Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hybrid Quantum-Classical Algorithm for Robust Fitting

Fitting geometric models onto outlier contaminated data is provably intractable. Many computer vision systems rely on random sampling heuristics to solve robust fitting, which do not provide optimality guarantees and error bounds. It is therefore critical to develop novel approaches that can bridge the gap between exact solutions that are costly, and fast heuristics that offer no quality assurances. In this paper, we propose a hybrid quantum-classical algorithm for robust fitting. Our core contribution is a novel robust fitting formulation that solves a sequence of integer programs and terminates with a global solution or an error bound. The combinatorial subproblems are amenable to a quantum annealer, which helps to tighten the bound efficiently. While our usage of quantum computing does not surmount the fundamental intractability of robust fitting, by providing error bounds our algorithm is a practical improvement over randomised heuristics. Moreover, our work represents a concrete application of quantum computing in computer vision. We present results obtained using an actual quantum computer (D-Wave Advantage) and via simulation11Source code: https://github.com/dadung/HQC-robust-fitting.
ALMA Observations of Molecular Complexity in the Large Magellanic Cloud: The N 105 Star-forming Region

The Large Magellanic Cloud (LMC) is the nearest laboratory for detailed studies on the formation and survival of complex organic molecules (COMs), including biologically important ones, in low-metallicity environments—typical of earlier cosmological epochs. We report the results of 1.2 mm continuum and molecular line observations of three fields in the star-forming region N 105 with the Atacama Large Millimeter/submillimeter Array. N 105 lies at the western edge of the LMC bar with ongoing star formation traced by H2O, OH, and CH3OH masers, ultracompact H ii regions, and young stellar objects. Based on the spectral line modeling, we estimated rotational temperatures, column densities, and fractional molecular abundances for 12 1.2 mm continuum sources. We identified sources with a range of chemical makeups, including two bona fide hot cores and four hot core candidates. The CH3OH emission is widespread and associated with all the continuum sources. COMs CH3CN and CH3OCH3 are detected toward two hot cores in N 105 together with smaller molecules typically found in Galactic hot cores (e.g., SO2, SO, and HNCO) with the molecular abundances roughly scaling with metallicity. We report a tentative detection of the astrobiologically relevant formamide molecule (NH2CHO) toward one of the hot cores; if confirmed, this would be the first detection of NH2CHO in an extragalactic subsolar metallicity environment. We suggest that metallicity inhomogeneities resulting from the tidal interactions between the LMC and the Small Magellanic Cloud might have led to the observed large variations in COM abundances in LMC hot cores.
A Novel Temporal Attentive-Pooling based Convolutional Recurrent Architecture for Acoustic Signal Enhancement

Removing background noise from acoustic observations to obtain clean signals is an important research topic regarding numerous real acoustic applications. Owing to their strong model capacity in function mapping, deep neural network-based algorithms have been successfully applied in target signal enhancement in acoustic applications. As most target signals carry semantic information encoded in a hierarchal structure in short- and long-term contexts, noise may distort such structures nonuniformly. In most deep neural network-based algorithms, such local and global effects are not explicitly considered in a modeling architecture for signal enhancement. In this article, we propose a temporal attentive pooling (TAP) mechanism combined with a conventional convolutional recurrent neural network (CRNN) model, called TAP-CRNN, which explicitly considers both global and local information for acoustic signal enhancement (ASE). In the TAP-CRNN model, we first use a convolution layer to extract local information from acoustic signals and a recurrent neural network (RNN) architecture to characterize temporal contextual information. Second, we exploit a novel attention mechanism to contextually process salient regions of noisy signals. We evaluate the proposed ASE system using an infant cry dataset. The experimental results confirm the effectiveness of the proposed TAP-CRNN, compared with related deep neural network models, and demonstrate that the proposed TAP-CRNN can more effectively reduce noise components from infant cry signals with unseen background noises at different signal-to-noise levels. We further tested the TAP-CRNN ASE system on a downstream infant cry detection (ICD) system, which determines whether a sound segment is involved in an infant cry event. Experimental results show that TAP-CRNN ASE can effectively reduce the noise components, thereby improving the performance of ICD under noisy conditions.
Link Prediction with Contextualized Self-Supervision

