Miracle-dz/UniFAR-FTUs huggingface dataset:
Microsoft Word-6

In this paper static and dynamic responses of a fixed-fixed microbeam to electrostatic force and mechanical shock for different cases have been studied. The governing equations whose solution holds the answer to all our questions about the mechanical behavior is the nonlinear elastoelectrostatic equations. Due to the nonlinearity and complexity of the derived equations analytical solution are not generally available; therefore, the obtained differential equations have been solved by using of a step by step linearization method (SSLM) and a Galerkin based reduced order model. The pull-in voltage of the structure and the effect of shock forces on the mechanical behavior of undeflected and electrostatically deflected microbeam have been investigated. The proposed models capture the other design parameters such as intrinsic residual stress from fabrication processes and the nonlinear stiffening or stretching stress due to beam deflection. © 2009 IAU, Arak Branch. All rights reserved.
Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation

In this paper, we study the problems of principle Generalized Eigenvector computation and Canonical Correlation Analysis in the stochastic setting. We propose a simple and efficient algorithm for these problems. We prove the global convergence of our algorithm, borrowing ideas from the theory of fast-mixing Markov chains and two-Time-Scale Stochastic Approximation, showing that it achieves the optimal rate of convergence. In the process, we develop tools for understanding stochastic processes with Markovian noise which might be of independent interest.
A Survey on Collaborative Learning Approach for Speech and Speaker Recognition

The Multi-task recurrent neural net model delivers improved performance on both automatic speech and speaker recognition. Neural networks are prognosticating methods that are based on simple mathematical models of the brain. They allow complex nonlinear relationships between the response variable and its predictors. A deep learning approach has been used to derive speaker identities (d-vector) by a Deep Neural Network (DNN). A DNN is an Artificial Neural Network (ANN) with multiple hidden layers between the input and output layers. In the DNN, the hidden layers can be considered as increasingly complex feature transformations. The final softmax layer is a log-linear classifier which makes use of the abstract features computed in the hidden layers. Long Short-Term Memory (LSTM) is specific recurrent neural network (RNN) architecture. This paper analyzes the various approaches for the training of speech and speaker recognisation by identifying the factors like target delay, partially marked data, and negatively-correlated tasks.
Processing transactions in a predefined order

In this paper we provide a high performance solution to the problem of committing transactions while enforcing a pre-defined order. We provide the design and implementation of three algorithms, which deploy a specialized cooperative transaction execution model. This model permits the propagation of written values along the chain of ordered transactions. We show that, even in the presence of data conflicts, the proposed algorithms outperform single threaded execution, and other baseline and specialized state-of-the-art competitors (e.g., STMLite). The maximum speedup achieved in micro benchmarks, STAMP, PARSEC and SPEC200 applications is in the range of 4.3x -- 16.5x.
Bias in the use of broadscale vegetation data in the analysis of habitat selection

Abstract Effective conservation efforts often depend on accurate identification of habitat requirements. Studies that identify habitat requirements for animals typically investigate use of structural habitat (vegetation) instead of functional habitat (conditions for biological fitness). The spatial scale of data could bias the match between functional and structural habitat because broadscale structural habitat maps exclude small habitat patches (inclusions) and broadscale location error can bias estimates of habitat use. To investigate how spatial scale affects the match between functional and structural habitat, we backtracked American marten (Martes americana) and fisher (Pekania [formerly Martes] pennanti) movement paths during winter and compared results from selection and tortuosity analyses conducted with broadscale (4 ha) and fine-scale (0.02 ha) structural habitat data. Functional habitat (rest sites and prey kill sites) occurred disproportionately in hemlock–cedar. Fine-scale structural habitat data detected greater selection and tortuosity within hemlock–cedar by traveling martens, but broadscale structural habitat data did not, which demonstrates that combining fine-scale location data with fine-scale structural habitat data improves the match between functional and structural habitat and understanding of habitat requirements. Selection and tortuosity indexes were poorly correlated, indicating that factors other than structural habitat influenced movement patterns. Within-stand structural habitat heterogeneity is important to martens and fishers, especially when heterogeneity includes mature conifer inclusions within primarily deciduous forests. Broadscale data may identify structural habitat associated with required types, rather than required habitat itself, when functional habitat corresponds with landscape features such as inclusions.
Interpreting Non-coding Genetic Variation in Multiple Sclerosis Genome-Wide Associated Regions

