Miracle-dz/UniFAR-FTUs huggingface dataset:
Construction of equally entangled bases in arbitrary dimensions via quadratic Gauss sums and graph states

Recently, Karimipour and Memarzadeh [Phys. Rev. A 73, 012329 (2006)] studied the problem of finding a family of orthonormal bases in a bipartite space, each of dimension D, with the following properties: (i) The family continuously interpolates between a product basis and a maximally entangled basis as some parameter t is varied, and (ii) for a fixed t, all basis states have the same amount of entanglement. The authors derived a necessary condition and provided explicit solutions for D{<=}5, but the existence of a solution for arbitrary dimensions remained an open problem. We prove that such families exist in arbitrary dimensions by providing two simple solutions, one that employs the properties of quadratic Gauss sums and the other that uses graph states. The latter can be generalized to more than two parties--we can construct equally entangled families that vary continuously between a product basis and a graph-state basis.
Integrals of Irreducible Representations of Classical Groups

This paper is concerned with integrals which integrands are the monomials of matrix elements of irreducible representations of classical groups. Based on analysis on Young tableaux, we discuss some related duality theorems and compute the asymptotics of the group integrals when the signatures of the irreducible representations are fixed, as the rank of the classical groups go to infinity. These group integrals have physical origins in quantum mechanics, quantum information theory, and lattice Gauge theory.
Impact of Bio-alcohol Fuels Combustion on Particulate Matter (PM) Morphology from Efficient Gasoline Direct Injection Engines

The requirements for controlling particulate emissions in gasoline direct injection (GDI) engines, particularly in hybrid vehicles (where frequent cold-start event impact on both, particles characteristics and catalytic aftertreament efficiency), nesesitates the need for understanding their formation mechanism and their morphological characteristics. The findings described in this investigation have significance in the design of efficient Gasoline Particulate Filters (GPFs) and the development of computational models that predict particle filtration and oxidation processes. Morphological analysis of the particulate emissions from the combustion of commercial gasoline and two bio-alcohol blends: of 25% v/v ethanol in gasoline and 33% v/v butanol and 67% v/v gasoline, in a modern GDI engine has been carried out using a transmission electron microscopy. The primary particle size distribution from the combustion of butanol-gasoline blend was slightly smaller compared to gasoline, while the mean primary particle diameter was 3 nm smaller from the combustion of ethanol-gasoline fuel. This decrease in primary particle size for ethanol-gasoline blend was also reflected in a reduction of the mean radius of gyration and mean number of primary particles per agglomerate. The combustion of butanol-gasoline blend induced improved particle oxidation rates during the combustion process and post-oxidation stage, and led in 80% and 60% reduction in particle concentration in the engine exhaust when compared to the combustion of gasoline and ethanol-gasoline blend, respectively. Additionally, the estimation of the particle fractal dimension through the use of fractal equation, minimum bonding rectangle method and root form factor showed comparable results for butanol-gasoline and gasoline, with the particle agglomerates being more compact than the ethanol-gasoline fuel, where more chain like particles are seen. Therefore, particles emitted from the combustion of ethanol-gasoline fuel are easier to be trapped (lower fractal dimension) and present a higher reactivity (high surface/volume ratio) compared to particles emitted from gasoline combustion.
YOLOv5s-GTB: light-weighted and improved YOLOv5s for bridge crack detection

In response to the situation that the conventional bridge crack manual detection method has a large amount of human and material resources wasted, this study is aimed to propose a light-weighted, high-precision, deep learning-based bridge apparent crack recognition model that can be deployed in mobile devices' scenarios. In order to enhance the performance of YOLOv5, firstly, the data augmentation methods are supplemented, and then the YOLOv5 series algorithm is trained to select a suitable basic framework. The YOLOv5s is identified as the basic framework for the light-weighted crack detection model through experiments for comparison and validation.By replacing the traditional DarkNet backbone network of YOLOv5s with GhostNet backbone network, introducing Transformer multi-headed self-attention mechanism and bi-directional feature pyramid network (BiFPN) to replace the commonly used feature pyramid network, the improved model not only has 42% fewer parameters and faster inference response, but also significantly outperforms the original model in terms of accuracy and mAP (8.5% and 1.1% improvement, respectively). Luckily each improved part has a positive impact on the result. This paper provides a feasible idea to establish a digital operation management system in the field of highway and bridge in the future and to implement the whole life cycle structure health monitoring of civil infrastructure in China.
Development of Epoxy Grout Containing Fine Waste from Production of Mineral Wool Board Insulation

