Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum Algorithm for Fidelity Estimation

For two unknown mixed quantum states <inline-formula> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula> in an <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>-dimensional Hilbert space, computing their fidelity <inline-formula> <tex-math notation="LaTeX">$F(\rho,\sigma)$ </tex-math></inline-formula> is a basic problem with many important applications in quantum computing and quantum information, for example verification and characterization of the outputs of a quantum computer, and design and analysis of quantum algorithms. In this paper, we propose a quantum algorithm that solves this problem in <inline-formula> <tex-math notation="LaTeX">${\mathrm{ poly}}(\log (N), r, 1/\varepsilon)$ </tex-math></inline-formula> time, where <inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> is the lower rank of <inline-formula> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> is the desired precision, provided that the purifications of <inline-formula> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula> are prepared by quantum oracles. This algorithm exhibits an exponential speedup over the best known algorithm (based on quantum state tomography) which has time complexity polynomial in <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>.
Coordinate Constructions in English Enhanced Universal Dependencies: Analysis and Computational Modeling

In this paper, we address the representation of coordinate constructions in Enhanced Universal Dependencies (UD), where relevant dependency links are propagated from conjunction heads to other conjuncts. English treebanks for enhanced UD have been created from gold basic dependencies using a heuristic rule-based converter, which propagates only core arguments. With the aim of determining which set of links should be propagated from a semantic perspective, we create a large-scale dataset of manually edited syntax graphs. We identify several systematic errors in the original data, and propose to also propagate adjuncts. We observe high inter-annotator agreement for this semantic annotation task. Using our new manually verified dataset, we perform the first principled comparison of rule-based and (partially novel) machine-learning based methods for conjunction propagation for English. We show that learning propagation rules is more effective than hand-designing heuristic rules. When using automatic parses, our neural graph-parser based edge predictor outperforms the currently predominant pipelines using a basic-layer tree parser plus converters.
Sparse Curriculum Reinforcement Learning for End-to-End Driving

—Deep reinforcement Learning for end-to-end driving is limited by the need of complex reward engineering. Sparse rewards can circumvent this challenge but suffers from long training time and leads to sub-optimal policy. In this work, we explore driving using only goal conditioned sparse rewards and propose a curriculum learning approach for end to end driving using only navigation view maps that beneﬁt from small virtual-to-real domain gap. To address the complexity of multiple driving policies, we learn concurrent individual policies which are selected at inference by a navigation system. We demonstrate the ability of our proposal to generalize on unseen road layout, and to drive longer than in the training.
Vertical Beamforming in Reconfigurable Intelligent Surface-aided Cognitive Radio Networks

In this letter, we investigate joint application of reconfigurable intelligent surface (RIS) and vertical beamforming in cognitive radio networks (CRN). After properly modeling the network, an optimization problem is formed to jointly design the beamforming vector and tilt angle at the secondary base station (BS) as well as the phase shifts at the RIS with the objective of maximizing spectral efficiency of the secondary network. The optimization problem is non-convex; thus, we propose an efficient solution method for it. Numerical results show that adding a RIS and optimizing the radiation orientation, can significantly improve performance of the CRNs.
S-band single-longitudinal-mode erbium-doped fiber ring laser with ultra-narrow linewidth, ultra-high OSNR, high stability and low RIN

A high-performance S-band single-longitudinal-mode (SLM) erbium-doped fiber (EDF) ring cavity laser based on a depressed cladding EDF is investigated and experimentally demonstrated. We combine a double-ring passive resonator (DR-PR) and a length of unpumped polarization maintaining (PM) EDF in the laser cavity to achieve the SLM lasing without mode hopping. The DR-PR, composed of two efficient dual-coupler fiber rings, is utilized to expand the free spectral range of the EDF ring cavity laser and to eliminate the dense longitudinal modes greatly. The PM EDF, insusceptible to random change induced by environmental perturbations, is used as a saturable absorber filter to guarantee and to stabilize the SLM operation of the EDF ring cavity laser. At the pump power of 400 mW, we obtain an SLM EDF ring laser with a linewidth as narrow as 568 Hz, an optical signal-to-noise ratio as high as 77 dB, and a relative intensity noise as low as 140 dB/Hz at the frequency over 5 MHz. Meanwhile, the stability performance of both the wavelength lasing and the output power, the dependence of the OSNR and the output power on pump power for the Sband fiber laser are also investigated in detail. keywords: S-band, single longitudinal mode (SLM), erbium-doped fiber (EDF), fiber ring laser. Corresponding author Email addresses: qiao@bupt.edu. cn (Yaojun Qiao)
A Multilayer Network Model implementation for Covid-19