Multiple sclerosis (MS) is the most common neurological disorder in young adults. Despite extensive studies, only a fraction of MS heritability has been explained, with association studies focusing primarily on protein-coding genes, essentially for the difficulty of interpreting non-coding features. However, non-coding RNAs (ncRNAs) and functional elements, such as super-enhancers (SE), are crucial regulators of many pathways and cellular mechanisms, and they have been implicated in a growing number of diseases. In this work, we searched for possible enrichments in non-coding elements at MS genome-wide associated loci, with the aim to highlight their possible involvement in the susceptibility to the disease. We first reconstructed the linkage disequilibrium (LD) structure of the Italian population using data of 727,478 single-nucleotide polymorphisms (SNPs) from 1,668 healthy individuals. The genomic coordinates of the obtained LD blocks were intersected with those of the top hits identified in previously published MS genome-wide association studies (GWAS). By a bootstrapping approach, we hence demonstrated a striking enrichment of non-coding elements, especially of circular RNAs (circRNAs) mapping in the 73 LD blocks harboring MS-associated SNPs. In particular, we found a total of 482 circRNAs (annotated in publicly available databases) vs. a mean of 194 ± 65 in the random sets of LD blocks, using 1,000 iterations. As a proof of concept of a possible functional relevance of this observation, we experimentally verified that the expression levels of a circRNA derived from an MS-associated locus, i.e., hsa_circ_0043813 from the STAT3 gene, can be modulated by the three genotypes at the disease-associated SNP. Finally, by evaluating RNA-seq data of two cell lines, SH-SY5Y and Jurkat cells, representing tissues relevant for MS, we identified 18 (two novel) circRNAs derived from MS-associated genes. In conclusion, this work showed for the first time that MS-GWAS top hits map in LD blocks enriched in circRNAs, suggesting circRNAs as possible novel contributors to the disease pathogenesis.
$FREE$ —Fine-Grained Scheduling for Reliable and Energy-Efficient Data Collection in LoRaWAN

LoRaWAN promises to provide wide-area network access to low-cost devices that can operate for up to ten years on a single 1000-mAh battery. This makes LoRaWAN particularly suited for the data collection applications (e.g., monitoring applications), where device lifetime is a key performance metric. However, when supporting a large number of devices, LoRaWAN suffers from a scalability issue due to the high collision probability of its Aloha-based MAC layer. The performance worsens further when using acknowledged transmissions due to the duty-cycle restriction at the gateway. For this, we propose FREE, a fine-grained scheduling scheme for reliable and energy-efficient data collection in LoRaWAN. FREE takes advantage of applications that do not have hard delay requirements on data delivery by supporting the synchronized bulk data transmission. This means data are buffered for transmission in scheduled time slots instead of transmitted straight away. FREE allocates spreading factors, transmission powers, frequency channels, time slots, and schedules slots in frames for LoRaWAN end-devices. As a result, FREE overcomes the scalability problem of LoRaWAN by eliminating collisions and grouping acknowledgments. We evaluate the performance of FREE versus different legacy LoRaWAN configurations. The numerical results show that FREE scales well and achieves almost 100% data delivery and the device lifetime is estimated over ten years independent of traffic type and network size. In comparison to poor scalability, low data delivery and device lifetime of fewer than two years for acknowledged data traffic in the standard LoRaWAN configurations.
DATELINE: Deep Plackett-Luce Model with Uncertainty Measurements

The aggregation of k-ary preferences is a historical and important problem, since it has many real-world applications, such as peer grading, presidential elections and restaurant ranking. Meanwhile, variants of Plackett-Luce model has been applied to aggregate k-ary preferences. However, there are two urgent issues still existing in the current variants. First, most of them ignore feature information. Namely, they consider k-ary preferences instead of instance-dependent k-ary preferences. Second, these variants barely consider the uncertainty in k-ary preferences provided by agnostic crowds. In this paper, we propose Deep plAckeTt-luce modEL wIth uNcertainty mEasurements (DATELINE), which can address both issues simultaneously. To address the first issue, we employ deep neural networks mapping each instance into its ranking score in Plackett-Luce model. Then, we present a weighted Plackett-Luce model to solve the second issue, where the weight is a dynamic uncertainty vector measuring the worker quality. More importantly, we provide theoretical guarantees for DATELINE to justify its robustness.
Speech and Speaker Recognition from Raw Waveform with SincNet

Deep neural networks can learn complex and abstract representations, that are progressively obtained by combining simpler ones. A recent trend in speech and speaker recognition consists in discovering these representations starting from raw audio samples directly. Differently from standard hand-crafted features such as MFCCs or FBANK, the raw waveform can potentially help neural networks discover better and more customized representations. The high-dimensional raw inputs, however, can make training significantly more challenging. This paper summarizes our recent efforts to develop a neural architecture that efficiently processes speech from audio waveforms. In particular, we propose SincNet, a novel Convolutional Neural Network (CNN) that encourages the first layer to discover meaningful filters by exploiting parametrized sinc functions. In contrast to standard CNNs, which learn all the elements of each filter, only low and high cutoff frequencies of band-pass filters are directly learned from data. This inductive bias offers a very compact way to derive a customized front-end, that only depends on some parameters with a clear physical meaning. Our experiments, conducted on both speaker and speech recognition, show that the proposed architecture converges faster, performs better, and is more computationally efficient than standard CNNs.
Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning

Multiple-step lookahead policies have demonstrated high empirical competence in Reinforcement Learning, via the use of Monte Carlo Tree Search or Model Predictive Control. In a recent work (Efroni et al., 2018), multiple-step greedy policies and their use in vanilla Policy Iteration algorithms were proposed and analyzed. In this work, we study multiple-step greedy algorithms in more practical setups. We begin by highlighting a counter-intuitive difficulty, arising with soft-policy updates: even in the absence of approximations, and contrary to the 1-step-greedy case, monotonic policy improvement is not guaranteed unless the update stepsize is sufficiently large. Taking particular care about this difficulty, we formulate and analyze online and approximate algorithms that use such a multi-step greedy operator.
Sereum: Protecting Existing Smart Contracts Against Re-Entrancy Attacks

Recently, a number of existing blockchain systems have witnessed major bugs and vulnerabilities within smart contracts. Although the literature features a number of proposals for securing smart contracts, these proposals mostly focus on proving the correctness or absence of a certain type of vulnerability within a contract, but cannot protect deployed (legacy) contracts from being exploited. In this paper, we address this problem in the context of re-entrancy exploits and propose a novel smart contract security technology, dubbed Sereum (Secure Ethereum), which protects existing, deployed contracts against re-entrancy attacks in a backwards compatible way based on run-time monitoring and validation. Sereum does neither require any modification nor any semantic knowledge of existing contracts. By means of implementation and evaluation using the Ethereum blockchain, we show that Sereum covers the actual execution flow of a smart contract to accurately detect and prevent attacks with a false positive rate as small as 0.06% and with negligible run-time overhead. As a by-product, we develop three advanced re-entrancy attacks to demonstrate the limitations of existing offline vulnerability analysis tools.
Towards Automating Precision Studies of Clone Detectors

Current research in clone detection suffers from poor ecosystems for evaluating precision of clone detection tools. Corpora of labeled clones are scarce and incomplete, making evaluation labor intensive and idiosyncratic, and limiting intertool comparison. Precision-assessment tools are simply lacking. We present a semiautomated approach to facilitate precision studies of clone detection tools. The approach merges automatic mechanisms of clone classification with manual validation of clone pairs. We demonstrate that the proposed automatic approach has a very high precision and it significantly reduces the number of clone pairs that need human validation during precision experiments. Moreover, we aggregate the individual effort of multiple teams into a single evolving dataset of labeled clone pairs, creating an important asset for software clone research.
Fast Neural Architecture Construction using EnvelopeNets

In recent years, advances in the design of convolutional neural networks have resulted in significant improvements on the image classification and object detection problems. One of the advances is networks built by stacking complex cells, seen in such networks as InceptionNet and NasNet. These cells are either constructed by hand, generated by generative networks or discovered by search. Unlike conventional networks (where layers consist of a convolution block, sampling and non linear unit), the new cells feature more complex designs consisting of several filters and other operators connected in series and parallel. Recently, several cells have been proposed or generated that are supersets of previously proposed custom or generated cells. Influenced by this, we introduce a network construction method based on EnvelopeNets. An EnvelopeNet is a deep convolutional neural network of stacked EnvelopeCells. EnvelopeCells are supersets (or envelopes) of previously proposed handcrafted and generated cells. We propose a method to construct improved network architectures by restructuring EnvelopeNets. The algorithm restructures an EnvelopeNet by rearranging blocks in the network. It identifies blocks to be restructured using metrics derived from the featuremaps collected during a partial training run of the EnvelopeNet. The method requires less computation resources to generate an architecture than an optimized architecture search over the entire search space of blocks. The restructured networks have higher accuracy on the image classification problem on a representative dataset than both the generating EnvelopeNet and an equivalent arbitrary network.
Age-Dependent Performance on Pro-point and Anti-point Tasks

