Miracle-dz/UniFAR-FTUs huggingface dataset:
Engineering the Dissipation of Crystalline Micromechanical Resonators

High quality micro- and nano-mechanical resonators are widely used in sensing, communications and timing, and have future applications in quantum technologies and fundamental studies of quantum physics. Crystalline thin-films are particularly attractive for such resonators due to their prospects for high quality, intrinsic stress and yield strength, and low dissipation. However, when grown on a silicon substrate, interfacial defects arising from lattice mismatch with the substrate have been postulated to introduce additional dissipation. Here, we develop a new backside etching process for single crystal silicon carbide microresonators that allows us to quantitatively verify this prediction. By engineering the geometry of the resonators and removing the defective interfacial layer, we achieve quality factors exceeding a million in silicon carbide trampoline resonators at room temperature, a factor of five higher than without the removal of the interfacial defect layer. We predict that similar devices fabricated from ultrahigh purity silicon carbide and leveraging its high yield strength, could enable room temperature quality factors as high as $6\times10^9$
CommonGen: A Constrained Text Generation Dataset Towards Generative Commonsense Reasoning

Rational humans can generate sentences that cover a certain set of concepts while describing natural and common scenes. For example, given {apple(noun), tree(noun), pick(verb)}, humans can easily come up with scenes like "a boy is picking an apple from a tree" via their generative commonsense reasoning ability. However, we find this capacity has not been well learned by machines. Most prior works in machine commonsense focus on discriminative reasoning tasks with a multi-choice question answering setting. Herein, we present CommonGen: a challenging dataset for testing generative commonsense reasoning with a constrained text generation task. We collect 37k concept-sets as inputs and 90k human-written sentences as associated outputs. Additionally, we also provide high-quality rationales behind the reasoning process for the development and test sets from the human annotators. We demonstrate the difficulty of the task by examining a wide range of sequence generation methods with both automatic metrics and human evaluation. The state-of-the-art pre-trained generation model, UniLM, is still far from human performance in this task. Our data and code is publicly available at this http URL .
Crowd-Powered Mechanisms for Viewing and Imagining Public Spaces

Crowdsourcing allows one to collect information from a large number of citizens relatively easily, thereby creating an opportunity to provide a novel technological means to view and imagine the city, complementing conventional geovisualization and urban simulation games. We postulate that the inflexibility of existing crowdsourcing frameworks can impose inherent limitations in terms of the imaginability of the city, and propose citizensourcing, which augments crowdsourcing with the mechanisms for supporting exploration and active participation in order to address the limitations. We then describe our experiences with the smartphone-based prototypes of the mechanisms, and discuss their implications for the design of citizensourcing environments.
DialogAct2Vec: Towards End-to-End Dialogue Agent by Multi-Task Representation Learning

In end-to-end dialogue modeling and agent learning, it is important to (1) effectively learn knowledge from data, and (2) fully utilize heterogeneous information, e.g., dialogue act flow and utterances. However, the majority of existing methods cannot simultaneously satisfy the two conditions. For example, rule definition and data labeling during system design take too much manual work, and sequence-to-sequence methods only model one-side utterance information. In this paper, we propose a novel joint end-to-end model by multi-task representation learning, which can capture the knowledge from heterogeneous information through automatically learning knowledgeable low-dimensional embeddings from data, named with DialogAct2Vec. The model requires little manual work for intervention in system design and we find that the multi-task learning can greatly improve the effectiveness of representation learning. Extensive experiments on a public dataset for restaurant reservation show that the proposed method leads to significant improvements against the state-of-the-art baselines on both the act prediction task and utterance prediction task.
OFDM With Hybrid Number and Index Modulation

A novel transmission scheme is introduced for efficient data transmission by conveying additional information bits through jointly changing the index and number of active subcarriers within each orthogonal frequency division multiplexing (OFDM) subblock. The proposed scheme is different from the conventional OFDM-subcarrier number modulation (OFDM-SNM) and OFDM-index modulation (OFDM-IM), in which data bits are transmitted using either number or index of active subcarriers. The proposed modulation technique offers superior spectral and energy efficiency compared to its counterparts OFDM-SNM and OFDM-IM, especially at low modulation orders such as binary phase shift keying (BPSK) that can provide high reliability and low complexity, thus making it very suitable for meeting the requirements of Internet of things (IoT) applications. Bit error rate (BER) performance analysis is provided for the proposed scheme, and Monte Carlo simulations are presented to prove the consistency of the simulated BER with the analyzed one. More importantly, it is demonstrated that the proposed scheme can offer much superior BER performance compared to that of OFDM-IM and classical OFDM under equivalent power and spectral efficiency values.
Dynamic Knowledge Graph Construction for Zero-shot Commonsense Question Answering