We present a numerical implementation for a multilayer network used to model the transmission of Covid-19 or other diseases with a similar transmission mechanism. The model incorporates different contact types between individuals (household, social contacts, and strangers), which allows flexibility compared to standard SIR type models. The algorithm described in this paper is a simplification of the model used to give public health authorities an additional tool for the decision-making process in Costa Rica, by simulating extensive possible scenarios and projections.
Unsupervised Anomaly Segmentation using Image-Semantic Cycle Translation

The goal of unsupervised anomaly segmentation (UAS) is to detect the pixel-level anomalies unseen during training. It is a promising field in the medical imaging community, e.g, we can use the model trained with only healthy data to segment the lesions of rare diseases. Existing methods are mainly based on Information Bottleneck, whose underlying principle is modeling the distribution of normal anatomy via learning to compress and recover the healthy data with a low-dimensional manifold, and then detecting lesions as the outlier from this learned distribution. However, this dimensionality reduction inevitably damages the localization information, which is especially essential for pixel-level anomaly detection. In this paper, to alleviate this issue, we introduce the semantic space of healthy anatomy in the process of modeling healthy-data distribution. More precisely, we view the couple of segmentation and synthesis as a special Autoencoder, and propose a novel cycle translation framework with a journey of 'image->semantic->image'. Experimental results on the BraTS and ISLES databases show that the proposed approach achieves significantly superior performance compared to several prior methods and segments the anomalies more accurately.
Robustly Optimized and Distilled Training for Natural Language Understanding

In this paper, we explore multi-task learning (MTL) as a second pretraining step to learn enhanced universal language representation for transformer language models. We use the MTL enhanced representation across several natural language understanding tasks to improve performance and generalization. Moreover, we incorporate knowledge distillation (KD) in MTL to further boost performance and devise a KD variant that learns effectively from multiple teachers. By combining MTL and KD, we propose Robustly Optimized and Distilled (ROaD) modeling framework. We use ROaD together with the ELECTRA model to obtain state-of-the-art results for machine reading comprehension and natural language inference.
The Tumor Proteolytic Landscape: A Challenging Frontier in Cancer Diagnosis and Therapy