Within this work, it was experimentally verified that the waste from mineral wool board insulation production (WIRG) with high portion of glass recyclate (> 80%) and no organic material seems like ideal filler for polymer grouting materials. The main objective was to develop a progressive grout on epoxy basis with as high content of this secondary raw material as possible, while achieving physical and mechanical properties as e.g. very fast strength increase and high thermal resistance. With regard to the consistency of epoxy grout in the fresh state, three different filling were tested, namely 60%, 65% and 70%. The grout with lower filling is too fluid, and it is also disadvantageous from an economic point of view because a large amount of epoxy resin is used. On the other hand, at higher filing, it is not possible to mix the filler into epoxy resin properly. Setting of an optimal filler content in the mixture was performed mostly on the basis of the results of compressive and three-point flexural strength test. It was found out that the optimal amount of the filler is 65%. In case of the best formulation with optimal filler content (65% WIRG), the thermal resistance was monitored by determination of the glass transition temperature (Tg) by the dynamic mechanical analysis (DMA) method. Furthermore, the optical microscope with high resolution was used to monitor filler distribution and homogeneity of the hardened developed epoxy grout.
BaCaDI: Bayesian Causal Discovery with Unknown Interventions

Inferring causal structures from experimentation is a central task in many domains. For example, in biology, recent advances allow us to obtain single-cell expression data under multiple interventions such as drugs or gene knockouts. However, the targets of the interventions are often uncertain or unknown and the number of observations limited. As a result, standard causal discovery methods can no longer be reliably used. To fill this gap, we propose a Bayesian framework (BaCaDI) for discovering and reasoning about the causal structure that underlies data generated under various unknown experimental or interventional conditions. BaCaDI is fully differentiable, which allows us to infer the complex joint posterior over the intervention targets and the causal structure via efficient gradient-based variational inference. In experiments on synthetic causal discovery tasks and simulated gene-expression data, BaCaDI outperforms related methods in identifying causal structures and intervention targets.
Thread and Data Mapping in Software Transactional Memory: An Overview

In current microarchitectures, due to the complex memory hierarchies and different latencies on memory accesses, thread and data mapping are important issues to improve application performance. Software transactional memory (STM) is an abstraction used for thread synchronization, replacing the use of locks in parallel programming. Regarding thread and data mapping, STM presents new challenges and mapping opportunities, since (1) STM can use different conflict detection and resolution strategies, making the behavior of the application less predictable and; (2) the STM runtime has precise information about shared data and the intensity with each thread accesses them. These unique characteristics provide many opportunities for low-overhead, but precise statistics to guide mapping strategies for STM applications. The main objective of this paper is to survey the existing work about thread and data mapping that uses solely information gathered from the STM runtime to guide thread and data mapping decisions. We also discuss future research directions within this research area.
Comprehensive Survey of Ternary Full Adders: Statistics, Corrections, and Assessments

The history of ternary adders goes back to more than six decades ago. Since then, a multitude of ternary full adders (TFAs) have been presented in the literature. This paper conducts a review of TFAs so that one can be familiar with the utilized design methodologies and their prevalence. Moreover, despite numerous TFAs, almost none of them are in their simplest form. A large number of transistors could have been eliminated by considering a partial TFA instead of a complete one. According to our investigation, only 28.6% of the previous designs are partial TFAs. Also, they could have been simplified even further by assuming a partial TFA with an output carry voltage of 0V or VDD. This way, in a single-VDD design, voltage division inside the Carry generator part would have been eliminated and less power dissipated. As far as we have searched, there are only three partial TFAs with this favorable condition in the literature. Additionally, most of the simulation setups in the previous articles are not realistic enough. Therefore, the simulation results reported in these papers are neither comparable nor entirely valid. Therefore, we got motivated to conduct a survey, elaborate on this issue, and enhance some of the previous designs. Among 84 papers, 10 different TFAs (from 11 papers) are selected, simplified, and simulated in this paper. Simulation results by HSPICE and 32nm CNFET technology reveal that the simplified partial TFAs outperform their original versions in terms of delay, power, and transistor count.
Pulsed laser ablation in liquid of sp-carbon chains: Status and recent advances

This review provides a discussion of the current state of research on sp-carbon chains synthesized by pulsed laser ablation in liquid. In recent years, pulsed laser ablation in liquid (PLAL) has been widely employed for polyynes synthesis thanks to its flexibility with varying laser parameters, solvents, and targets. This allows the control of sp-carbon chains properties as yield, length, termination and stability. Although many reviews related to PLAL have been published, a comprehensive work reporting the current status and advances related to the synthesis of sp-carbon chains by PLAL is still missing. Here we first review the principle of PLAL and the mechanisms of formation of sp-carbon chains. Then we discuss the role of laser fluence (i.e. energy density), solvent, and target for sp-carbon chains synthesis. Lastly, we report the progress related to the prolonged stability of sp-carbon chains by PLAL encapsulated in polymeric matrices. This review will be a helpful guide for researchers interested in synthesizing sp-carbon chains by PLAL.
Style-Content Disentanglement in Language-Image Pretraining Representations for Zero-Shot Sketch-to-Image Synthesis

