Miracle-dz/UniFAR-FTUs huggingface dataset:
Cerebrovascular and cardiovascular autonomic regulation in sickle cell patients with white matter lesions

Background The prevalence of asymptomatic white matter lesions (WML) in patients with sickle cell disease (SCD) has been described to be very frequent in young adults. Cerebrovascular regulation and cardiovascular autonomic regulation, more specifically the sympatho-vagal balance can be altered in SCD. In this study we assessed the association between WML, cerebrovascular regulation and sympatho-vagal balance in SCD. Method Adults with no history of stroke from a cohort of SCD patients were prospectively evaluated for, cerebrovascular regulation using Mx for autoregulation, breath holding test for cerebrovascular reactivity and cerebral arterial compliance calculated from arterial blood pressure and cerebral velocities. Sympatho-vagal balance was assessed using heart rate variability parameters. WML was assessed with MRI using Fazekas score grading and the presence of lacunar lesions. Results Forty-one patients (F/M:25/16) were included. Median age was 37.5 (range 19-65). Twenty-nine (70,7%) patients had SS genotype, 7 patients (17,1%) had SC genotype and 5 (12,2%) patients had S{degree sign} genotype. Among the 41 patients included, 11 patients had WML (26,8%). Patients with WML were significantly older (44.5 vs 30.6 years; p<0.001), had a lower HF (HF 157 ms2 vs HF 467.6 ms2; p<0.005) and impaired cerebral arterial compliance (CaBVR 15.4 vs 37.3 cm3/mmHg; p<0.014). Cerebral blood flow velocities, reactivity to breath holding test and cerebral autoregulation parameters did not significantly differ between the two groups. Conclusions Lower parasympathetic activity and impaired cerebral arterial compliance were associated with WML in adults with SCD. This could potentially yield to a better understanding of pathophysiological parameters leading to premature cerebrovascular ageing in SCD patients.
A Phase‐field Model of Dislocations on Parallel Slip Planes

We establish a line‐tension energy for dislocation networks concentrated in finitely many parallel slip planes as the limit of a Peierls‐Nabarro energy. Dislocations in different planes interact depending on their distance. At intermediate distances a two‐scale dislocation microstructure is optimal. (© 2015 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Effect of Low-intensity Ultrasound on Cd Tolerance of Partial Nitrification Sludge

Cadmium (Cd) is a typical nitrification inhibitor, which has a strong inhibitory effect on partial nitrification (PN). The discrepancy on Cd tolerance of PN sludge caused by low-intensity ultrasound (LIU) was studied. It suggested that the half inhibitory concentration (IC50) of sludge was increased from 24.51 to 29.07 mg/L by 35 days of ultrasonic treatment, and the Cd tolerance of sludge was enhanced. Further analysis indicated that contents of extracellular polymeric substances (EPS) rose substantially because of LIU, which played a catalytic role on increasing adsorption capacity of PN sludge for Cd.
Study of Air Pressure on Arc Ion Plating on Epoxy Resin Matrix Composites at Meter Range Working Distance

The metal film was deposited on the surface of epoxy resin based composite by arc ion plating technology. The distribution of metal ions at the interface of matrix was simulated. The growth mode of metal ions on the composite surface was analyzed. The deposition rate, surface morphology and membrane base bonding strength were investigated. The results show that the transition layer of nm level will be formed on the surface of composite materials, and the larger the sputtering energy of metal ions, the deeper the distribution on the surface of composite materials. Nucleation occurs at the junction of fiber and resin matrix, and the fusion of nucleated particles grows along the fiber boundary with the increase of deposition time, and extends to the vertical fiber direction. Finally, the particles between fibers and the particles on the boundary gradually expand and merge into layered films. The working pressure decreased, the deposition rate of metal increased, the grain size decreased, the large particles on the surface of the metal coating decreased, and the bonding strength of the film base increased.
Analysis of 00Cr17Ni14Mo2 Bellows Leakage

A multi-layer welded bellows made of 00Cr17Ni14Mo2 was found to have leakage points in the heat-affected zone of the weld during airtight inspection and leak detection by helium mass spectrometry. In view of the leakage of multilayer welded bellows, the reasons for the leakage of multilayer welded bellows areanalyzed by means of macroscopic observation, electron microscope observation, energy spectrum analysis, metallographic examination, microhardness test and chemical composition analysis. The results show that there are liquefaction cracks in the heat-affected zone of the outer bellows, which results in air leakage in the airtight test. The generation of liquefaction crack is related to the low Ni content in the material and the pollution of S and Cu elements. The cleaning treatment of the material surface in the welding area should be strengthened before welding to reduce the generation of cracks. At the same time, should be paid attention to the control of welding raw material composition.
Improved Design of Feedback Matrix of Adaptive Observer for Induction Motor