Understanding narratives requires dynamically reasoning about the implicit causes, effects, and states of the situations described in text, which in turn requires understanding rich background knowledge about how the social and physical world works. At the core of this challenge is how to access contextually relevant knowledge on demand and reason over it. In this paper, we present initial studies toward zero-shot commonsense QA by formulating the task as probabilistic inference over dynamically generated commonsense knowledge graphs. In contrast to previous studies for knowledge integration that rely on retrieval of existing knowledge from static knowledge graphs, our study requires commonsense knowledge integration where contextually relevant knowledge is often not present in existing knowledge bases. Therefore, we present a novel approach that generates contextually relevant knowledge on demand using generative neural commonsense knowledge models. Empirical results on the SocialIQa and StoryCommonsense datasets in a zero-shot setting demonstrate that using commonsense knowledge models to dynamically construct and reason over knowledge graphs achieves performance boosts over pre-trained language models and using knowledge models to directly evaluate answers.
Personalized Intervention Program For Children With Special Educational Needs (Adhd)

In this paper, we aim to bring into question a subject that has been addressed over the years by many professionals from the field of educational sciences, which consists in adapting the methods of teaching learning to the educational needs of the student, in particular, those with attention-deficit and hyperactivity disorder. The Custom Intervention Program lies at the core of the inclusive education of the student with ADHD and promotes the idea of a personalized and differentiated teaching method. In the past years, there have been adopted more personalized intervention programs based on behavior change regarding children with ADHD in the school environment, without significant results. Nevertheless, we pored over one that is being used in health psychology. In order to verify the utility of the program, we examined some relevant studies. The originality of our study lies in our own adaptation of the behavior change program based on the INTEGRATED THEORY OF HEALTH BEHAVIOR CHANGE (ITHBC) to the school environment. The methods that we used are the following: observation, interview, investigation of school documents and case study. We concluded that the problem of children with ADHD is linked to the adaptive behavior to the school environment and, therefore, we consider it necessary to use the above mentioned program. Creating a healthy educational environment implies adopting a functional behavior by the students with special educational needs, more precisely, by those with ADHD. © 2017 Published by Future Academy www.FutureAcademy. org.UK
Deconstructing HARI on The Dynamical Analysis of Clinical Trial Outcomes

Kaplan-Meier survival analysis [1] and Cox proportional hazards regression [2] are commonly used to assess outcomes in randomized clinical trials. These methods typically employ a simple two-state non-deterministic model to represent the transition from an initial baseline state to a terminal outcome state (often a composite of multiple endpoints). Many pathophysiological processes, however, involve a deterministic sequence of some number of intermediate transitions: from the baseline state to unstable angina (UA) to myocardial infarction (MI), for example. In such cases, the simple two-state model may be insufficient to capture important dynamics of the actual underlying clinical process.
Calculating the Contribution Rate of Intelligent Transportation System in Improving Urban Traffic Smooth Based on Advanced DID Model

Recent years have witnessed the rapid development of intelligent transportation system around the world, which helps to relieve urban traffic congestion problems. For instance, many mega-cities in China have devoted a large amount of money and resources to the development of intelligent transportation system. This poses an intriguing and important issue: how to measure and quantify the contribution of intelligent transportation system to the urban city, which is still a puzzle. This paper proposes a matching difference-in-difference model to calculate the contribution rate of intelligent transportation system on traffic smoothness. Within the model, the main effect indicators of traffic smoothness are first identified, and then the evaluation index system is built, and finally the ideas of the matching pool are introduced. The proposed model is illustrated in Guangzhou, China (capital city of Guangdong province). The results show that introduction of ITS contributes 9.25% to the improvement of traffic smooth in Guangzhou. Also, the research explains the working mechanism of how ITS improves urban traffic smooth. Eventually, some strategy recommendations are put forward to improve urban traffic smooth.
MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams

Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant time and memory? Existing approaches aim to detect individually surprising edges. In this work, we propose Midas, which focuses on detecting microcluster anomalies, or suddenly arriving groups of suspiciously similar edges, such as lockstep behavior, including denial of service attacks in network traffic data. Midas has the following properties: (a) it detects microcluster anomalies while providing theoretical guarantees about its false positive probability; (b) it is online, thus processing each edge in constant time and constant memory, and also processes the data 108–505 times faster than state-of-the-art approaches; (c) it provides 46%-52% higher accuracy (in terms of AUC) than state-of-the-art approaches.
Learning dimensionality-reduced classifiers for information fusion Citation