In recent decades, dysregulation of proteases and atypical proteolysis have become increasingly recognized as important hallmarks of cancer, driving community-wide efforts to explore the proteolytic landscape of oncologic disease. With more than 100 proteases currently associated with different aspects of cancer development and progression, there is a clear impetus to harness their potential in the context of oncology. Advances in the protease field have yielded technologies enabling sensitive protease detection in various settings, paving the way towards diagnostic profiling of disease-related protease activity patterns. Methods including activity-based probes and substrates, antibodies, and various nanosystems that generate reporter signals, i.e., for PET or MRI, after interaction with the target protease have shown potential for clinical translation. Nevertheless, these technologies are costly, not easily multiplexed, and require advanced imaging technologies. While the current clinical applications of protease-responsive technologies in oncologic settings are still limited, emerging technologies and protease sensors are poised to enable comprehensive exploration of the tumor proteolytic landscape as a diagnostic and therapeutic frontier. This review aims to give an overview of the most relevant classes of proteases as indicators for tumor diagnosis, current approaches to detect and monitor their activity in vivo, and associated therapeutic applications.
FAM83A and FAM83A-AS1 both play oncogenic roles in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer. Nevertheless, the detailed molecular mechanisms of the progression of LUAD remain largely unknown. The present bioinformatics analysis reported that FAM83A and FAM83A-AS1 were upregulated in LUAD tissues and associated with prognosis in patients with LUAD. The purpose of the current study was to investigate the role of FAM83A and its antisense long non-coding (lnc)RNA FAM83A-AS1 in LUAD. Gene Expression Profiling Interactive Analysis was used to screen for potential oncogenes in LUAD and to analyze the clinical significance of FAM83A and FAM83A-AS1. Small interfering RNAs were constructed and transfected into LUAD cells to knock down the expression of FAM83A and FAM83A-AS1. EdU, Cell Counting Kit-8, Transwell and Matrigel assays were performed to detect the proliferation, migration and invasion of LUAD cells. The interaction between FAM83A-AS1, microRNA (miR)-495-3p and FAM83A was explored using a luciferase reporter assay. FAM83A and FAM83A-AS1 were both overexpressed in LUAD tissues compared with adjacent normal tissues. High expression of FAM83A and FAM83A-AS1 predicted worse survival and more advanced clinical stage. Knockdown of FAM83A or FAM83A-AS1 could inhibit the proliferation, migration and invasion of LUAD cells. Moreover, lncRNA FAM83A-AS1 regulated the expression of FAM83A by functioning as competing endogenous RNA for miR-495-3p. These results implicated that FAM83A and FAM83A-AS1 both played oncogenic roles in LUAD and FAM83A-AS1 could regulate the expression of FAM83A by sponging miR-495-3p. The study revealed a novel regulatory mechanism of tumor development in LUAD and FAM83A and FAM83A-AS1 may be novel biomarkers and therapeutic targets for LUAD.
Short (6 mm) and Regular Dental Implants in the Posterior Maxilla–7-Years Follow-up Study

Short 6 mm dental implants are considered as an alternative to the maxillary sinus elevation and bone augmentation procedure where there is a reduced alveolar ridge height. The aim of this study was to compare the implant survival rate between short dental implants (6 mm) and regular length implants (11–13 mm) when placed in combination with bone grafting and loaded with a single non splinted crown, seven years after placing the implant. It was conducted as a controlled clinical study of 30 patients with partial edentulism in the posterior maxilla. The protocol included radiological and clinical evaluation of the C/I ratio (length of the superstructure divided by the length of the implant crestal part), marginal bone level (MBL), ultrasonography measurement of soft tissue surrounding implant (STT), patient-reported outcomes, and biological and technical complications. A total number of 28 implants (93%) remained integrated during follow-up period. MBL of 0.50 and 0.52 mm was observed for short implants and regular implants, respectively. MBL was checked for correlation with STT, and a negative correlation was found between MBL: STT. Our study has demonstrated a significantly lower implant survival rate for short implants compared to regular implants (87% compared to 100%). Despite the loss of several implants, good clinical results were achieved in the remaining implants in both groups. It is, therefore, worth considering short implants as an alternative to regular implants with a sinus lift surgery.
Decoration of Fe3O4 magnetic nanoparticles on graphene oxide nanosheets

This study focuses on covalent grafting of Fe3O4 magnetic nanoparticles (MNPs) to graphene oxide nanosheets (GNOS) via three different chemical routes. To this purpose, firstly, dopamine-functionalized Fe3O4 (Fe3O4/DA) nanoparticles were firmly attached onto the surface of GONS by activation of carboxylic groups of GONS via thionyl chloride (SOCl2), anhydride formation by using trifluoro acetic acid (TFAA) and 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide/N-hydroxysuccinimide (EDC/NHS) to form Fe3O4/DA/GONS. The attachment of Fe3O4 MNPs on the GONS surface was evaluated and compared by using FT-IR, XRD, TEM, TGA, VSM, TEM and SAED techniques. The results showed that, depending on the route used, the density and immobilization site of Fe3O4 MNPs can be different.
Niveles de plomo, zinc, cadmio y cobre en el Rio Almendares, Ciudad Habana, Cuba