In order to address the issue of the induction motor speed sensorless vector control system’s poor dynamic performance at low speed, this research suggests a brand-new feedback matrix algorithm for the full-order adaptive observer. Firstly, the open-loop transfer function based on the back-EMF error is analyzed, the stability conditions of the induction motor operation are deduced, and a preliminary framework is established for designing the feedback gain. Secondly, in order to maximize the system’s dynamic performance, the damping ratio of the speed identification system is chosen as the subject of study. To achieve this, the pole of the second-order characteristic equation of the observer’s complex number system is set up to satisfy the premise of stable operation at low speed and inverted state. The outcomes of the simulations confirm the method’s efficacy.
A FBS Approach to Designing Monitoring System in Nuclear Power Plant

The design framework of human-machine interface (HMI) provides guiding principles for the display of digital information on the system interface. In order to solve the problem of human error caused by the unreasonable design of information interaction interface of complex system, this paper takes the feedwater flow control system (ARE) of nuclear power plant as an example to optimize . The design of the human-machine interface of the system is based on the framework of function behavior state (FBS), defines the mapping relationship between various levels of the system interface, defines the information requirements of the monitoring system interface and presents the information according to the cognitive decision-making theory, and improves it on the basis of the original interface. The purpose of this research is to make the monitoring interface more consistent with the mental model of the operator and reduce the cognitive workload and error probability, so as to ensure the safe and reliable operation of the nuclear power plant. This research also provides some reference for the optimal design of the HMI of the monitoring system of the nuclear power plant in the future.
Designing of Pd1/TiO2-x Nanosheets for the Electrocatalyzed Reduction of CO2

The electrochemical reduction of carbon dioxide into valuable products has attracted increasing attention to meet the demand for sustainable fuel, as well as achieving the carbon neutralization strategy. Pd is art of the state CO2RR catalyst but prone to be poisoned by the primary product CO. For the modulating the electronic state of Pd sites and therefore affinity for CO, we herein synthesized TiO2-x nanosheets to support Pd atoms. The oxygen vacancies were introduced into the titanium oxide by calcination to increase the electric conductivity of the titanium oxide, and more importantly, as a means of modifying the energy diagram of Pd. The morphology of the as-synthesized Pd1/TiO2-x was observed on high-resolution transmission electron microscope and Cs-corrected high-angle annular dark-field (HAADF)-scanning transmission electron microscope. The structure of the atomically dispersed palladium was also characterized by FTIR spectra of chemisorbed CO. The impedance of the Pd1/TiO2-x was determined to prove the improvement of its electric conductivity. The Faradaic efficiency for liquid products, mainly HCOOH/CH3OH of the electrochemical reduction of CO2 using this catalyst is 14.74%.
What is the Expected Transient Behavior of Opinion Evolution for Two Communities?

We study the transient behavior of a gossip model, in which agents randomly interact pairwise over a weighted graph with two communities. Edges within each community have identical weights, different from the weights between communities. It is shown that, at the early stage of the opinion evolution, the expected agent states in the same community have identical sign, despite influence of stubborn agents. Moreover, it is shown that the expected states of the agents in the same community concentrate around the initial average opinion of that community, if the weights within communities are larger than between. In contrast, if the edge weights between communities are larger, then the expected states of all agents concentrate around everyone's initial average opinion. Different from the traditional asymptotic analysis in the opinion dynamics literature, these results focus on the initial phase of opinion evolution and establish a correspondence between community structure and transient behavior of the gossip model. The results are illustrated by numerical examples.
On Galois Coverings of curves and their Families

In this paper, we describe Galois covers of algebraic curves and their families by using local systems associated to push-forward of sheaves by the structure morphism. More precisely, if $f:C\to Y$, we consider the sheaves $f_*(\C)$. The group action by the Galois group $G$, yields a decomposition of this sheaf into irreducible local systems corresponding to irreducible representations of the group $G$. If $\rho$ is such an irreducible representation, the eigensheaf $\L_{\rho}$ of $f_*(\C)$ gives rise to another useful sheaf which is related to the homology group $H_1(C,\C)$. Using this, we describe the action of the Galois group $G$ on the homology group. As a particular example, we study the Dihedral covers of $\P^1$ in some detail.
Band-gap expansion in the surface-localized electronic structure of MoS 2 (0002)

The electronic band structure of MoS${}_{2}$ single crystals has been investigated using angle-resolved photoelectron spectroscopy and first-principles calculations. The orbital symmetry and $k$ dispersion of these electronic states responsible for the direct and the indirect electronic band gaps have been unambiguously determined. By experimentally probing an increase of the electronic band gap, we conclude that a MoS${}_{2}$ (0002) surface localized state exists just below the valence band maximum at the $\ensuremath{\Gamma}$ point. This electronic state originates from the sulfur planes within the topmost layer. Our comprehensive study addresses the surface electronic structure of MoS${}_{2}$ and the role of van der Waals interlayer interactions.
A Novel Dual of Shannon Information and Weighting Scheme