The fusion of multimodal sensor information often requires learning decision rules from samples of high-dimensional data. Each data dimension may only be weakly informative for the detection problem of interest. Also, it is not known a priori which components combine to form a lower-dimensional feature space that is most informative. To learn both the combination of dimensions and the decision rule specified in the reduced-dimensional space together, we jointly optimize the linear dimensionality reduction and margin-based supervised classification problems, representing dimensionality reduction by matrices on the Stiefel manifold. We describe how the learning procedure and resulting decision rule can be implemented in parallel, serial, and tree-structured fusion networks.
14 Group Tracking Algorithm for Crowded Scene

Automatic visual monitoring systems gain more significance as the enhancement of high speed processors that are able to execute the prolonged algorithm efficiently. Video surveillance monitoring systems are typically applied to study human behavior in a crowded situation. It is used to observe posture, movement, trajectories and the interaction between people. For this purpose CCD cameras are installed at many crowded places. Most researchers’ work however is focused on the people classification and the recognition of activities. Human identification is important because of a need of surveillance in the crowded scenes. Group motion tracking is applied to monitor the Groups of People (GOP) as single entities in the global flow rather than monitoring each person individually. It has been studied on the basis of dynamic movement and the surrounding of crowd density which will affect the speed of an individual/group. Large crowd flow perhaps causes deadly accidents if large numbers of GOP smash into each other in narrow passageways, streets or bridges. The system must identify multiple GOP within their global flow according to their Spatio-temporal characteristics. Figure 1 illustrates the density of large crowd flow. The Religious and Sport activities are the application examples. According to Crowd Dynamics Ltd, (Crowd Dynamics Ltd) in the year of 2006, 363 pilgrims lost their lives while performing the Hajj. There are a number of readily available wide area surveillance systems but most of the systems are not suitable for high density crowd monitoring. For safety surveillance of large crowd, there should be a system which can detect and track unstable GOP by careful monitoring of crowd and display results in realtime scenario. And, with the results being displayed in real-time, it will certainly assist the system’s operator to make better decision.
Direct conversion of the flux of kinetic energy into radiation in gamma-ray burst

The time evolution of a Gamma-ray burst (GRB) is associated with the evolution of a supernova remnant (SNR). The time evolution of the flux of a GRB is modeled introducing a law for the density of the medium in the advancing layer. The adopted radiative model for the GRB in the various e.m. frequencies is synchrotron emission. The X-ray ring which appears a few hours after a GRB is simulated.
Unsupervised Annotation of Phenotypic Abnormalities via Semantic Latent Representations on Electronic Health Records

The extraction of phenotype information which is naturally contained in electronic health records (EHRs) has been found to be useful in various clinical informatics applications such as disease diagnosis. However, due to imprecise descriptions, lack of gold standards and the demand for efficiency, annotating phenotypic abnormalities on millions of EHR narratives is still challenging. In this work, we propose a novel unsupervised deep learning framework to annotate the phenotypic abnormalities from EHRs via semantic latent representations. The proposed framework takes the advantage of Human Phenotype Ontology (HPO), which is a knowledge base of phenotypic abnormalities, to standardize the annotation results. Experiments have been conducted on 52,722 EHRs from MIMIC-III dataset. Quantitative and qualitative analysis have shown the proposed framework achieves state-of-the-art annotation performance and computational efficiency compared with other methods.
ASP-Core-2 Input Language Format

Abstract Standardization of solver input languages has been a main driver for the growth of several areas within knowledge representation and reasoning, fostering the exploitation in actual applications. In this document, we present the ASP-CORE-2 standard input language for Answer Set Programming, which has been adopted in ASP Competition events since 2013.
Privacy-Preserving Multiple Tensor Factorization for Synthesizing Large-Scale Location Traces with Cluster-Specific Features

Abstract With the widespread use of LBSs (Location-based Services), synthesizing location traces plays an increasingly important role in analyzing spatial big data while protecting user privacy. In particular, a synthetic trace that preserves a feature specific to a cluster of users (e.g., those who commute by train, those who go shopping) is important for various geo-data analysis tasks and for providing a synthetic location dataset. Although location synthesizers have been widely studied, existing synthesizers do not provide su˚cient utility, privacy, or scalability, hence are not practical for large-scale location traces. To overcome this issue, we propose a novel location synthesizer called PPMTF (Privacy-Preserving Multiple Tensor Factorization). We model various statistical features of the original traces by a transition-count tensor and a visit-count tensor. We factorize these two tensors simultaneously via multiple tensor factorization, and train factor matrices via posterior sampling. Then we synthesize traces from reconstructed tensors, and perform a plausible deniability test for a synthetic trace. We comprehensively evaluate PPMTF using two datasets. Our experimental results show that PPMTF preserves various statistical features including cluster-specific features, protects user privacy, and synthesizes large-scale location traces in practical time. PPMTF also significantly outperforms the state-of-theart methods in terms of utility and scalability at the same level of privacy.
Redacted for Privacy

