Miracle-dz/UniFAR-FTUs huggingface dataset:
The shear behaviour of non-prismatic reinforced concrete beams determined using digital image correlation

It can be seen that when subject to a uniformly distributed load, a prismatic, steel reinforced concrete beam with constant longitudinal and transverse reinforcement percentages is inefficient. Following simple optimisation routines can result in beams whose flexural and shear capacities reflect the requirements of their loading envelope, and such elements tend to be non-prismatic. Structural research into the use of flexible formwork has developed simple methods for the creation of such elements to offer material and carbon emission savings of up to 40%. However, the shear behaviour of such variable section steel reinforced concrete beams has previously been an area of confusion for designers, who must often rely on empirical relationships derived from prismatic beam tests. This paper presents the results of new tests undertaken at the University of Bath on tapered concrete beams designed using BS EN 1992-1-1 [1], the compressive force path method and a strut and tie model, in which digital image correlation was used to analyse the specimens. Using the 'MatchID' program, strain distributions in each of the specimens during loading were determined, with the results allowing important comparisons to be drawn between the various design methods. A digital image correlation (DIC) technique was subsequently used to verify new proposals for the shear design of non-prismatic concrete beams, which aim to provide improved guidance for designers of optimised concrete structures.
Element-specific channels for the photoexcitation of V-doped TiO2 nanoparticles

Incorporation of dopants in semiconductors is commonly used to modify the optical response and improve the efﬁciency of related devices. A physical understanding with elemental and local sensitivity of the electron excitation and trapping channels which follow photoexcitation is a prerequisite for knowledge-based materials design.Byusinghigh-resolutionx-rayabsorptionmethodsweshowthat,inV-dopedTiO 2 nanoparticles,subband-gap visible light absorption is predominantly due to excitation of electrons from V ions to defective and long-lived Ti sites. We thus identify an element-speciﬁc photoexcitation channel.
Hydrodynamics of flagellar swimming and synchronization

What is flagellar swimming? Cilia and flagella are whip-like cell appendages that can exhibit regular bending waves. This active process emerges from the non-equilibrium dynamics of molecular motors distributed along the length of cilia and flagella. Eukaryotic cells can possess many cilia and flagella that beat in a coordinated fashion, thus transporting fluids, as in mammalian airways or the ventricular system inside the brain. Many unicellular organisms posses just one or two flagella, rendering them microswimmers that are propelled through fluids by the flagellar beat including sperm cells and the biflagellate green alga Chlamydomonas. Objectives. In this thesis in theoretical biological physics, we seek to understand the nonlinear dynamics of flagellar swimming and synchronization. We investigate the flow fields induced by beating flagella and how in turn external hydrodynamic flows change speed and shape of the flagellar beat. This flagellar load-response is a prerequisite for flagellar synchronization. We want to find the physical principals underlying stable synchronization of the two flagella of Chlamydomonas cells. Results. First, we employed realistic hydrodynamic simulations of flagellar swimming based on experimentally measured beat patterns. For this, we developed analysis tools to extract flagellar shapes from high-speed videoscopy data. Flow-signatures of flagellated swimmers are analysed and their effect on a neighboring swimmer is compared to the effect of active noise of the flagellar beat. We were able to estimate a chemomechanical energy efficiency of the flagellar beat and determine its waveform compliance by comparing findings from experiments, in which a clamped Chlamydomonas is exposed to external flow, to predictions from an effective theory that we designed. These mechanical properties have interesting consequences for the synchronization dynamics of Chlamydomonas, which are revealed by computer simulations. We propose that direct elastic coupling between the two flagella of Chlamydomonas, as suggested by recent experiments, in combination with waveform compliance is crucial to facilitate in-phase synchronization of the two flagella of Chlamydomonas.
Observer‐based leader‐following consensus of uncertain nonlinear multi‐agent systems

In this paper, the leader‐following consensus problem of uncertain high‐order nonlinear multi‐agent systems on directed graph with a fixed topology is studied, where it is assumed that the relative states of a follower and its neighbors are immeasurable and only the relative outputs are available. Nonlinear adaptive observers are firstly proposed for each follower to estimate the states of it and its neighbors, and an observer‐based distributed adaptive control scheme is constructed to guarantee that all followers asymptotically synchronize to a leader with tracking errors being semi‐globally uniform ultimate bounded. On the basis of algebraic graph theory and Lyapunov theory, the closed‐loop system stability analysis is conducted. Finally, numerical simulations are presented to illustrate the effectiveness and potential of the proposed new design techniques. Copyright © 2017 John Wiley & Sons, Ltd.
Performance monitoring and assessment of optimality under a linear quadratic criterion