Shannon Information theory has achieved great success in not only communication technology where it was originally developed for but also many other science and engineering fields such as machine learning and artificial intelligence. Inspired by the famous weighting scheme TF-IDF, we discovered that information entropy has a natural dual. We complement the classical Shannon information theory by proposing a novel quantity, namely troenpy. Troenpy measures the certainty, commonness and similarity of the underlying distribution. To demonstrate its usefulness, we propose a troenpy based weighting scheme for document with class labels, namely positive class frequency (PCF). On a collection of public datasets we show the PCF based weighting scheme outperforms the classical TF-IDF and a popular Optimal Transportation based word moving distance algorithm in a kNN setting. We further developed a new odds-ratio type feature, namely Expected Class Information Bias(ECIB), which can be regarded as the expected odds ratio of the information quantity entropy and troenpy. In the experiments we observe that including the new ECIB features and simple binary term features in a simple logistic regression model can further significantly improve the performance. The simple new weighting scheme and ECIB features are very effective and can be computed with linear order complexity.
Shot Optimization in Quantum Machine Learning Architectures to Accelerate Training

Quantum Machine Learning (QML) has recently emerged as a rapidly growing domain as an intersection of Quantum Computing (QC) and Machine Learning (ML) fields. Hybrid quantum-classical models have demonstrated exponential speedups in various machine learning tasks compared to their classical counterparts. On one hand, training of QML models on real hardware remains a challenge due to long wait queue and the access cost. On the other hand, simulation-based training is not scalable to large QML models due to exponentially growing simulation time. Since the measurement operation converts quantum information to classical binary data, the quantum circuit is executed multiple times (called shots) to obtain the basis state probabilities or qubit expectation values. Higher number of shots worsen the training time of QML models on real hardware and the access cost. Higher number of shots also increase the simulation-based training time. In this paper, we propose shot optimization method for QML models at the expense of minimal impact on model performance. We use classification task as a test case for MNIST and FMNIST datasets using a hybrid quantum-classical QML model. First, we sweep the number of shots for short and full versions of the dataset. We observe that training the full version provides 5-6% higher testing accuracy than short version of dataset with up to 10X higher number of shots for training. Therefore, one can reduce the dataset size to accelerate the training time. Next, we propose adaptive shot allocation on short version dataset to optimize the number of shots over training epochs and evaluate the impact on classification accuracy. We use a (a) linear function where the number of shots reduce linearly with epochs, and (b) step function where the number of shots reduce in step with epochs. We note around 0.01 increase in loss and maximum $\boldsymbol {\sim }4$ % (1%) reduction in testing accuracy for reduction in shots by up to 100X (10X) for linear (step) shot function compared to conventional constant shot function for MNIST dataset, and 0.05 increase in loss and $\boldsymbol {\sim }5$ -7% (5-7%) reduction in testing accuracy with similar reduction in shots using linear (step) shot function on FMNIST dataset. For comparison, we also use the proposed shot optimization methods to perform ground state energy estimation of different molecules and observe that step function gives the best and most stable ground state energy prediction at 1000X less number of shots.
Supermassive black hole wake or bulgeless edge-on galaxy?

van Dokkum et al. (2023) reported the serendipitous discovery of a thin linear object interpreted as the trail of star-forming regions left behind by a runaway supermassive black hole (SMBH) kicked out from the center of a galaxy. Despite the undeniable interest in the idea, the actual physical interpretation is not devoid of difficulty. The wake of a SMBH produces only small perturbations on the external medium, which has to be in exceptional physical conditions to collapse gravitationally and form a long (40 kpc) massive (3e9 Msun) stellar trace in only 39 Myr. Here we offer a more conventional explanation: the stellar trail is a bulgeless galaxy viewed edge-on. This interpretation is supported by the fact that its position--velocity curve resembles a rotation curve which, together with its stellar mass, puts the object right on top of the Tully-Fisher relation characteristic of disk galaxies. Moreover, the rotation curve (Vmax sim 110 km/s), stellar mass, extension, width (z0 sim 1.2 kpc), and surface brightness profile of the object are very much like those of IC5249, a well-known local bulgeless edge-on galaxy. These observational facts are difficult to interpret within the SMBH wake scenario. We discuss in detail the pros and cons of the two options.
Effective detection method for falls according to the distance between two tri-axial accelerometers