Changes in prefrontal cortex are thought to be responsible for many of the characteristic behavioral changes that are seen during adolescence and late adulthood. Disruption of prefrontal cortex is an early sign for many developmental, neurological, and psychiatric disorders. Goal directed eye movements, such as Anti-saccades, have been shown to have high sensitivity as a gross assessment of prefrontal lobe function. Previous studies on the developmental changes of saccades across age have shown that stimulus-driven and goal-directed eye movements follow a U-shaped trend with peaks in performance occuring during adolescence. Using novel tablet-based pointing tasks, modeled on eye movement tests, this study aims to provide a preliminary understanding of how age affects manual pointing performance, in order to more easily track behavioral changes of the prefrontal cortex. In this study, 82 participants between the ages of 10 and 63 were recruited to participate. Results show that similarly to saccades, manual pointing responses are age dependent with fastest response times found during late adolescence to early adulthood (U-shaped curves). Importantly, we also demonstrated significant differences in the effect of age in stimulus-driven (Pro-point) and goal-directed (Anti-point) pointing tasks. The effect of age on response time (RT) is greater on Anti-point compared to Pro-point task (with a 79 ms greater mean decrease during early development and a 148 ms greater mean increase during later aging). Further, for Pro-point task, the U-shaped curve flattens at about 45 years whereas for Anti-point task the U-shaped curve continues up to the maximum age tested (about 60 years). This dissociation between age-related changes in sensorimotor and cognitive performance suggests independent development of associated brain circuity. Thus, changes of performance in disease that are specific for age and task may be able to help identify brain circuitry involved. Finally, given that these tablet-based pointing tasks show similar age-related patterns reported previously with eye-tracking technology, our findings suggest that such tablet-based tasks may provide an inexpensive, quick, and more practical way of detecting neurological deficits or tracking cognitive changes.
3D nanofabrication by volumetric deposition and controlled shrinkage of patterned scaffolds

Shrinking problems in 3D printing Although a range of materials can now be fabricated using additive manufacturing techniques, these usually involve assembly of a series of stacked layers, which restricts three-dimensional (3D) geometry. Oran et al. developed a method to print a range of materials, including metals and semiconductors, inside a gel scaffold (see the Perspective by Long and Williams). When the hydrogels were dehydrated, they shrunk 10-fold, which pushed the feature sizes down to the nanoscale. Science, this issue p. 1281; see also p. 1244 Direct writing of nanoscale 3D structures of metals and semiconductors operates with no geometry limitations. Lithographic nanofabrication is often limited to successive fabrication of two-dimensional (2D) layers. We present a strategy for the direct assembly of 3D nanomaterials consisting of metals, semiconductors, and biomolecules arranged in virtually any 3D geometry. We used hydrogels as scaffolds for volumetric deposition of materials at defined points in space. We then optically patterned these scaffolds in three dimensions, attached one or more functional materials, and then shrank and dehydrated them in a controlled way to achieve nanoscale feature sizes in a solid substrate. We demonstrate that our process, Implosion Fabrication (ImpFab), can directly write highly conductive, 3D silver nanostructures within an acrylic scaffold via volumetric silver deposition. Using ImpFab, we achieve resolutions in the tens of nanometers and complex, non–self-supporting 3D geometries of interest for optical metamaterials.
A Chatbot Framework for the Children's Legal Centre

This paper presents a novel method to address legal rights for children through a chatbot framework by integrating machine learning, a dialogue graph, and information extraction. The method addresses a significant problem: we cannot presume that children have common knowledge about their rights or express themselves as an adult might. In our framework, a chatbot user begins a conversation, where based on the circumstance described, a neural network predicts both speech acts, relating to a dialogue graph, and legal types. Based on the legal types, relevant legal rights are returned to the user. Information is extracted throughout the conversation in order to create a case for a legal advisor. In collaboration with the Children's Legal Centre Wales, who advocate for the improvement and dissemination of legal rights in Wales, a corpus has been constructed and a prototype chatbot developed. The framework has been evaluated with classification measures and a user study. The framework can be extended and adapted to other domains.
Reducing acute kidney injury incidence and progression in a large teaching hospital

Acute kidney injury (AKI) is a common syndrome that is associated with significant mortality and cost. The Quality Improvement AKI Collaborative at Salford Royal Foundation Trust was established to review and improve both the recognition and management of AKI. This was a whole-system intervention to tackle AKI implemented as an alternative to employing separate AKI nurses. Our aims were to reduce the overall incidence of AKI by 10%, to reduce hospital-acquired AKI by 25% and to reduce the progression of AKI from stage 1 to stage 2 or 3 by 50%. From 2014 to 2016, several multifaceted changes were introduced. These included system changes, such as inserting an e-alert for AKI into the electronic patient record, an online educational package and face-to-face teaching for AKI, and AKI addition to daily safety huddles. On 10 Collaborative wards, development of an AKI care bundle via multidisciplinary team (MDT) plan, do, study, act testing occurred. Results showed a 15.6% reduction in hospital-wide-acquired AKI, with a 22.3% reduction on the collaborative wards. Trust-wide rates of progression of AKI 1 to AKI 2 or 3 showed normal variation, whereas there was a 48.5% reduction in AKI progression on the Collaborative wards. This implies that e-alerts were ineffective in isolation. The Collaborative wards’ results were a product of the educational support, bundle and heightened awareness of AKI. A number of acute hospitals have demonstrated impactful successes in AKI reduction centred on a dedicated AKI nurse model plus e-alerting with supporting changes. This project adds value by highlighting another approach that does not require a new post with attendant rolling costs and risks. We believe that our approach increased our efficacy in acute care in our front-line teams by concentrating on embedding improved recognition and actions across the MDT.