Wheat is an important cereal crop in the Willamette Valley of Oregon and the stiff -strawed, high -yielding varieties are the predominant types grown. Observation of the effect of seeding rates on the yield and yield components are useful in determining which of the components contributes most to the yield of a winter wheat variety and in developing optimum seeding rate recommendations. The experiments were conducted in 1963 -1964, 1964 -1965, and 1965 -1966. The purpose of these studies was to determine the effect of various seeding rates on the yield and yield components of two winter wheat varieties and to find the optimum seeding rates for these varieties. A further objective was to determine if reasonable yields could be obtained from low rates of seeding for both varieties. The yield was measured in terms of bushels per acre. The yield components studied were (1) the number of spikes per unit area, (2) number of spikelets per spike, (3) number of kernels per 4 I l
Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-Encoders

Conditional Text Generation has drawn much attention as a topic of Natural Language Generation (NLG) which provides the possibility for humans to control the properties of generated contents. Current conditional generation models cannot handle emerging conditions due to their joint end-to-end learning fashion. When a new condition added, these techniques require full retraining. In this paper, we present a new framework named Pre-train and Plug-in Variational Auto-Encoder (PPVAE) towards flexible conditional text generation. PPVAE decouples the text generation module from the condition representation module to allow “one-to-many” conditional generation. When a fresh condition emerges, only a lightweight network needs to be trained and works as a plug-in for PPVAE, which is efficient and desirable for real-world applications. Extensive experiments demonstrate the superiority of PPVAE against the existing alternatives with better conditionality and diversity but less training effort.
“ Balancing and Leveraging Management , Leadership and Technology and Fostering Innovation in Changing European High Technology Sector ” Reconstruction of High Technology Organisation

METROPOLIA UNIVERSITY OF APPLIED SCIENCES SCHOOL OF BUSINESS ADMINISTRATION Degree Programme: European Management Majors in University of Lincoln: Management Thesis title: “Balancing and Leveraging Management, Leadership and Technology and Fostering Innovation in Changing European High Technology Sector” Author: Antti Liukkonen Supervisor: Prof. Louise Stansfield Year: 2011 Pages: 55 Technology and Innovation are the main instruments in wealth creation in any business area. New technologies are changing the process of management. Technological competence is not enough to succeed. Managerial know-how and leadership skills are the essential components of successful businesses. Technologically-based companies face more uncertainty, due to rapid technological progress and discontinuity, risk-taking and risk management, understanding the context of innovation are very important functions of managers and leaders. European integration and increased competition has changed the nature of industry and opened new opportunities and threats. Managers, tasks and organisations alter. Potential gains for enterprises are easier access to European markets and better supply of labour resources, components, tools and equipment. However, increased competition and need to change the strategy makes it very challenging for those, who fail to adapt their managing practices for new business environment. The study does not cover all the aspects of management and leadership, but those areas that are least understood and are most common sources of problems and impedance of leadership capabilities, company prosperity, growth and sustainability in changing environment. The dissertation is an assessment into sociological and managerial implications coupled with High-Tech enterprise managerial practice. The author's personal observations and feelings of being managed and best practices from work experience are linked to the leadership theory. This work tries to prove that the managerial competence of enterprise leaders correlates with the wealth or economic performance of the organisation, its growth potential and employees' satisfaction.
Hierarchical Graph Network for Multi-hop Question Answering

In this paper, we present Hierarchical Graph Network (HGN) for multi-hop question answering. To aggregate clues from scattered texts across multiple paragraphs, a hierarchical graph is created by constructing nodes from different levels of granularity (questions, paragraphs, sentences, and entities), the representations of which are initialized with RoBERTa-based context encoders. Given this hierarchical graph, the initial node representations are updated through graph propagation, and multi-hop reasoning is performed via traversing through the graph edges for each subsequent sub-task (e.g., paragraph selection, supporting facts extraction, answer prediction). By weaving heterogeneous nodes into an integral unified graph, this characteristic hierarchical differentiation of node granularity enables HGN to support different question answering sub-tasks simultaneously. Experiments on the HotpotQA benchmark demonstrate that the proposed model achieves new state of the art in both the Distractor and Fullwiki settings.