The objective of this study was to assess the levels of Pb, Cu, Zn and Cd in Almendares River (Havana City, Cuba). Fifteen sampling stations were located along the river. The concentrations of these metals were determined in the sediments and the roots of Eichhornia crassipes during the dry season of 2003 and 2004. Concentrations of Pb between 38.7 and 217.5 mg/kg, of Cd between Eichhornia crassipes roots and in the sediments.
Effect of domain knowledge encoding in CNN model architecture—a prostate cancer study using mpMRI images

Background Prostate cancer is one of the most common cancers worldwide. Currently, convolution neural networks (CNNs) are achieving remarkable success in various computer vision tasks, and in medical imaging research. Various CNN architectures and methodologies have been applied in the field of prostate cancer diagnosis. In this work, we evaluate the impact of the adaptation of a state-of-the-art CNN architecture on domain knowledge related to problems in the diagnosis of prostate cancer. The architecture of the final CNN model was optimised on the basis of the Prostate Imaging Reporting and Data System (PI-RADS) standard, which is currently the best available indicator in the acquisition, interpretation, and reporting of prostate multi-parametric magnetic resonance imaging (mpMRI) examinations. Methods A dataset containing 330 suspicious findings identified using mpMRI was used. Two CNN models were subjected to comparative analysis. Both implement the concept of decision-level fusion for mpMRI data, providing a separate network for each multi-parametric series. The first model implements a simple fusion of multi-parametric features to formulate the final decision. The architecture of the second model reflects the diagnostic pathway of PI-RADS methodology, using information about a lesion’s primary anatomic location within the prostate gland. Both networks were experimentally tuned to successfully classify prostate cancer changes. Results The optimised knowledge-encoded model achieved slightly better classification results compared with the traditional model architecture (AUC = 0.84 vs. AUC = 0.82). We found the proposed model to achieve convergence significantly faster. Conclusions The final knowledge-encoded CNN model provided more stable learning performance and faster convergence to optimal diagnostic accuracy. The results fail to demonstrate that PI-RADS-based modelling of CNN architecture can significantly improve performance of prostate cancer recognition using mpMRI.
Disposition Decision Support by Laboratory Based Outcome Prediction

Disposition is one of the main tasks in the emergency department. However, there is a lack of objective and reliable disposition criteria, and diagnosis-based risk prediction is not feasible at early time points. The aim was to derive a risk score (TRIAL) based on routinely collected baseline (TRIage level and Age) and Laboratory data—supporting disposition decisions by risk stratification based on mortality. We prospectively included consecutive patients presenting to the emergency department over 18 weeks. Data sets of routinely collected baseline (triage level and age) and laboratory data were used for multivariable logistic regression to develop the TRIAL risk score predicting mortality. Routine laboratory variables and disposition cut-offs were chosen beforehand by expert consensus. Risk stratification was based on low risk (<1%), intermediate risk (1–10%), and high risk (>10%) of in-hospital mortality. In total, 8687 data sets were analyzed. Variables identified to develop the TRIAL risk score were triage level (Emergency Severity Index), age, lactate dehydrogenase, creatinine, albumin, bilirubin, and leukocyte count. The area under the ROC curve for in-hospital mortality was 0.93. Stratification according to the TRIAL score showed that 67.5% of all patients were in the low-risk category. Mortality was 0.1% in low-risk, 3.5% in intermediate-risk, and 26.2% in high-risk patients. The TRIAL risk score based on routinely available baseline and laboratory data provides prognostic information for disposition decisions. TRIAL could be used to minimize admission in low-risk and to maximize observation in high-risk patients.
Sinus computed tomography findings in patients with COVID-19