Link prediction aims to infer the link existence between pairs of nodes in networks/graphs. Despite their wide application, the success of traditional link prediction algorithms is hindered by three major challenges—link sparsity, node attribute noise and dynamic changes—that are faced by many real-world networks. To address these challenges, we propose a Contextualized Self-Supervised Learning (CSSL) framework that fully exploits structural context prediction for link prediction. The proposed CSSL framework learns a link encoder to infer the link existence probability from paired node embeddings, which are constructed via a transformation on node attributes. To generate informative node embeddings for link prediction, structural context prediction is leveraged as a self-supervised learning task to boost the link prediction performance. Two types of structural context are investigated, i.e., context nodes collected from random walks versus context subgraphs. The CSSL framework can be trained in an end-to-end manner, with the learning of model parameters supervised by both the link prediction and self-supervised learning tasks. The proposed CSSL is a generic and flexible framework in the sense that it can handle both attributed and non-attributed networks, and operate under both transductive and inductive link prediction settings. Extensive experiments and ablation studies on seven real-world benchmark networks demonstrate the superior performance of the proposed self-supervision based link prediction algorithm over state-of-the-art baselines, on different types of networks under both transductive and inductive settings. The proposed CSSL also yields competitive performance in terms of its robustness to node attribute noise and scalability over large-scale networks.
A Novel Method of Time-frequency Analysis for Frequency Hopping Signal

Nonlinear time-frequency analysis methods such as Wigner-Ville transform can't resolve the cross-term jam problem of frequency hopping signals.This paper proposes a novel method to improve the performance of time-frequency analysis about the frequency hopping signal.Firstly,we divide the frequency hopping signals into several single different carrier frequency signals based on singular locations after detecting singularity points.Secondly,each single carrier frequency signal is analyzed by the nonlinear time-frequency analysis method.Finally,we assemble the time-frequency relationships among the single carrier frequency signals based on frequency hopping rule,therefore,the integrated time-frequency relationship of the frequency hopping signals can be obtained.Computer simulation shows that the proposed method can restrain the cross-term jam effectively.
Rank-adaptive time integration of tree tensor networks

A rank-adaptive integrator for the approximate solution of high-order tensor differential equations by tree tensor networks is proposed and analyzed. In a recursion from the leaves to the root, the integrator updates bases and then evolves connection tensors by a Galerkin method in the augmented subspace spanned by the new and old bases. This is followed by rank truncation within a specified error tolerance. The memory requirements are linear in the order of the tensor and linear in the maximal mode dimension. The integrator is robust to small singular values of matricizations of the connection tensors. Up to the rank truncation error, which is controlled by the given error tolerance, the integrator preserves norm and energy for Schrodinger equations, and it dissipates the energy in gradient systems. Numerical experiments with a basic quantum spin system illustrate the behavior of the proposed algorithm.
Effect of hybrid FES exercise on body composition during the sub-acute phase of spinal cord injury

Objectives To determine the Effect of Hybrid functional electrically stimulated (FES) Exercise on Body Composition during the Sub-acute Phase of Spinal Cord Injury (SCI). Design Randomized Clinical Trial. Setting Rehabilitation Hospital. Participants Patients within sub-acute phase (3–24 months) of SCI. Interventions We investigated if high-intensity exercise training via the addition of functional electrically stimulated (FES) leg muscles, provides sufficient stimulus to mitigate against body composition changes in the sub-acute phase after SCI. Main outcome measures We explored potential effects of FES row training (FESRT) on body fat gain, lean mass loss, and cardiometabolic parameters and compared the effects of 6-month of FESRT (n = 18) to standard of care (SOC, n = 13). Those in SOC were crossed over to FESRT. Results FESRT resulted in greater exercise capacity and a tendency for lesser total body fat accumulation with a significant increase in total and leg lean mass (p<0.05). In addition pelvis and total bone mineral density declines were significantly less (p<0.05). Compared to SOC, FESRT did not lead to any significant difference in insulin sensitivity or serum lipids. However, HbA1C levels were significantly decreased in SOC participants who crossed over to 6-month FESRT. Conclusion FESRT early after SCI provides a sufficient stimulus to mitigate against detrimental body composition changes. This may lead to prevention of losses in lean mass, including bone.
Personalized endoprostheses for the proximal humerus and scapulohumeral joint in dogs: Biomechanical study of the muscles’ contributions during locomotion

Osteosarcoma represents one of the most common bone tumours in dogs. It commonly occurs in the proximal humerus, the most affected anatomic site. Until recently, amputation or limb-sparing surgery leading to an arthrodesis coupled with chemotherapy were the only available treatments, but they often lead to complications, reduced mobility and highly impact dog’s quality of life. Prototypes of both articulated and monobloc (no mobility) patient-specific endoprostheses have been designed to spare the limb afflicted with osteosarcoma of the proximal humerus. This study focuses on the biomechanical effects of endoprostheses and shoulder muscle kinematics. For each of the endoprosthesis designs, a minimal number of muscles needed to ensure stability and a certain degree of joint movement during walking is sought. A quasi-static study based on an optimization method, the minimization of the sum of maximal muscle stresses, was carried out to assess the contribution of each muscle to the shoulder function. The identification of the most important muscles and their impact on the kinematics of the prosthetic joint lead to an improvement of the endoprosthesis design relevance and implantation feasibility.
Recent Progress in Nanobiosensors for Precise Detection of Blood Glucose Level