Identification schemes that are capable of taking account of model un­ certainties and also tracking the time-varying dynamics of the process offer great power to adaptive control systems. This paper introduces a new approach to pa­ rameter identification that has these characteristics. The parameter estimator uses a normalised confidence distribution function to compute bounds on the process parameters. This distribution function adds some characteristics of certainty equiv­ alence schemes to the “traditional” parameter bounding estimator, and straight­ forwardly solves the problems of adaptive tracking and detection of outliers.
Rastermap: a discovery method for neural population recordings

Neurophysiology has long progressed through exploratory experiments and chance discoveries. Anecdotes abound of researchers setting up experiments while listening to spikes in real time and observing a pattern of consistent firing when certain stimuli or behaviors happened. With the advent of large-scale recordings, such close observation of data has become harder because high-dimensional spaces are impenetrable to our pattern-finding intuitions. To help ourselves find patterns in neural data, our lab has been openly developing a visualization framework known as “Rastermap” over the past five years. Rastermap takes advantage of a new global optimization algorithm for sorting neural responses along a one-dimensional manifold. Displayed as a raster plot, the sorted neurons show a variety of activity patterns, which can be more easily identified and interpreted. We first benchmark Rastermap on realistic simulations with multiplexed cognitive variables. Then we demonstrate it on recordings of tens of thousands of neurons from mouse visual and sensorimotor cortex during spontaneous, stimulus-evoked and task-evoked epochs, as well as on whole-brain zebrafish recordings, widefield calcium imaging data, population recordings from rat hippocampus and artificial neural networks. Finally, we illustrate high-dimensional scenarios where Rastermap and similar algorithms cannot be used effectively.
Finite-time robust fault detection filter design for interconnected systems concerning with packet dropouts and changing structures

ABSTRACT This paper studies the problem of finite-time robust fault detection filter design for interconnected systems with packet dropouts and changing structures. There are some packet dropouts occurring in the network before transmitting to the robust fault detection filter. Different from previous results, taking cases of randomly disconnected interconnections among these subsystems into account, a switched scheme is introduced to address the problem of failed interconnection. Meanwhile, an arbitrary switching signal is introduced to denote which one subsystem is activated at the instant of disconnection of interconnection. The filter is designed to guarantee the finite-time boundedness and the achievement of the performance to the fault detection system. Sufficient conditions on the existence of desired filter are presented in term of feasible linear matrix inequalities by using the switched Lyapunov functional approach based on stochastic functional. Two examples are finally given to show the effectiveness of our method.
Learning deep discriminative features based on cosine loss function

Deep feature representation is widely used in various visual applications. However, the feature extracted by the convolutional neural networks (CNNs) is inappropriate for cosine similarity measurement. Because the classical CNNs are designed for classification rather than for similarity comparison. A novel cosine loss function for learning deep discriminative features, which are fit to the cosine similarity measurement, is designed. The loss can constrain the distribution of the features in the same class to be in a narrow angle region. Furthermore, a discriminative feature learning network framework and its corresponding two-stage learning method to learn the parameters is proposed. Experimental results show that the proposed method achieves state-of-the-art performance on the public Cifar10 and Market1501 datasets.
Investigating the neural basis of schematic false memories by examining schematic and lure pattern similarity

Schemas allow us to make assumptions about the world based upon previous experiences and aid in memory organization and retrieval. However, a reliance on schemas may also result in increased false memories to schematically related lures. Prior neuroimaging work has linked schematic processing in memory tasks to activity in prefrontal, visual, and temporal regions. Yet, it is unclear what type of processing in these regions underlies memory errors. The current study examined where schematic lures exhibit greater neural similarity to schematic targets, leading to this memory error, as compared to neural overlap with non-schematic lures, which, like schematic lures, are novel items at retrieval. Results showed that patterns of neural activity in ventromedial prefrontal cortex, medial frontal gyrus, middle temporal gyrus, hippocampus, and occipital cortices exhibited greater neural pattern similarity for schematic targets and schematic lures than between schematic lures and non-schematic lures. As such, results suggest that schematic membership, and not object history, may be more critical to the neural processes underlying memory retrieval in the context of a strong schema.
Motor, somatosensory, and executive cortical areas directly modulate firing activity in the auditory midbrain

