Miracle-dz/UniFAR-FTUs huggingface dataset:
BEHAVIOR OF CONCRETE BEAMS REINFORCED IN SHEAR WITH CARBON FIBER REINFORCED POLYMER

Carbon fiber reinforced polymers (CFRP) were widely used in strengthening reinforced concrete membersin the last few years, these fibers consist mainly of high strength fibers which increase the member capacity in addition to changing the mode of failure of the reinforced concrete beams. Experimental and theoretical investigations were carried to find the behavior of reinforced concrete beams strengthened by CFRP in shear and bending. The experimental work included testing of 12 beams divided into 4 groups; each group contains 3 beams. The following parameters were taken into consideration: - Concrete crushing strength. - CFRP strengthening location (shear strengthening and both shear and flexure strengthening). Reinforced beams were simply supported subjected to two point loads. Each group consists of three beams; the first beam without CFRP, the second one, is strengthened with CFRP in shear and the third is strengthened with CFRP in both flexure and shear. Four groups with different crushing strength of (12, 20, 30 and 39 MPa). The CFRP sheets are attached externally.It was found that in beam with low crushing strength loads transfer to the CFRP at early stages while inthose of high crushing strength, CFRP contribution only starts when full strength of the beam is fulfilled. Afull bond between CFRP sheets and the concrete is assumed in the theoretical analysis. Comparison between the theoretical and the experimental results revealed the validity of the numerical analysis and the developed methods such that there was a difference of 13% in the ultimate strength for the tested and analyzed beams.
Inter-professional assessment of Posterior Tibial Tendon Dysfunction and its timely diagnosis

Posterior tibial tendon dysfunction (PTTD) is a progressive deformity which can result in the development of a pathological flat foot deformity. A mixed methods approach has been adopted for this study. This blended philosophical stance has embodied both inductive and deductive paradigms to provide a robust exploration, not possible when adopting just one approach. The results presented within this thesis clearly demonstrates that there is inconsistency in the way in which clinicians practise when it comes to the assessment and diagnosis of PTTD. The tests that clinicians select for inclusion in the assessment of PTTD is problematic and differ significantly both within and across different professional groups. The results provide a unique insight into the approaches to assessment and diagnosis among podiatry and physiotherapy advanced musculoskeletal practitioners. Following quantitative data acquisition and investigation of some of the most popularised clinical tests referred to by clinicians and highlighted as important during the content and thematic analysis sections, a novel contribution to this research foci is provided. Findings clearly illustrate a lack of standardisation of the assessment and diagnosis of PTTD and with aligning evidence and research findings to clinical practice. The quantitative investigation and results have shown that reliance on these clinical tests in providing worthwhile and informative clinical information to aid in the assessment and diagnosis of PTTD is lacking. Navicular drift and navicular drop have been investigated and found not to be significantly different to a non-PTTD population. The single heel rise test, often used in a diagnostic capacity for PTTD, has been investigated and the results are not statistically dissimilar between PTTD and control participants. Overall the results of this study confirm that PTTD receives a varied response to identification, assessment and diagnosis from specialist practitioners. Furthermore some of the tests confirmed by qualitative inquiry as being important to clinicians in the assessment process should not be relied on to differentiate pathology from non-pathological.
TensorFlow Quantum: A Software Framework for Quantum Machine Learning

We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators. We provide an overview of the software architecture and building blocks through several examples and review the theory of hybrid quantum-classical neural networks. We illustrate TFQ functionalities via several basic applications including supervised learning for quantum classification, quantum control, and quantum approximate optimization. Moreover, we demonstrate how one can apply TFQ to tackle advanced quantum learning tasks including meta-learning, Hamiltonian learning, and sampling thermal states. We hope this framework provides the necessary tools for the quantum computing and machine learning research communities to explore models of both natural and artificial quantum systems, and ultimately discover new quantum algorithms which could potentially yield a quantum advantage.
Automatic Compilation of Resources for Academic Writing and Evaluating with Informal Word Identification and Paraphrasing System

We present the first approach to automatically building resources for academic writing. The aim is to build a writing aid system that automatically edits a text so that it better adheres to the academic style of writing. On top of existing academic resources, such as the Corpus of Contemporary American English (COCA) academic Word List, the New Academic Word List, and the Academic Collocation List, we also explore how to dynamically build such resources that would be used to automatically identify informal or non-academic words or phrases. The resources are compiled using different generic approaches that can be extended for different domains and languages. We describe the evaluation of resources with a system implementation. The system consists of an informal word identification (IWI), academic candidate paraphrase generation, and paraphrase ranking components. To generate candidates and rank them in context, we have used the PPDB and WordNet paraphrase resources. We use the Concepts in Context (CoInCO) “All-Words” lexical substitution dataset both for the informal word identification and paraphrase generation experiments. Our informal word identification component achieves an F-1 score of 82%, significantly outperforming a stratified classifier baseline. The main contribution of this work is a domain-independent methodology to build targeted resources for writing aids.
Combining GHOST and Casper