In this work, we propose and validate a framework to leverage language-image pretraining representations for training-free zero-shot sketch-to-image synthesis. We show that disentangled content and style representations can be utilized to guide image generators to employ them as sketch-to-image generators without (re-)training any parameters. Our approach for disentangling style and content entails a simple method consisting of elementary arithmetic assuming compositionality of information in representations of input sketches. Our results demonstrate that this approach is competitive with state-of-the-art instance-level open-domain sketch-to-image models, while only depending on pretrained off-the-shelf models and a fraction of the data.
MetaLR: Layer-wise Learning Rate based on Meta-Learning for Adaptively Fine-tuning Medical Pre-trained Models

When applying transfer learning for medical image analysis, downstream tasks often have signiﬁcant gaps with the pre-training tasks. Previous methods mainly focus on improving the transferabilities of the pre-trained models to bridge the gaps. In fact, model ﬁne-tuning can also play a very important role in tackling this problem. A conventional ﬁne-tuning method is updating all deep neural networks (DNNs) layers by a single learning rate (LR), which ignores the unique transferabilities of different layers. In this work, we explore the behaviors of different layers in the ﬁne-tuning stage. More precisely, we ﬁrst hypothesize that lower-level layers are more domain-speciﬁc while higher-level layers are more task-speciﬁc, which is veriﬁed by a simple bi-directional ﬁne-tuning scheme. It is harder for the pre-trained speciﬁc layers to transfer to new tasks than general layers. On this basis, to make different layers better co-adapt to the downstream tasks according to their transferabilities, a meta-learning-based LR learner, namely MetaLR, is proposed to assign LRs for each layer automatically. Extensive experiments on various medical applications ( i.e. , POCUS, BUSI, Chest X-ray, and LiTS) well conﬁrm our hypothesis and show the superior performance of the proposed methods to previous state-of-the-art ﬁne-tuning methods.
Understanding deep learning via decision boundary

This article discovers that the neural network (NN) with lower decision boundary (DB) variability has better generalizability. Two new notions, algorithm DB variability and (ϵ, η) -data DB variability, are proposed to measure the DB variability from the algorithm and data perspectives. Extensive experiments show significant negative correlations between the DB variability and the generalizability. From the theoretical view, two lower bounds based on algorithm DB variability are proposed and do not explicitly depend on the sample size. We also prove an upper bound of order O((1/√m)+ϵ+ηlog(1/η)) based on data DB variability. The bound is convenient to estimate without the requirement of labels and does not explicitly depend on the network size which is usually prohibitively large in deep learning.
Real-time Iris Recognition System for Non-Ideal Iris Images

Biometrics is popular nowadays because of its very useful security applications. There are different biometric technologies but iris recognition system was considered the most reliable since human irises are unique and cannot be forged easily. The study aims to segment ideal and non-ideal iris images with the help of Zuo and Xin Li's algorithm to determine the most accurate wavelet family and its coefficient for encoding the iris templates using Haar, Daubechies, Biorthogonal and Reverse- Biorthogonal wavelets. Test metrics, like False Rejection Rate (FRR), False Acceptance Rate (FAR), Compression Rate (CR), and Degrees-of-Freedom (DOF) were used in evaluating the performance of the system. Based on the results, the algorithm was able to segment ideal and non-ideal iris images, encode the irises and match the irises accurately. Haar was proven as the most accurate iris recognition algorithm while Reverse-Biorthogonal was the better wavelet algorithm image compression. There were different wavelets that would give better result in the recognition process depending on the algorithm or system used. The metrics suggested that the developed algorithm was sufficient for iris recognition.
Detecting Pulmonary Embolism from Computed Tomography Using Convolutional Neural Network

The clinical symptoms of pulmonary embolism (PE) are very diverse and non-specific, which makes it difficult to diagnose. In addition, pulmonary embolism has multiple triggers and is one of the major causes of vascular death. Therefore, if it can be detected and treated quickly, it can significantly reduce the risk of death in hospitalized patients. In the detection process, the cost of computed tomography pulmonary angiography (CTPA) is high, and angiography requires the injection of contrast agents, which increase the risk of damage to the patient. Therefore, this study will use a deep learning approach to detect pulmonary embolism in all patients who take a CT image of the chest using a convolutional neural network. With the proposed pulmonary embolism detection system, we can detect the possibility of pulmonary embolism at the same time as the patient's first CT image, and schedule the CTPA test immediately, saving more than a week of CT image screening time and providing timely diagnosis and treatment to the patient.
ADeepLearningFrameworkforDamageAssessmentofComposite Sandwich Structures