Falls and fall-related injuries are a significant problem in the elderly population. A number of different approaches for detecting falls and activities of daily living (ADLs) have been conducted in recent years. However, distinguishing between real falls and certain fall-like ADL is often difficult. The aim of this study is to discriminate falls from fall-like ADLs such as jogging, jumping, and jumping down. The distance between two tri-axial accelerometers attached to the abdomen and the sternum was increased from 10 to 30 cm in 10-cm intervals. Experiments for falls and ADLs were performed to investigate the feasibility of the detection system for falls developed in this study. When the distances between the two tri-axial electrometers were 20 and 30 cm, fall-like ADLs were effectively distinguished from falls. The thresholds for three parameters — SVM, Diff Z, and Sum_diff_Z — were set; falls could be distinguished from ADL action sequences when the SVM value was larger than 4 g (TH1), the Diff_Z parameter was larger than 1.25 g (TH2), and the Sum_diff_Z parameter was larger than 15 m/s (TH3). In particular, when the SVM, Diff_Z, and Sum_diff_Z parameter were sequentially applied to thresholds (TH1, TH2, and TH3), fall-like ADL action sequences were accurately discriminated from falls.
eFAT: Improving the Effectiveness of Fault-Aware Training for Mitigating Permanent Faults in DNN Hardware Accelerators

Fault-Aware Training (FAT) has emerged as a highly effective technique for addressing permanent faults in DNN accelerators, as it offers fault mitigation without significant performance or accuracy loss, specifically at low and moderate fault rates. However, it leads to very high retraining overheads, especially when used for large DNNs designed for complex AI applications. Moreover, as each fabricated chip can have a distinct fault pattern, FAT is required to be performed for each faulty chip individually, considering its unique fault map, which further aggravates the problem. To reduce the overheads of FAT while maintaining its benefits, we propose (1) the concepts of resilience-driven retraining amount selection, and (2) resilience-driven grouping and fusion of multiple fault maps (belonging to different chips) to perform consolidated retraining for a group of faulty chips. To realize these concepts, in this work, we present a novel framework, eFAT, that computes the resilience of a given DNN to faults at different fault rates and with different levels of retraining, and it uses that knowledge to build a resilience map given a user-defined accuracy constraint. Then, it uses the resilience map to compute the amount of retraining required for each chip, considering its unique fault map. Afterward, it performs resilience and reward-driven grouping and fusion of fault maps to further reduce the number of retraining iterations required for tuning the given DNN for the given set of faulty chips. We demonstrate the effectiveness of our framework for a systolic array-based DNN accelerator experiencing permanent faults in the computational array. Our extensive results for numerous chips show that the proposed technique significantly reduces the retraining cost when used for tuning a DNN for multiple faulty chips.
DocParser: End-to-end OCR-free Information Extraction from Visually Rich Documents

Information Extraction from visually rich documents is a challenging task that has gained a lot of attention in recent years due to its importance in several document-control based applications and its widespread commercial value. The majority of the research work conducted on this topic to date follow a two-step pipeline. First, they read the text using an off-the-shelf Optical Character Recognition (OCR) engine, then, they extract the fields of interest from the obtained text. The main drawback of these approaches is their dependence on an external OCR system, which can negatively impact both performance and computational speed. Recent OCR-free methods were proposed to address the previous issues. Inspired by their promising results, we propose in this paper an OCR-free end-to-end information extraction model named DocParser. It differs from prior end-to-end approaches by its ability to better extract discriminative character features. DocParser achieves state-of-the-art results on various datasets, while still being faster than previous works.
Anti-crossings occurrence as exponentially closing gaps in Quantum Annealing

This paper explores the phenomenon of avoided level crossings in quantum annealing, a promising framework for quantum computing that may provide a quantum advantage for certain tasks. Quantum annealing involves letting a quantum system evolve according to the Schr\"odinger equation, with the goal of obtaining the optimal solution to an optimization problem through measurements of the final state. However, the continuous nature of quantum annealing makes analytical analysis challenging, particularly with regard to the instantaneous eigenenergies. The adiabatic theorem provides a theoretical result for the annealing time required to obtain the optimal solution with high probability, which is inversely proportional to the square of the minimum spectral gap. Avoided level crossings can create exponentially closing gaps, which can lead to exponentially long running times for optimization problems. In this paper, we use a perturbative expansion to derive a condition for the occurrence of an avoided level crossing during the annealing process. We then apply this condition to the MaxCut problem on bipartite graphs. We show that no exponentially small gaps arise for regular bipartite graphs, implying that QA can efficiently solve MaxCut in that case. On the other hand, we show that irregularities in the vertex degrees can lead to the satisfaction of the avoided level crossing occurrence condition. We provide numerical evidence to support this theoretical development, and discuss the relation between the presence of exponentially closing gaps and the failure of quantum annealing.