We present "Gasper," a proof-of-stake-based consensus protocol, which is an idealized version of the proposed Ethereum 2.0 beacon chain. The protocol combines Casper FFG, a finality tool, with LMD GHOST, a fork-choice rule. We prove safety, plausible liveness, and probabilistic liveness under different sets of assumptions.
StereoNeuroBayesSLAM: A Neurobiologically Inspired Stereo Visual SLAM System Based on Direct Sparse Method

We propose a neurobiologically inspired visual simultaneous localization and mapping (SLAM) system based on direction sparse method to real-time build cognitive maps of large-scale environments from a moving stereo camera. The core SLAM system mainly comprises a Bayesian attractor network, which utilizes neural responses of head direction (HD) cells in the hippocampus and grid cells in the medial entorhinal cortex (MEC) to represent the head direction and the position of the robot in the environment, respectively. Direct sparse method is employed to accurately and robustly estimate velocity information from a stereo camera. Input rotational and translational velocities are integrated by the HD cell and grid cell networks, respectively. We demonstrated our neurobiologically inspired stereo visual SLAM system on the KITTI odometry benchmark datasets. Our proposed SLAM system is robust to real-time build a coherent semi-metric topological map from a stereo camera. Qualitative evaluation on cognitive maps shows that our proposed neurobiologically inspired stereo visual SLAM system outperforms our previous brain-inspired algorithms and the neurobiologically inspired monocular visual SLAM system both in terms of tracking accuracy and robustness, which is closer to the traditional state-of-the-art one.
A Corpus for Detecting High-Context Medical Conditions in Intensive Care Patient Notes Focusing on Frequently Readmitted Patients

A crucial step within secondary analysis of electronic health records (EHRs) is to identify the patient cohort under investigation. While EHRs contain medical billing codes that aim to represent the conditions and treatments patients may have, much of the information is only present in the patient notes. Therefore, it is critical to develop robust algorithms to infer patients’ conditions and treatments from their written notes. In this paper, we introduce a dataset for patient phenotyping, a task that is defined as the identification of whether a patient has a given medical condition (also referred to as clinical indication or phenotype) based on their patient note. Nursing Progress Notes and Discharge Summaries from the Intensive Care Unit of a large tertiary care hospital were manually annotated for the presence of several high-context phenotypes relevant to treatment and risk of re-hospitalization. This dataset contains 1102 Discharge Summaries and 1000 Nursing Progress Notes. Each Discharge Summary and Progress Note has been annotated by at least two expert human annotators (one clinical researcher and one resident physician). Annotated phenotypes include treatment non-adherence, chronic pain, advanced/metastatic cancer, as well as 10 other phenotypes. This dataset can be utilized for academic and industrial research in medicine and computer science, particularly within the field of medical natural language processing.
DL-based CSI Feedback and Cooperative Recovery in Massive MIMO

In this paper, we exploit the correlation between nearby user equipment (UE) and develop a deep learning-based channel state information (CSI) feedback and cooperative recovery framework, CoCsiNet, to reduce the feedback overhead. The CSI can be divided into two parts: shared by nearby UE and owned by individual UE. The key idea of exploiting the correlation is to reduce the overhead used to repeatedly feedback shared information. Unlike in the general autoencoder framework, an extra decoder is added at the base station to recover shared information from the feedback CSI of two nearby UE, but no modification is performed at the UE. For a UE with multiple antennas, we also introduce a baseline neural network architecture with long short-term memory modules to extract the correlation of nearby antennas. We propose two representative methods for converting CSI into bitstreams at the UE through quantization and binarization. Given that the CSI phase is not sparse, we propose a magnitude-dependent phase feedback strategy that introduces CSI magnitude information to the loss function of the phase feedback. Simulation results show the effectiveness of the proposed CoCsiNet and explain the mechanism of the CoCsiNet via parameter visualization.
Maximum Feasible Subsystem Algorithms for Recovery of Compressively Sensed Speech