Diabetes mellitus (DM) follows a series of metabolic diseases categorized by high blood sugar levels. Owing to the increasing diabetes disease in the world, early diagnosis of this disease is critical. New methods such as nanotechnology have made significant progress in many areas of medical science and physiology. Nanobiosensors are very sensible and can identify single virus particles or even low concentrations of a material that can be inherently harmful. One of the main factors for developing glucose sensors in the body is the diagnosis of hypoglycemia in individuals with insulin-dependent diabetes. Therefore, this study aimed to evaluate the most up-to-date and fastest glucose detection method by nanosensors and, as a result, faster and better treatment in medical sciences. In this review, we try to explore new ways to control blood glucose levels and treat diabetes. We begin with a definition of biosensors and their classification and basis, and then we examine the latest biosensors in glucose detection and new biosensors applications, including the artificial pancreas and updating quantum graphene data.
Energy Efficiency and Reliability Considerations in Wireless Body Area Networks: A Survey

In this paper, we have reviewed and presented a critical overview of “energy-efficient and reliable routing solutions” in the field of wireless body area networks (WBANs). In addition, we have theoretically analysed the importance of energy efficiency and reliability and how it affects the stability and lifetime of WBANs. WBAN is a type of wireless sensor network (WSN) that is unique, wherever energy-efficient operations are one of the prime challenges, because each sensor node operates on battery, and where an excessive amount of communication consumes more energy than perceiving. Moreover, timely and reliable data delivery is essential in all WBAN applications. Moreover, the most frequent types of energy-efficient routing protocols include crosslayer, thermal-aware, cluster-based, quality-of-service, and postural movement-based routing protocols. According to the literature review, clustering-based routing algorithms are the best choice for WBAhinwidth, and low memory WBAN, in terms of more computational overhead and complexity. Thus, the routing techniques used in WBAN should be capable of energy-efficient communication at desired reliability to ensure the improved stability period and network lifetime. Therefore, we have highlighted and critically analysed various performance issues of the existing “energy-efficient and reliable routing solutions” for WBANs. Furthermore, we identified and compiled a tabular representation of the reviewed solutions based on the most appropriate strategy and performance parameters for WBAN. Finally, concerning to reliability and energy efficiency in WBANs, we outlined a number of issues and challenges that needs further consideration while devising new solutions for clustered-based WBANs.
Application of DNA-Binding Protein Prediction Based on Graph Convolutional Network and Contact Map

DNA contains the genetic information for the synthesis of proteins and RNA, and it is an indispensable substance in living organisms. DNA-binding proteins are an enzyme, which can bind with DNA to produce complex proteins, and play an important role in the functions of a variety of biological molecules. With the continuous development of deep learning, the introduction of deep learning into DNA-binding proteins for prediction is conducive to improving the speed and accuracy of DNA-binding protein recognition. In this study, the features and structures of proteins were used to obtain their representations through graph convolutional networks. A protein prediction model based on graph convolutional network and contact map was proposed. The method had some advantages by testing various indexes of PDB14189 and PDB2272 on the benchmark dataset.
Coverage-guided differential testing of TLS implementations based on syntax mutation

Transport layer security (TLS) protocol is the most widely used security protocol in modern network communications. However, protocol vulnerabilities caused by the design of the network protocol or its implementation by programmers emerge one after another. Meanwhile, various versions of TLS protocol implementations exhibit different behavioral characteristics. Researchers are attempting to find the differences in protocol implementations based on differential testing, which is conducive to discovering the vulnerabilities. This paper provides a solution to find the differences more efficiently by targeting the TLS protocol handshake process. The differences of different implementations during the fuzzing process, such as code coverage and response data, are taken to guide the mutation of test cases, and the seeds are mutated based on the TLS protocol syntax. In addition, the definition of duplicate discrepancies is theoretically explored to investigate the root cause of the discrepancies and to reduce the number of duplicate cases that are caused by the same reason. Besides, the necessary conditions for excluding duplicate cases are further analyzed to develop the deduplication strategy. The proposed method is developed based on open-source tools, i.e., NEZHA and TLS-diff. Three types of widely used TLS protocol implementations, i.e., OpenSSL, BoringSSL, and LibreSSL, are taken for experimental testing. The experimental results show that the proposed method can effectively improve the ability to find differences between different implementations. Under the same test scale or the same time, the amount of discrepancies increases by about 20% compared to TLS-diff, indicating the effectiveness of the deduplication strategy.
Molecular perturbations in pulmonary tuberculosis patients identified by pathway-level analysis of plasma metabolic features