%e vibrational behavior of composite structures has been demonstrated as a useful feature for identifying debonding damage. %e precision of the damage localization can be greatly improved by the addition of more measuring points. %erefore, full-field vibration measurements, such as those obtained using high-speed digital image correlation (DIC) techniques, are particularly useful. In this study, deep learning techniques, which have demonstrated excellent performance in image classification and segmentation, are incorporated into a novel approach for assessing damage in composite structures. %is article presents a damage-assessment algorithm for composite sandwich structures that uses full-field vibration mode shapes and deep learning. First, the vibration mode shapes are identified using high-speed 3D DIC measurements. %en, Gaussian process regression is implemented to estimate the mode shape curvatures, and a baseline-free gapped smoothing method is applied to compute the damage images. %e damage indices, which are represented as grayscale images, are processed using a convolutional-neuralnetwork-based algorithm to automatically identify damaged regions. %e proposed methodology is validated using numerical and experimental data from a composite sandwich panel with different damage configurations.
Elliptic constructions of hyperkaehler metrics III: Gravitons and Poncelet polygons

In the generalized Legendre approach, the equation describing an asymptotically locally Euclidean space of type $D_n$ is found to admit an algebraic formulation in terms of the group law on a Weierstrass cubic. This curve has the structure of a Cayley cubic for a pencil generated by two transversal plane conics, that is, it takes the form $Y^2 = \det ({\cal A}+X{\cal B})$, where ${\cal A}$ and ${\cal B}$ are the defining $3 \times 3$ matrices of the conics. In this light, the equation can be interpreted as the closure condition for an elliptic billiard trajectory tangent to the conic ${\cal B}$ and bouncing into various conics of the pencil determined by the positions of the monopoles. Poncelet's porism guarantees then that once a trajectory closes to a star polygon, any trajectory will close, regardless of the starting point and after the same number of steps.
Efficient Passive Shielding of MWCNT Interconnects to Reduce Crosstalk Effects in Multiple-Valued Logic Circuits

In this paper, the effectiveness of passive shielding of multiwalled carbon nanotube (MWCNT) bundle interconnects in reducing the crosstalk effects in carbon nanotube FET based ternary circuits is comprehensively investigated. In addition, based on the tighter sublithographic pitch of MWCNTs compared to Cu, we decrease the space between the carbon nanotube (CNT) interconnects to reduce the area overhead, while still having significantly higher effectiveness compared with Cu. The worst-case crosstalk delay and noise for passively shielded MWCNT and Cu wires at 14 nm technology are evaluated using comprehensive HSPICE simulations. The results indicate that the crosstalk delay and noise area in the efficient passively shielded MWCNT bundle interconnects are much smaller than the shielded Cu wires for ternary logic. Furthermore, the impacts of process variation on the crosstalk effects in these interconnects in ternary logic are investigated using Monte Carlo simulations. The results demonstrate the robustness of the passively shielded MWCNT bundles in ternary logic.
A Smartphone-Based Drowsiness Detection and Warning System for Automotive Drivers

This paper presents a smartphone-based system for the detection of drowsiness in automotive drivers. The proposed framework uses three-stage drowsiness detection. The first stage uses the percentage of eyelid closure (PERCLOS) obtained through images captured by the front camera with a modified eye state classification method. The system uses near infrared lighting for illuminating the face of the driver during night-driving. The second step uses the voiced to the unvoiced ratio obtained from the speech data from the microphone, in the event PERCLOS crosses the threshold. A final verification stage is used as a touch response within a stipulated time to declare the driver as drowsy and subsequently sound an alarm. The device maintains a log file of the periodic events of the metrics along with the corresponding GPS coordinates. The system has three advantages over existing drowsiness detection systems. First, the three-stage verification process makes the system more reliable. The second advantage is its implementation on an Android smart-phone, which is readily available to most drivers or cab owners as compared to other general purpose embedded platforms. The third advantage is the use of SMS service to inform the control room as well as the passenger regarding the loss of attention of the driver. The framework provides 93.33% drowsiness state classification as compared to a single stage which gives 86.66%.
Positivit\'e des exposants de Lyapounov pour un op\'erateur de Schr\

In this note, we study a continuous matrix-valued Anderson-type model. Both leading Lyapounov exponents of this model are proved to be positive and distincts for all energies in $(2,+\infty)$ except those in a discrete set, which leads to absence of absolutely continuous spectrum in $(2,+\infty)$. The methods, using group theory results by Breuillard and Gelander, allow for singular Bernoulli distributions.