The goal in signal compression is to reduce the size of the input signal without a significant loss in the quality of the recovered signal. One way to achieve this goal is to apply the principles of compressive sensing, but this has not been particularly successful for real-world signals that are insufficiently sparse, such as speech. We present three new algorithms based on solutions for the MAXimum Feasible Subsystem problem (MAX FS) that improve upon the state of the art in recovery of compressed speech signals: more highly compressed signals can be successfully recovered with greater quality. The new recovery algorithms deliver sparser solutions when compared with those obtained using traditional compressive sensing recovery algorithms. When tested by recovering compressively sensed speech signals from the TIMIT speech database, the recovered speech signals had better perceptual quality than speech signals recovered using traditional compressive sensing recovery algorithms.
Captioning Images with Novel Objects via Online Vocabulary Expansion

In this study, we introduce a low cost method for generating descriptions from images containing novel objects. Generally, constructing a model, which can explain images with novel objects, is costly because of the following: (1) collecting a large amount of data for each category, and (2) retraining the entire system. If humans see a small number of novel objects, they are able to estimate their properties by associating their appearance with known objects. Accordingly, we propose a method that can explain images with novel objects without retraining using the word embeddings of the objects estimated from only a small number of image features of the objects. The method can be integrated with general image-captioning models. The experimental results show the effectiveness of our approach.
Can We Read Speech Beyond the Lips? Rethinking RoI Selection for Deep Visual Speech Recognition

Recent advances in deep learning have heightened interest among researchers in the field of visual speech recognition (VSR). Currently, most existing methods equate VSR with automatic lip reading, which attempts to recognise speech by analysing lip motion. However, human experience and psychological studies suggest that we do not always fix our gaze at each other’s lips during a face-to-face conversation, but rather scan the whole face repetitively. This inspires us to revisit a fundamental yet somehow overlooked problem: can VSR models benefit from reading extraoral facial regions, i.e. beyond the lips? In this paper, we perform a comprehensive study on the evaluation of the effects of different facial regions with state-of-the-art VSR models, including the mouth, the whole face, the upper face, and even the cheeks. Experiments are conducted on both word-level and sentence-level benchmarks with different characteristics. We find that despite the complex variations of the data, incorporating information from extraoral facial regions, even the upper face, consistently benefits VSR performance. Furthermore, we introduce a simple yet effective method based on Cutout to learn more discriminative features for face-based VSR, hoping to maximise the utility of information encoded in different facial regions. Our experiments show obvious improvements over existing state-of-the-art methods that use only the lip region as inputs, a result we believe would probably provide the VSR community with some new and exciting insights.
Improving Neural Named Entity Recognition with Gazetteers

The goal of this work is to improve the performance of a neural named entity recognition system by adding input features that indicate a word is part of a name included in a gazetteer. This article describes how to generate gazetteers from the Wikidata knowledge graph as well as how to integrate the information into a neural NER system. Experiments reveal that the approach yields performance gains in two distinct languages: a high-resource, word-based language, English and a high-resource, character-based language, Chinese. Experiments were also performed in a low-resource language, Russian on a newly annotated Russian NER corpus from Reddit tagged with four core types and twelve extended types. This article reports a baseline score. It is a longer version of a paper in the 33rd FLAIRS conference (Song et al. 2020).
Me Love (SYN-)Cookies: SYN Flood Mitigation in Programmable Data Planes

The SYN flood attack is a common attack strategy on the Internet, which tries to overload services with requests leading to a Denial-of-Service (DoS). Highly asymmetric costs for connection setup - putting the main burden on the attackee - make SYN flooding an efficient and popular DoS attack strategy. Abusing the widely used TCP as an attack vector complicates the detection of malicious traffic and its prevention utilizing naive connection blocking strategies. Modern programmable data plane devices are capable of handling traffic in the 10 Gbit/s range without overloading. We discuss how we can harness their performance to defend entire networks against SYN flood attacks. Therefore, we analyze different defense strategies, SYN authentication and SYN cookie, and discuss implementation difficulties when ported to different target data planes: software, network processors, and FPGAs. We provide prototype implementations and performance figures for all three platforms. Further, we fully disclose the artifacts leading to the experiments described in this work.
Impact of Spatial Correlation in MIMO Radar

The impact of spatial correlation on mutual information (MI) is analyzed for MIMO radar. Unlike the work done in literature for statistical MIMO radar, we consider the spatial correlation of the target matrix elements to study the correlated MIMO radar performance. There is a trade-off between coherent processing gain in correlated MIMO radar and spatial diversity gain of target scatterers in uncorrelated MIMO radar. We address how the MI between the received signal and target channel matrix is affected by spatial correlation. Using majorization theory and the notion of Schur-convexity, we prove that MI has a changing behavior with respect to spatial correlation, where at low SNR, the MI is Schur-convex, i.e. showing increasing performance as correlation increases. However, this behavior changes at high SNR, since MI is Schur-concave at high SNR, hence it decreases as the spatial correlation increases. Moreover, we investigate the conditions for spatially uncorrelated MIMO radar. According to these conditions, as the operating frequency increases with respect to the target location and dimensions, the received paths become more uncorrelated. Hence, the setup with lower operating frequency (more correlated) performs better compared to the higher frequency setup at low SNR. However at high SNR, this behavior is reversed.
Making top-heavy IMFs from canonical IMFs near the Galactic Centre