Insight into the metabolic biosignature of tuberculosis (TB) may inform clinical care, reduce adverse effects, and facilitate metabolism-informed therapeutic development. However, studies often yield inconsistent findings regarding the metabolic profiles of TB. Herein, we conducted an untargeted metabolomics study using plasma from 63 Korean TB patients and 50 controls. Metabolic features were integrated with the data of another cohort from China (35 TB patients and 35 controls) for a global functional meta-analysis. Specifically, all features were matched to a known biological network to identify potential endogenous metabolites. Next, a pathway-level gene set enrichment analysis-based analysis was conducted for each study and the resulting p-values from the pathways of two studies were combined. The meta-analysis revealed both known metabolic alterations and novel processes. For instance, retinol metabolism and cholecalciferol metabolism, which are associated with TB risk and outcome, were altered in plasma from TB patients; proinflammatory lipid mediators were significantly enriched. Furthermore, metabolic processes linked to the innate immune responses and possible interactions between the host and the bacillus showed altered signals. In conclusion, our proof-of-concept study indicated that a pathway-level meta-analysis directly from metabolic features enables accurate interpretation of TB molecular profiles.
Improved strong lensing modelling of galaxy clusters using the Fundamental Plane: Detailed mapping of the baryonic and dark matter mass distribution of Abell S1063

Aims. From accurate photometric and spectroscopic information, we build the Fundamental Plane (FP) relation for the early-type galaxies of the cluster Abell S1063. We use this relation to develop an improved strong lensing model of the cluster, and we decompose the cluster’s cumulative projected total mass proﬁle into its stellar, hot gas, and dark matter mass components. We compare our results with the predictions of cosmological simulations. Methods. We calibrate the FP using Hubble Frontier Fields photometry and data from the Multi Unit Spectroscopic Explorer on the Very Large Telescope. The FP allows us to determine the velocity dispersions of all 222 cluster members included in the model from their measured structural parameters. As for their truncation radii, we test a proportionality relation with the observed half-light radii. Fixing the mass contribution of the hot gas component from X-ray data, the mass density distributions of the di ﬀ use dark matter haloes are optimised by comparing the observed and model-predicted positions of 55 multiple images of 20 background sources distributed over the redshift range 0 . 73 − 6 . 11. We determine the uncertainties on the model parameters with Monte Carlo Markov chains. Results. We ﬁnd that the most accurate predictions of the positions of the multiple images are obtained when the truncation radii of the member galaxies are approximately 2.3 times their e ﬀ ective radii. Compared to earlier work on the same cluster, our model allows for the inclusion of some scatter on the relation between the total mass and the velocity dispersion of the cluster members. We notice a lower statistical uncertainty on the value of some model parameters. For instance, the main dark matter halo of the cluster has a core radius of 86 ± 2kpc: the uncertainty on this value decreases by more than 30% with respect to previous work. Taking advantage of a new estimate of the stellar mass of all cluster members from the HST multi-band data, we measure the cumulative two-dimensional mass proﬁles out to a radius of 350kpc for all baryonic and dark matter components of the cluster. At the outermost radius of 350kpc, we obtain a baryon fraction of 0 . 147 ± 0 . 002. We study the stellar-to-total mass fraction of the high-mass cluster members in our model, ﬁnding good agreement with the observations of wide galaxy surveys and some disagreement with the predictions of halo occupation distribution studies based on N -body simulations. Finally, we compare the features of the sub-haloes as described by our model with those predicted by high-resolution hydrodynamical simulations. We obtain compatible results in terms of the stellar over total mass fraction. On the other hand, we report some discrepancies both in terms of the maximum circular velocity, which is an indication of the halo compactness, and the sub-halo total mass function in the central cluster regions.
Real-time Trajectory Generation for Collision Avoidance with Obstacle Uncertainty

This paper first presents the integration of trajectory generation into an autonomous collision avoidance system and then proposes a systematic update scheme for trajectory generation so that real-time feedback can be obtained to compensate for obstacle uncertainty. Driven by the promise of optimization-based methods for collision avoidance, the real-time update scheme attempts to alleviate two of the well-known problems that are intrinsic to optimization-based methods: namely no guarantee for convergence and dependence on predictions (models). Both the not-converged solutions and prediction uncertainty may endanger the host aircraft by leading it to a possibly infeasible trajectory. This paper demonstrates that the proposed approach is able to accommodate some degree of uncertainty by utilizing real-time feedback and to handle not-converged situations by appending an onboard backup trajectory to the avoidance trajectory at every update step. These are illustrated with two examples and the effects of obstacle uncertainty and not-converged solutions on the vehicle safety are discussed.
Blind super-resolution of point sources via fast iterative hard thresholding

In this work, we develop a provable fast algorithm for blind super-resolution based on the low rank structure of vectorized Hankel matrix associated with the target matrix. Theoretical results show that the proposed method converges to the ground truth with linear convergence rate. Numerical experiments are also conducted to illustrate the linear convergence and effectiveness of the proposed approach.