ABSTRACT Objective: To analyze computed tomography scans of paranasal sinuses of a series of patients with coronavirus disease 2019, and correlate the findings with the disease. Methods: Computed tomography scans of 95 adult patients who underwent a polymerase chain reaction test for severe acute respiratory syndrome coronavirus 2 were analyzed. Clinical data were obtained from patients’ records and telephone calls. Paranasal sinus opacification was graded and compared according to severe acute respiratory syndrome coronavirus 2 positivity. Results: Of the patients 28 (29.5%) tested positive for severe acute respiratory syndrome coronavirus 2 (median age 52 [range 26-95] years) and 67 were negative (median age 50 [range 18-95] years). Mucosal thickening was present in 97.4% of maxillary sinuses, 80% of anterior ethmoid air cells, 75.3% of posterior ethmoid air cells, 74.7% of frontal sinuses, and 66.3% of sphenoid sinuses. Minimal or mild mucosal thickening (score 1)and normally aerated sinuses (score 0) corresponded to 71.4% and 21.3% of all paranasal sinuses, respectively. The mean score of each paranasal sinus among severe acute respiratory syndrome coronavirus 2 positive and negative patients was 0.85±0.27 and 0.87±0.38, respectively (p=0.74). Median paranasal sinus opacification score among severe acute respiratory syndrome coronavirus 2 positive patients was 9 (interquartile range 8-10) compared to 9 (interquartile range 5-10) in negative patients (p=0.89). There was no difference in mean score adjusted for age and sex. Nasal congestion was more frequent in severe acute respiratory syndrome coronavirus 2 positive than negative patients (p=0.05). Conclusion: Severe acute respiratory syndrome coronavirus 2 infection was associated with patient recall of nasal congestion, but showed no correlation with opacification of paranasal sinuses.
Novel Mutation m.10372A>G in MT-ND3 Causing Sensorimotor Axonal Polyneuropathy

Objective To investigate the pathogenicity of a novel MT-ND3 mutation identified in a patient with adult-onset sensorimotor axonal polyneuropathy and report the clinical, morphologic, and biochemical findings. Methods Clinical assessments and morphologic and biochemical investigations of skeletal muscle and cultured myoblasts from the patient were performed. Whole-genome sequencing (WGS) of DNA from skeletal muscle and Sanger sequencing of mitochondrial DNA (mtDNA) from both skeletal muscle and cultured myoblasts were performed. Heteroplasmic levels of mutated mtDNA in different tissues were quantified by last-cycle hot PCR. Results Muscle showed ragged red fibers, paracrystalline inclusions, a significant reduction in complex I (CI) respiratory chain (RC) activity, and decreased adenosine triphosphate (ATP) production for all substrates used by CI. Sanger sequencing of DNA from skeletal muscle detected a unique previously unreported heteroplasmic mutation in mtDNA encoded MT-ND3, coding for a subunit in CI. WGS confirmed the mtDNA mutation but did not detect any other mutation explaining the disease. Cultured myoblasts, however, did not carry the mutation, and RC activity measurements in myoblasts were normal. Conclusions We report a case with adult-onset sensorimotor axonal polyneuropathy caused by a novel mtDNA mutation in MT-ND3. Loss of heteroplasmy in blood, cultured fibroblasts and myoblasts from the patient, and normal measurement of RC activity of the myoblasts support pathogenicity of the mutation. These findings highlight the importance of mitochondrial investigations in patients presenting with seemingly idiopathic polyneuropathy, especially if muscle also is affected.
Locomotion with Pedestrian Aware from Perception Sensor by Pavement Sweeping Reconfigurable Robot

Regular washing of public pavements is necessary to ensure that the public environment is sanitary for social activities. This is a challenge for autonomous cleaning robots, as they must adapt to the environment with varying pavement widths while avoiding pedestrians. A self-reconfigurable pavement sweeping robot, named Panthera, has the mechanisms to perform reconfiguration in width to enable smooth cleaning operations, and it changes its behavior based on environment dynamics of moving pedestrians and changing pavement widths. Reconfiguration in the robot’s width is possible, due to the scissor mechanism at the core of the robot’s body, which is driven by a lead screw motor. Panthera will perform locomotion and reconfiguration based on perception sensors feedback control proposed while using an Red Green Blue-D (RGB-D) camera. The proposed control scheme involves publishing robot kinematic parameters for reconfiguration during locomotion. Experiments were conducted in outdoor pavements to demonstrate the autonomous reconfiguration during locomotion to avoid pedestrians while complying with varying pavements widths in a real-world scenario.