Miracle-dz/UniFAR-FTUs huggingface dataset:
Network induced large covariance matrix estimation

In this paper, we consider to estimate network/community induced large covariance matrices. The massive biomedical data (e.g. gene expression or neuroimaging data) often include networks where features are correlated with each other, and thus the covariance matrix in a complex yet organized topology. Although, current large covariance matrix and precision matrix estimation methods using thresholding or shrinkage strategies could provide satisfactory overall covariance/precision matrix estimation, they are limited for automatic network/topology detection and network induced covariance matrix estimation. To fill the gap, we propose a new network induced covariance estimation method (NICE) to simultaneously detect highly correlated networks and estimate the covariance matrix by leveraging an adaptive and graph topology oriented thresholding strategy. The novel thresholding strategy can reduce both false positive and false negative discovery rates by using the whole graph topological information which allows edges to borrow with each other. Moreover, we propose a novel `quality and quantity' objective function to shrink the covariance matrix towards a parsimonious model while retaining most of the information. Simulation study results show that our method outperform the competing thresholding and shrinkage methods. We further illustrate the application of our new method by analysis of a serum mass spectrometry proteomics data set.
SAN-T2T: An automated table-to-text generator based on selective attention network

Table-to-text generation aims to generate descriptions for structured data (i.e., tables) and has been applied in many fields like question-answering systems and search engines. Current approaches mostly use neural language models to learn alignment between output and input based on the attention mechanisms, which are still flawed by the gradual weakening of attention when processing long texts and the inability to utilize the records’ structural information. To solve these problems, we propose a novel generative model SAN-T2T, which consists of a field-content selective encoder and a descriptive decoder, connected with a selective attention network. In the encoding phase, the table’s structure is integrated into its field representation, and a content selector with self-aligned gates is applied to take advantage of the fact that different records can determine each other’s importance. In the decoding phase, the content selector’s semantic information enhances the alignment between description and records, and a featured copy mechanism is applied to solve the rare word problem. Experiments on WikiBio and WeatherGov datasets show that SAN-T2T outperforms the baselines by a large margin, and the content selector indeed improves the model’s performance.
SyncNet: Correlating Objective for Time Delay Estimation in Audio Signals

This study addresses the task of performing robust and reliable time-delay estimation in signals in noisy and reverberating environments. In contrast to the popular signal processing based methods, this paper proposes to transform the input signals using a deep neural network into another pair of sequences which show high cross correlation at the actual time delay. This is achieved with the help of a novel correlation function based objective function for training the network. The proposed approach is also intrinsically interpretable as it does not lose temporal information. Experimental evaluations are performed for estimating mutual time delays for different types of audio signals such as pulse, speech and musical beats. SyncNet out-performs other classical approaches, such as GCC-PHAT, and some other learning based approaches.
Deep3DSketch: 3D Modeling from Free-Hand Sketches with View- and Structural-Aware Adversarial Training

This work aims to investigate the problem of 3D modeling using single free-hand sketches, which is one of the most natural ways we humans express ideas. Although sketch-based 3D modeling can drastically make the 3D modeling process more accessible, the sparsity and ambiguity of sketches bring significant challenges for creating high-fidelity 3D models that reflect the creators’ ideas. In this work, we propose a view-and structural-aware deep learning approach, Deep3DSketch, which tackles the ambiguity and fully uses sparse information of sketches, emphasizing the structural information. Specifically, we introduced random pose sampling on both 3D shapes and 2D silhouettes, and an adversarial training scheme with an effective progressive discriminator to facilitate learning of the shape structures. Extensive experiments demonstrated the effectiveness of our approach, which outperforms existing methods – with state-of-the-art (SOTA) performance on both synthetic and real datasets.
Distributed Gaussian Process Hyperparameter Optimization for Multi-Agent Systems

Gaussian Process (GP) is a flexible non-parametric method which has a wide variety of applications e.g., field estimation using multi-agent systems. However, the training of the hyperparameters suffers from high computational complexity. Recently, distributed hyperparameter optimization with proximal gradients has been proposed to reduce complexity, however only for a network with a central station. In this work, exploiting edge-based constraints, we propose two fully-distributed algorithms pxADMMfd and pxADMMfd,fast for a network of multi-agent systems, which do not rely on a central station. In addition, asynchronous versions of the algorithms are also proposed to reduce the synchronization overhead in heterogeneous networks. Simulations are conducted for a field estimation problem, using both artificial, and real-world datasets, which show that the proposed fully-distributed algorithms successfully converge, at the cost of an increased number of iterations.
Co-Harboring of Beta-Lactamases and mcr-1 Genes in Escherichia coli and Klebsiella pneumoniae from Healthy Carriers and Backyard Animals in Rural Communities in Ecuador