We have recently reported that the central nucleus of the inferior colliculus (the auditory midbrain) is innervated by glutamatergic pyramidal cells originating not only in auditory cortex (AC) but also in multiple ‘non-auditory’ regions of the cerebral cortex. Using optogenetics and electrical stimulation, we investigated the functional properties of these descending connections in vivo in anaesthetised rats. A retrograde virus encoding green fluorescent protein (GFP) and channelrhodopsin (ChR2) injected into the central nucleus of the inferior colliculus (ICC), labelled discrete groups of cells in multiple areas of the cerebral cortex. Light stimulation of AC and M1 caused local activation of cortical neurones and increased the firing rate of neurones in ICc indicating a direct excitatory input from AC and M1 to ICC. Electrical stimulation of M1, secondary motor, somatosensory and prefrontal cortical regions evoked short, fixed latency firing events in ICC as well as longer latency, longer duration increases in firing activity. The short latency events were singular spikes of consistent shape and size likely resulting from monosynaptic excitation of individual ICC units. The longer latency responses comprised multiple units and spikes occurred with significant temporal jitter suggesting polysynaptic activation of local circuits within the ICC. The probability of the monosynaptic event, the magnitude of the polysynaptic response, and the area of ICC affected were dependent on the stimulus current. Our data are consistent with cortical regions exerting an important excitatory direct and indirect regulation of ICc neurones. Significance statement We have recently described inputs from motor, somatosensory, and executive cortices to the inferior colliculus (IC, auditory midbrain). Here we provide functional evidence for such connections. Optogenetics, using a retrograde virus encoding channelrhodopsin injected into IC revealed a direct excitatory influence of neurones in auditory and motor cortices on firing in IC. Electrical stimulation of discrete cortical regions revealed that multiple non-auditory cortical regions have a direct monosynaptic excitatory influence on neurones in the IC which, in turn, activates local circuits increasing the firing probability of multiple neurones in the IC. This is the first evidence for circuitry by which auditory processing can be influenced at an early stage by activity in the sensory, motor and executive domains.
Human-Machine Interaction: Causal Dynamical Networks

The objective of this paper is to introduce a modified version of the Causal Dynamical Networks (CDN) algorithm for application in the human-machine interaction. It is demonstrated that an individual does not interact with one robot, but with a multitude of personalities stored in the robot. These personalities are independent of each other. A robot thus does not have a unique personality. In order for a robot to become a unique individual a new algorithm is proposed. The new algorithm is called the Causal Form Fluctuation Network (CEFN). It is shown that such an algorithm can help machines develop similar to human general intelligence capabilities such as interpretation, wisdom (acquiring knowledge), and prediction (intuition). Also to be able to make decisions, have ideas, and imaginations.
An introduction to semiparametric function-on-scalar regression

Abstract: Function-on-scalar regression models feature a function over some domain as the response while the regressors are scalars. Collections of time series as well as 2D or 3D images can be considered as functional responses. We provide a hands-on introduction for a flexible semiparametric approach for function-on-scalar regression, using spatially referenced time series of ground velocity measurements from large-scale simulated earthquake data as a running example. We discuss important practical considerations and challenges in the modelling process and outline best practices. The outline of our approach is complemented by comprehensive R code, freely available in the online appendix. This text is aimed at analysts with a working knowledge of generalized regression models and penalized splines.
Decoding working memory information from persistent and activity-silent neurons in the primate prefrontal cortex

Persistent activity of neurons in the prefrontal cortex has been thought to represent the information maintained in working memory, though alternative models have recently challenged this idea. Activity-silent theories posit that stimulus information may be maintained by the activity pattern of neurons that do not produce firing rate significantly elevated about their baseline during the delay period of working memory tasks. We thus tested the ability of neurons that do and do not generate persistent activity in the prefrontal cortex of monkeys to represent spatial and object information in working memory. Neurons that generated persistent activity represented more information about the stimuli in both spatial and object working memory tasks. The amount of information that could be decoded from neural activity depended on the choice of decoder and parameters used but neurons with persistent activity outperformed non-persistent neurons consistently. Although averaged across all neurons and stimuli, firing rate did not appear clearly elevated above baseline during the maintenance of neural activity particularly for object working memory, this grant average masked neurons that generated persistent activity selective for their preferred stimuli, which carried the majority of information about the stimulus identity. These results reveal that prefrontal neurons with generate persistent activity constitute the primary mechanism of working memory maintenance in the cortex. NEW AND NOTEWORTHY Competing theories suggest that neurons that generate persistent activity or do not are primarily responsible for the maintenance of information, particularly regarding object working memory. While the two models have been debated on theoretical terms, direct comparison of empirical results have been lacking. Analysis of neural activity in a large database of prefrontal recordings revealed that neurons that generate persistent activity were primarily responsible for the maintenance of both spatial and object working memory.
Optical system for object detection and delineation in space

