability in carrying and delivering chemotherapy drugs in rhabdomyosarcoma and glioblastoma cells with efficiency in tackling the tumour cells. Our polymeric NCs exhibited excellent potential as a novel therapeutic approach for targeted drug delivery in head and brain tumours. This could be verified in the preclinical models to assess their improved efficacy and reducing side effects of first line cancer therapies. It could further pave the way for clinical trials of head and brain cancers in human patients

    Radiometric techniques for the detection and assessment of tritium in aqueous media - a review

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    Tritium (3H) is one of the hardest isotopes to detect by most traditional radiometric means due to the low energy of the β− emission, (β−MEAN 5.67 keV, β−MAX 18.59 keV). The high mobility of the isotope in groundwater environments and subsequent entry into the food chain constitutes a radiation safety risk justifying assessment. Accordingly, there is a need to measure 3H accurately and efficiently, often in low concentrations, both in laboratory settings and on-line flow-cells for potential in situ measurement requirements. This review covers technologies developed to assess aqueous tritium-containing samples. Of the techniques reviewed, liquid scintillation counting (LSC) is the best performing means of aqueous 3H detection with a minimal detectable activity of 6 × 10−4 Bq L−1 for a 195-min counting time. LSC is also established as the industry standard and is the basis of the first, commercially-available, real-time 3H detection system. This review also covers the other means described in literature for the detection of tritium in aqueous samples, including the use of plastic and inorganic scintillators, imaging plates, both in off-line and on-line modes of operation. Whilst most of these techniques lag LSC in terms of technological maturity, several offer detection sensitivities that could rival LSC, without the need for the sample preparation and waste generation associated with LSC, and providing real-time in situ measurements

    Effect of substrate bed temperature on solute segregation and mechanical properties in Ti–6Al–4V produced by laser powder bed fusion

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    Titanium alloys are particularly sensitive to temperature during additive manufacturing processes, due to their dual phase microstructure and sensitivity to oxygen uptake. In this paper, laser powder bed fusion (LPBF) was used in conjunction with a heated substrate bed at 100 °C, 570 °C and 770 °C to produce specimens of Ti–6Al–4V, to investigate the change in mechanical properties and segregation of alloying elements. An initial increase in ductility was observed when increasing the temperature from 100 °C to 570 °C, followed by a significant loss in ductility when samples were produced at 770 °C. A suite of multi-scale characterisation techniques revealed that the as-printed microstructure was drastically different across the range of temperatures. At 100 °C, α + α′ phases were identified. Deformation twinning was extensively observed in the a phase, with Al and V segregating at the twin interfaces. At 570 °C (the most ductile sample), α′, α and nano-particles of β were observed, with networks of entangled dislocations showing V segregation. At 770 °C, no martensitic α′ was identified. The microstructure was an α + β microstructure and an increased volume fraction of tangled dislocations with localised V segregation. Thermodynamic modelling based on the Gibbs-free energy of formation showed that the increased V concentration at dislocations was insufficient to locally nucleate β phase. However, b-phase nucleation at grain boundaries (not dislocations) caused pinning of grain boundaries, impeding slip and leading to a reduction in ductility. It is likely that the increased O-content within specimens printed at increased temperatures also played a key role in high-temperature embrittlement. Building operations are therefore best performed below sub-transus temperatures, to encourage the growth of strengthening phases via solute segregation, and the build atmosphere must be tightly controlled to reduce oxygen uptake within the samples

    Advanced Nanomaterials for Electrochemical Energy Conversion and Storage

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    This book focuses on advanced nanomaterials for energy conversion and storage, covering their design, synthesis, properties and applications in various fields. Developing advanced nanomaterials for high-performance and low-cost energy conversion and storage devices and technologies is of great significance in order to solve the issues of energy crisis and environmental pollution. In this book, various advanced nanomaterials for batteries, capacitors, electrocatalysis, nanogenerators, and magnetic nanomaterials are presente

    Advances in Micro- and Nanomechanics

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    This book focuses on recent advances in both theoretical and experimental studies of material behaviour at the micro- and nano-scales. Special attention is given to experimental studies of nanofilms, nanoparticles and nanocomposites as well as tooth defects. Various experimental techniques were used. Magneto- and thermoelastic coupling were considered, as were nonlocal models of thin structures