Few studies have addressed drug resistance of Enterobacterales in rural communities in developing countries. This study aimed to determine the coexistence of extended-spectrum β-lactamase (ESBL) and carbapenemase genes in Escherichia coli and Klebsiella pneumoniae strains carrying the mcr-1 gene in rural communities in Ecuador from healthy humans and their backyard animals. Sixty-two strains, thirty E. coli and thirty-two K. pneumoniae strains carrying the mcr-1 gene were selected from a previous study. PCR were performed for the presence of ESBLs and carbapenemase genes. The strains were further characterized, and the genetic relationship was studied with multi-locus sequencing typing (MLST) of seven housekeeping genes. Fifty-nine of the sixty-two mcr-1 isolates (95%) harbored at least on β-lactam resistance gene. The most prevalent ESBL genes were the blaTEM genes (present in in 80% of the E. coli strains) and the blaSHV gene (present in 84% of the K. pneumoniae strains). MSLT analysis revealed 28 different sequence types (ST); 15 for E. coli and 12 for K. pneumoniae, with most ST never described in humans and animals. The coexistence of mcr-1 and β-lactams resistant genes in E. coli and K. pneumoniae strains is alarming and threatens the efficacy of last-resort antibiotics. Our findings highlight backyard animals as a reservoir of mcr-1/β-lactams resistant genes.
A universality of dark-halo surface density for the Milky Way and Andromeda dwarf satellites as a probe of the coldness of dark matter

Abstract We propose a new astrophysical test on the nature of dark matter based on the properties of dark halos associated with dwarf spheroidal galaxies. The method adopts a mean surface density of a dark halo defined within a radius of maximum circular velocity, which is derivable for a wide variety of galaxies with any dark-matter density profiles. We find that even though dark halo density profiles are derived based on the different assumptions for each galaxy sample, this surface density is generally constant across a wide mass range of galaxy. We find that at higher halo-mass scales, this constancy for real galaxies can be naturally reproduced by both cold and warm dark matter (CDM and WDM) models. However, at low-mass scales, for which we have estimated from the Milky Way and Andromeda dwarf satellites, the mean surface density derived from WDM models largely deviates from the observed constancy, whereas CDM models are in reasonable agreement with observations.
Making Sense of Sexual Harassment Over Time: Young Women’s and Nonbinary People’s Accounts in 2000 and 2021

ABSTRACT The #metoo movement and various other social media campaigns have made sexual harassment increasingly visible in recent years. Such collective practices of naming and thereby resisting sexual harassment have been made possible by feminist discourses that have enabled the linking of personal experiences to gendered social structures. In this paper, we examine temporal shifts in young people’s accounts of sexual harassment based on two datasets generated by 15–16-year-old girls and nonbinary people which were collected 20 years apart (2000 and 2021) in Finland. We draw on poststructuralist discourse theory, intersectionality and Sara Ahmed’s writings on complaints in analysing the participants’ positions in relation to sexual harassment. Notably, in the 2000 dataset, the participants emphasized individual agency and responsibility, whereas in the 2021 dataset, they acknowledged gendered and intersectional patterns in victimization and actively resisted victim-blaming and silencing. We conclude that the positions the participants held in the two datasets differ specifically in the extent to which they are informed by feminist discourses and the extent to which sexual harassment is seen as warranting and legitimating complaint.
Improving Field Moisture Monitoring of Recycled and Virgin Aggregates

The structural capacity of pavement foundation is altered by moisture conditions. Reliable moisture monitoring of geomaterials throughout pavement service life is critical for asset management by transportation agencies. Improved moisture sensing enables transportation officials and practitioners to better understand performance of complex recycled materials under frequent extreme rain events. Although moisture sensing for geomaterials has improved significantly, there are still challenges when using sensors in recycled materials that may contain unhydrated cement, aged asphalt, or both. Challenges include the development of calibration functions that account for the presence of recycled materials and robust installation procedures, as technology was developed mostly for agricultural practices. In this study, a series of experiments were conducted to suggest improvements for installation techniques and data interpretation of soil moisture and water potential sensors. Suggested installation guidelines minimize wash-out and erosion potential at the soil–sensor interface. Experimental results indicate a strong bilinear relation between dielectric of recycled materials and water content, with a region of relatively small change governed by dielectric permittivity of air and a region of rapid change controlled by dielectric permittivity of water. Moreover, it was found that dielectric permittivity is not significantly affected by aggregate internal structure as dielectric for a specific moisture content for different compaction degrees is relatively similar. Furthermore, soil water characteristic curves obtained using the water potential sensor and improved installation technique compare reasonably well with laboratory results obtained with traditional equipment. Reliability of both moisture and water potential sensors was improved with the suggested guidelines.
Growing up with Idiopathic Pulmonary Arterial Hypertension: An Arduous Journey