Abstract. Object recognition and delineation is an important task in many environments, such as in crime scenes and operating rooms. Marking evidence or surgical tools and attracting the attention of the surrounding staff to the marked objects can affect people’s lives. We present an optical system comprising a camera, computer, and small laser projector that can detect and delineate objects in the environment. To prove the optical system’s concept, we show that it can operate in a hypothetical crime scene in which a pistol is present and automatically recognize and segment it by various computer-vision algorithms. Based on such segmentation, the laser projector illuminates the actual boundaries of the pistol and thus allows the persons in the scene to comfortably locate and measure the pistol without holding any intermediator device, such as an augmented reality handheld device, glasses, or screens. Using additional optical devices, such as diffraction grating and a cylinder lens, the pistol size can be estimated. The exact location of the pistol in space remains static, even after its removal. Our optical system can be fixed or dynamically moved, making it suitable for various applications that require marking of objects in space.
Sociodemographic and Clinical Characteristics of Adult Asthma Patients: A Cross-sectional Study

Objective: Asthma is one of the most prevalent public health problems worldwide, known to be difficult to control. This study investigates the sociodemographic and clinical characteristics of patients with asthma and evaluate asthma control using the asthma control test (ACT). Methods: This cross-sectional study was conducted between 13 th January 2020 to 27 th February 2020 at Kırşehir Ahi Evran University Training and Research Hospital. ACT and general information form were used for data collection. Multiple linear regression analysis was performed to determine the sociodemographic and clinical characteristics of adult asthma patients. Two-dimensional graphics were plotted to investigate the patterns between asthma control levels and these risk factors. Results: Out of 140 participants, n=17 (12.1%) were found to have partly controlled asthma. Exercise habit was associated with uncontrolled asthma levels (p=0.043). Male gender, irregular drug use, and lower body mass index (BMI) were associated with higher ACT scores. Partially controlled asthma levels were associated with males, high school graduates, and normal weight, irregular drug use. Uncontrolled asthma levels were related to obesity, regular drug use, and females, housewives, patients aged between 41 and 50 years, having primary school degrees, and illiterate participants. Male gender was found as a sociodemographic risk factor, while lower BMI and irregular drug use were recorded as clinical risk factors. Moreover, a high prevalence of uncontrolled asthma (87.9%) was revealed among adult asthma patients. Conclusion: Training programs for patients with asthama regarding the risk factors are suggested to be organized to increase the awareness of asthma.
Dependence of the ellipsometric parameters of reflected light on the orientation of the optical axis relative to the plane of incidence

The reflection of linearly polarized light at the boundary of a plane anisotropic layer is considered, the optical axis of which is located arbitrarily in relation to the plane of incidence. Matrix amplitude coefficients of reflection and transmission of light by a plane anisotropic uniaxial layer are obtained, the energy coefficients of reflection, as well as ellipsometric parameters of the reflected light, and also their angular spectra.
HIGHLIGHT GENERATION WITH ELMO CONTEXTUAL EMBEDDINGS

With the advent of digital publishing and online databases, the volume of textual data generated by scientific research has increased exponentially. This makes it increasingly diﬀicult for academics to keep up with new breakthroughs and synthesise important information for their own work. Abstracts have long been a standard feature of scientific papers, providing a concise summary of the paper’s content and main findings. In recent years, some journals have begun to provide research highlights as an additional summary of the paper. The aim of this article is to create research highlights automatically by using various sections of a research paper as input. We employ a pointer-generator network with a coverage mechanism and pretrained ELMo contextual embeddings to generate the highlights. Our experiments shows that the proposed model outperforms several competitive models in the literature in terms of ROUGE, METEOR, BERTScore, and MoverScore metrics.
Xu hướ ng chuy ển đổ i k ỹ thu ậ t s ố d ị ch v ụ công cho t ỉ nh Bình Dương Digital transformation trends of public services for Binh Duong province

: The trend of digital transformation in public services in Vietnam is currently being paid attention and strongly built in terms of infrastructure. The public service industry of Binh Duong province is not an exception to that trend, the trend of digital transformation of urban government. Binh Duong is considered as one of the provinces with a strong industrial development speed, thereby leading to favorable conditions for the implementation of technological revolution strategies, especially the transformation of technology. number in the public service. The purpose of this paper is to make comments on how digital transformation of public services in Binh Duong province presents challenges and opportunities for success in building e-government. The article will present the outstanding features of the digital transformation of public services, thereby providing digital transformation trends for the urban government of Binh Duong province in the future construction and development.