    Advances in Binders for Construction Materials

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    The global binder production for construction materials is approximately 7.5 billion tons per year, contributing ~6% to the global anthropogenic atmospheric CO2 emissions. Reducing this carbon footprint is a key aim of the construction industry, and current research focuses on developing new innovative ways to attain more sustainable binders and concrete/mortars as a real alternative to the current global demand for Portland cement.With this aim, several potential alternative binders are currently being investigated by scientists worldwide, based on calcium aluminate cement, calcium sulfoaluminate cement, alkali-activated binders, calcined clay limestone cements, nanomaterials, or supersulfated cements. This Special Issue presents contributions that address research and practical advances in i) alternative binder manufacturing processes; ii) chemical, microstructural, and structural characterization of unhydrated binders and of hydrated systems; iii) the properties and modelling of concrete and mortars; iv) applications and durability of concrete and mortars; and v) the conservation and repair of historic concrete/mortar structures using alternative binders.We believe this Special Issue will be of high interest in the binder industry and construction community, based upon the novelty and quality of the results and the real potential application of the findings to the practice and industry

    On learning the structure of clusters in graphs

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    Graph clustering is a fundamental problem in unsupervised learning, with numerous applications in computer science and in analysing real-world data. In many real-world applications, we find that the clusters have a significant high-level structure. This is often overlooked in the design and analysis of graph clustering algorithms which make strong simplifying assumptions about the structure of the graph. This thesis addresses the natural question of whether the structure of clusters can be learned efficiently and describes four new algorithmic results for learning such structure in graphs and hypergraphs. The first part of the thesis studies the classical spectral clustering algorithm, and presents a tighter analysis on its performance. This result explains why it works under a much weaker and more natural condition than the ones studied in the literature, and helps to close the gap between the theoretical guarantees of the spectral clustering algorithm and its excellent empirical performance. The second part of the thesis builds on the theoretical guarantees of the previous part and shows that, when the clusters of the underlying graph have certain structures, spectral clustering with fewer than k eigenvectors is able to produce better output than classical spectral clustering in which k eigenvectors are employed, where k is the number of clusters. This presents the first work that discusses and analyses the performance of spectral clustering with fewer than k eigenvectors, and shows that general structures of clusters can be learned with spectral methods. The third part of the thesis considers efficient learning of the structure of clusters with local algorithms, whose runtime depends only on the size of the target clusters and is independent of the underlying input graph. While the objective of classical local clustering algorithms is to find a cluster which is sparsely connected to the rest of the graph, this part of the thesis presents a local algorithm that finds a pair of clusters which are densely connected to each other. This result demonstrates that certain structures of clusters can be learned efficiently in the local setting, even in the massive graphs which are ubiquitous in real-world applications. The final part of the thesis studies the problem of learning densely connected clusters in hypergraphs. The developed algorithm is based on a new heat diffusion process, whose analysis extends a sequence of recent work on the spectral theory of hypergraphs. It allows the structure of clusters to be learned in datasets modelling higher-order relations of objects and can be applied to efficiently analyse many complex datasets occurring in practice. All of the presented theoretical results are further extensively evaluated on both synthetic and real-word datasets of different domains, including image classification and segmentation, migration networks, co-authorship networks, and natural language processing. These experimental results demonstrate that the newly developed algorithms are practical, effective, and immediately applicable for learning the structure of clusters in real-world data

    Enhancing Embedding Representations of Biomedical Data using Logic Knowledge

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    Knowledge Graph Embeddings (KGE) have become a quite popular class of models specifically devised to deal with ontologies and graph structure data, as they can implicitly encode statistical dependencies between entities and relations in a latent space. KGE techniques are particularly effective for the biomedical domain, where it is quite common to deal with large knowledge graphs underlying complex interactions between biological and chemical objects. Recently in the literature, the PharmKG dataset has been proposed as one of the most challenging knowledge graph biomedical benchmark, with hundreds of thousands of relational facts between genes, diseases and chemicals. Despite KGEs can scale to very large relational domains, they generally fail at representing more complex relational dependencies between facts, like logic rules, which may be fundamental in complex experimental settings. In this paper, we exploit logic rules to enhance the embedding representations of KGEs on the PharmKG dataset. To this end, we adopt Relational Reasoning Network (R2N), a recently proposed neural-symbolic approach showing promising results on knowledge graph completion tasks. An R2N uses the available logic rules to build a neural architecture that reasons over KGE latent representations. In the experiments, we show that our approach is able to significantly improve the current state-of-the-art on the PharmKG dataset. Finally, we provide an ablation study to experimentally compare the effect of alternative sets of rules according to different selection criteria and varying the number of considered rules
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