Idiopathic pulmonary arterial hypertension (IPAH) is an uncommon and severe disease. We report the case of a 7-year-old boy investigated for cardiac murmur and exercise intolerance. Pulmonary hypertension (PH) was suspected at clinical examination and confirmed by echocardiography and cardiac catheterization. This case of pulmonary hypertension was classified as idiopathic given the negative etiological investigation. Vasoreactive testing with oxygen and nitric oxide was negative. Therefore, treatment with sildenafil (1.4 mg/kg/d) and bosentan (3 mg/kg/d) was initiated. This allowed the stabilization of, but not a decrease in, pulmonary artery pressure for the next 5 years, during which the patient’s quality of life was significantly reduced. At a later follow-up, the estimated pulmonary pressure was found to have increased and become supra-systemic, with a consequent deterioration in the child’s condition. This led to the decision to enter him into a clinical trial that is still ongoing. Idiopathic pulmonary arterial hypertension is a severe disease that can present with non-specific symptoms, such as asthenia and exercise limitation, which are important not to trivialize. The disease is associated with significantly decreased quality of life in affected children and carries a high burden in terms of mortality and morbidity. The current knowledge about IPAH in children is reviewed, with a particular focus on the future prospects for its treatment and the related quality of life of patients.
Theoretical study on the thermal transport and its tunability of a-plane trilayer GaN

Two-dimensional (2D) a-plane gallium nitride, a non-layered 2D material, has promising applications in photoelectric nanodevices due to its direct band bandgap. Herein, employing molecular dynamics simulations, we studied the thermal transport properties of a-plane trilayer GaN, and the temperature, together with strain modulation on the thermal conductivity of the system. The a-plane trilayer GaN shows anisotropic thermal conductivity with 70.22 Wm−1K−1 and 41.81 Wm−1K−1 along zigzag- and armchair- directions respectively at room temperature when extrapolated to infinite size. In addition, the thermal conductivity of trilayer GaN exhibits decreasing trend in response to the increase of temperature. The thermal conductivity decreases monotonically with the increased compressive uniaxial and biaxial strain, while it shows an up-then-down trend under tensile strain. The tunability of thermal conductivity under biaxial strain is much larger than that of uniaxial strain. The phonon density of states is further investigated to understand the behavior of thermal conductivity. The tunability of the system thermal conductivity will expand its applications in thermal management and nanodevices.
Radiological Evaluation of OrthAlign—A Novel Handheld Navigation System used in Total Knee Replacement

Background: The goal of this research is to determine how well OrthAlign, a novel portable navigation system for total knee replacement, helps surgeons make accurate incisions. When comparing OrthoAlign, a portable accelerometer-based technique, to the gold standard of extramedullary jigs, the results are promising. This study aimed to evaluate the accuracy of distal femoral and proximal tibial cuts in total knee arthroplasty. Materials and Methods: This research analyzed data from a prospective cohort study. Participants in the study all had resections of the proximal tibia and distal femur using the OrthAlign portable navigations device. Total knee arthroplasty (TKA) patients throughout the same time period who used traditional medullary alignment jigs were included as a control group. Before and after surgery, full-length standing stitch radiographs of the patient's lower limbs were acquired so that the alignment of their knees could be assessed. Results and Conclusions: In the mechanical alignment exam done following the surgery, the OrthAlign group performed substantially better than the control group, although the difference was not statistically significant. Patients treated with OrthoAlign had considerably improved alignment of the tibial components in the coronal plane compared to those treated manually. When comparing the OrthAlign cohort to the conventional cohort, average sagittal plane alignment of tibial components was significantly different. Yet, when comparing femoral alignment after surgery, neither the mechanical alignment nor the OrthAlign groups fared better. Furthermore, there was no statistically significant difference between the two groups when it comes to the occurrence of outliers with postoperative mechanical axis alignment >3 degrees or tibial alignment in the coronal plane >2 degrees. Compared to OrthAlign, conventional alignment methods resulted in a higher percentage of postoperative tibial alignment in the sagittal plane (greater than 2 degrees). Patients whose femurs were misaligned by more than 2 degrees after surgery favored the OrthAlign method, albeit this was not statistically different from the control group. There was a significant reduction in tourniquet time for patients using OrthoAlign compared to those using mechanical alignment devices.
Enhancing the Efficiency of WMMSE and FP for Beamforming by Minorization-Maximization