We show that dynamical evolution in a strong (Galactic Centre-like) tidal field can create clusters that would appear to have very top-heavy IMFs. The tidal disruption of single star forming events can leave several bound `clusters' spread along 20 pc of the orbit within 1-2 Myr. These surviving (sub)clusters tend to contain an over-abundance of massive stars, with low-mass stars tending to be spread along the whole `tidal arm'. Therefore observing a cluster in a strong tidal field with a top-heavy IMF might well not mean the stars formed with a top-heavy IMF.
Chordal-TSSOS: A Moment-SOS Hierarchy That Exploits Term Sparsity with Chordal Extension

This work is a follow-up and a complement to arXiv:1912.08899 [math.OC] for solving polynomial optimization problems (POPs). The chordal-TSSOS hierarchy that we propose is a new sparse moment-SOS framework based on term-sparsity and chordal extension. By exploiting term-sparsity of the input polynomials we obtain a two-level hierarchy of semidefinite programming relaxations. The novelty and distinguishing feature of such relaxations is to obtain quasi block-diagonal matrices obtained in an iterative procedure that performs chordal extension of certain adjacency graphs. The graphs are related to the terms arising in the original data and not to the links between variables. Various numerical examples demonstrate the efficiency and the scalability of this new hierarchy for both unconstrained and constrained POPs. The two hierarchies are complementary. While the former TSSOS arXiv:1912.08899 [math.OC] has a theoretical convergence guarantee, the chordal-TSSOS has superior performance but lacks this theoretical guarantee.
DeLTra: Deep Light Transport for Projector-Camera Systems

In projector-camera systems, light transport models the propagation from projector emitted radiance to camera-captured irradiance. In this paper, we propose the first end-to-end trainable solution named Deep Light Transport (DeLTra) that estimates radiometrically uncalibrated projector-camera light transport. DeLTra is designed to have two modules: DepthToAtrribute and ShadingNet. DepthToAtrribute explicitly learns rays, depth and normal, and then estimates rough Phong illuminations. Afterwards, the CNN-based ShadingNet renders photorealistic camera-captured image using estimated shading attributes and rough Phong illuminations. A particular challenge addressed by DeLTra is occlusion, for which we exploit epipolar constraint and propose a novel differentiable direct light mask. Thus, it can be learned end-to-end along with the other DeLTra modules. Once trained, DeLTra can be applied simultaneously to three projector-camera tasks: image-based relighting, projector compensation and depth/normal reconstruction. In our experiments, DeLTra shows clear advantages over previous arts with promising quality and meanwhile being practically convenient.
Practical Reinforcement Learning For MPC: Learning from sparse objectives in under an hour on a real robot

Model Predictive Control (MPC) is a powerful control technique that handles constraints, takes the system's dynamics into account, and optimizes for a given cost function. In practice, however, it often requires an expert to craft and tune this cost function and find trade-offs between different state penalties to satisfy simple high level objectives. In this paper, we use Reinforcement Learning and in particular value learning to approximate the value function given only high level objectives, which can be sparse and binary. Building upon previous works, we present improvements that allowed us to successfully deploy the method on a real world unmanned ground vehicle. Our experiments show that our method can learn the cost function from scratch and without human intervention, while reaching a performance level similar to that of an expert-tuned MPC. We perform a quantitative comparison of these methods with standard MPC approaches both in simulation and on the real robot.
Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder

Deep probabilistic generative models enable modeling the likelihoods of very high dimensional data. An important application of generative modeling should be the ability to detect out-of-distribution (OOD) samples by setting a threshold on the likelihood. However, a recent study shows that probabilistic generative models can, in some cases, assign higher likelihoods on certain types of OOD samples, making the OOD detection rules based on likelihood threshold problematic. To address this issue, several OOD detection methods have been proposed for deep generative models. In this paper, we make the observation that some of these methods fail when applied to generative models based on Variational Auto-encoders (VAE). As an alternative, we propose Likelihood Regret, an efficient OOD score for VAEs. We benchmark our proposed method over existing approaches, and empirical results suggest that our method obtains the best overall OOD detection performances compared with other OOD method applied on VAE.