Weighted minimum mean squared error (WMMSE) and fractional programming (FP) constitute two common approaches to the weighted sum-rate maximization in communication system design. One subtle issue with WMMSE and FP lies in the tuning of a Lagrange multiplier for the power constraint when it comes to the multi-antenna transmission. To obtain the optimal Lagrange multiplier, we must repeatedly inverse an M × M matrix, where M is the number of transmit antennas, which incurs considerable complexity. To address the above issue, this work explores the connection of WMMSE and FP to minorization-maximization (MM), thereby modifying the two methods to get rid of the Lagrange multiplier. The proposed algorithm enables a parameter-free iterative optimization of the beamforming vectors with the power constraint enforced automatically. Numerical results demonstrate the faster convergence of the proposed beamforming method as compared to the conventional WMMSE and FP methods.
Efficient Online Convolutional Dictionary Learning Using Approximate Sparse Components

Most available convolutional dictionary learning (CDL) methods use a batch-learning strategy, which consists of alternating optimization of the dictionary and the sparse representations using a training dataset. The computational efficiency of CDL can be improved using an online-learning approach, where the dictionary is optimized incrementally following a sparse approximation of each training sample. However, the existing online CDL (OCDL) methods are still computationally costly when learning large dictionaries. In this paper, we propose an OCDL approach that incorporates decomposed sparse approximations instead of the training samples and substantially improves the computational costs of the existing CDL methods. The resulting optimization problem is addressed using the alternating direction method of multipliers (ADMM).
Fed-TSN: Joint Failure Probability-Based Federated Learning for Fault-Tolerant Time-Sensitive Networks

Industrial Internet of Things (IIoT) applications have diverse network session requirements. Certain critical applications, such as emergency alert relays, as well as industrial floor evacuation and surveillance systems, require fresh updates that can maintain the most recently delivered packets. This requires high reconfigurability to an extent where the system can measure the impact of an event and adapt the network accordingly. Recent research has demonstrated that network failures can undermine the sustainability of Industry 4.0 or Industrial IoT in general. In this paper, we design an intelligent Federated learning based Time-Sensitive Networking (Fed-TSN) controller framework to optimize the failure recovery. In industrial IoT scenarios, such as emergency evacuations on factory floors due to natural disasters, there can be multiple link failures with no disjoint paths which require a sustainable recovery solution. Accordingly, we consider multiple simultaneous link failures, both for networks with and without disjoint paths. The typically probabilistic network failures on a factory floor call for designing a mechanism that can search for routes with minimum joint failure probability (JFP). We formulate the JFP minimization problem as a non-linear integer program. We design a Software Defined Networking (SDN) controller that runs an application to produce near-optimal solutions for providing enhanced sustainability in a wide range of Industry 4.0 scenarios. We employ this non-linear integer program solution as input to our intelligent Fed-TSN fault recovery strategy that predicts the migration location based on the changes in the TSN gate schedule. We conduct simulations to quantify the improvements achieved with Fed-TSN compared to state-of-the-art approaches.
CNN Filter for RPR-Based SR in VVC with Wavelet Decomposition

In this paper, we propose a convolutional neural network (CNN) filter for reference picture resampling (RPR)-based super-resolution (SR) with wavelet decomposition. The proposed CNN filter takes the low resolution (LR) reconstructed frame (RecLR), LR prediction frame (PreLR) and high resolution (HR) RPR upsampled frame (RPRoutput) as the input for RPR-based SR. Thus, the proposed CNN filter not only learns a mapping function between LR and HR images, but also effectively removes blocking artifacts in the reconstructed frame. We adopt wavelet decomposition to make RPRoutput the same size as RecLR and PreLR as well as obtain the relationship between high frequency (HF) and low frequency (LF) components. To maximize feature reuse under the limited parameters, we design a residual spatial and channel attention block (RSCB) that combines residual blocks with spatial attention and channel attention to learn the weighted local information and global information in different receptive fields. Experimental results show that the proposed CNN filter achieves -8.98% and -4.05% BD-rate reductions on Y channel in AI and RA configurations over VTM-11. 0_NNVC-2.0 anchor, respectively.
Delayed Diagnosis of Blunt Ureteral Injury following Motor Vehicle Collision

Background A 19-year-old male requiring emergency surgery after presenting to the emergency department (ED) as a trauma activation status post-motor vehicle collision. Summary. The patient presented to the ED after a motor vehicle collision. He was taken emergently to the operating room after finding hemoperitoneum on computerized tomography scan without evidence of solid organ injury. Significant small and large bowel injuries were discovered requiring resection and anastomosis. The patient had an uneventful post-operative recovery and was discharged home. He was later re-admitted to the hospital with a large pelvic abscess and a left mid-ureteral stricture causing hydronephrosis. The abscess was treated with antibiotics, and the left ureteral injury was treated with a nephrostomy tube and stent placement. He made a full recovery after hospital re-admission and a delay in diagnosis of blunt ureteral injury. Conclusion Patients involved in motor vehicle collisions are at risk of multi-system trauma including genito-urinary injuries. A small percentage of these patients may present with blunt ureteral injuries. A high index of suspicion is required to make an early diagnosis. Earlier diagnosis may help to prevent morbidity.