Miracle-dz/UniFAR-FTUs huggingface dataset:
Iterative Optimization of Quarter Sampling Masks for Non-Regular Sampling Sensors

Non-regular sampling can reduce aliasing at the expense of noise. Recently, it has been shown that non-regular sampling can be carried out using a conventional regular imaging sensor when the surface of its individual pixels is partially covered. This technique is called quarter sampling (also 1/4 sampling), since only one quarter of each pixel is sensitive to light. For this purpose, the choice of a proper sampling mask is crucial to achieve a high reconstruction quality. In the scope of this work, we present an iterative algorithm to improve an arbitrary quarter sampling mask which results in a continuous increase of the reconstruction quality. In terms of the reconstruction algorithms, we test two simple algorithms, namely, linear interpolation and nearest neighbor interpolation, as well as two more sophisticated algorithms, namely, steering kernel regression and frequency selective extrapolation. Besides PSNR gains of +0.31 dB to +0.68 dB relative to a random quarter sampling mask resulting from our optimized mask, visually noticeable enhancements are perceptible.
A Comparative Study of Combined Dermaroller and Platelet-Rich Plasma Versus Dermaroller Alone in Acne Scars and Assessment of Quality of Life Before and After Treatment

Background: Scarring due to acne is a frequently encountered problem in dermatological outpatient department. Microneedling has been a well-accepted modality for the treatment of atrophic acne scars. Platelet-rich plasma (PRP) is a newer option to investigate. Aims: The aims of this study were (1) to compare the efficacy of combined dermaroller and PRP therapy with dermaroller alone in facial acne scars and (2) to assess the psychosocial impact due to acne scars and its treatment. Methods: A total of 55 patients were included in the study and randomly divided into two groups, Group A: 28 and Group B: 27. Proper counseling was done, and detailed clinical findings were recorded. Patients in Group A were treated with dermaroller alone while Group B patients underwent treatment with a combination of dermaroller and intradermal PRP injections. A total of three sitting were done at monthly interval. Final response was assessed at 1 month after the last sitting. Criteria of evaluation included Goodman and Baron's quantitative scale, visual analog score, and dermatology life quality index scores. Side effects were noted. Results were analyzed using Chi-square test and t-test. Results: Significant percentage improvement was noted in both the groups. However, Group B treated with both modalities had better results when compared with that in the Group A. Conclusion: A combination approach using dermaroller and PRP was a safe and better option than using dermaroller alone in atrophic acne scars for clinical improvement as well as for improvement in dermatology life quality index score.
Discriminative Autoencoder

Classification using cross-datasets (where a classifier trained using annotated image set A is used to test similar images of set B due to lack of training images in B) is important for many classification problems especially in biomedical imaging. We propose a discriminative autoencoder, useful for addressing the challenge of classification using cross-datasets. Our autoencoder learns an encoder and decoder such that the distances between the representations of the same class is minimized whereas the distances between the representations of different classes are maximized. We derive a fast algorithm to solve the aforementioned problem using the Augmented Lagrangian Alternating Directions Method of Multipliers (ADMM) approach. ADMM is a faster alternative to back-propagation which is used in standard autoencoders. The proposed method outperforms state-of-the-art representation learning tools in terms of classification results in breast cancer related histopathological image set MITOS and AMIDA and some of the benchmark image datasets.
Utilization of DES-Lignin as a Bio-Based Hydrophilicity Promoter in the Fabrication of Antioxidant Polyethersulfone Membranes

Enhancement of membrane permeability at no detriment of its other performances, e.g., selectivity, is a goal-directed objective in membrane fabrication. A novel antioxidant DES-lignin (lignin extracted from birch wood by using a deep eutectic solvent) polyethersulfone (PES) membrane, containing 0–1 wt % DES-lignin, was fabricated with the phase inversion technique. The performance and morphology of the fabricated membranes were characterized by a pure water flux, polyethylene glycol (PEG) retention, Fourier transform infrared spectroscopy, scanning electron microscopy, and contact angle measurements. Membranes with less negative charge and better hydrophilicity were obtained when the DES-lignin content in the polymer solution was increased. With the highest dosage, the incorporation of DES-lignin in the membrane matrix improved the membrane permeability by 29.4% compared to a pure PES membrane. Moreover, no leakage of DES-lignin from the membrane structure was observed, indicating good compatibility of DES-lignin with the PES structure. It was also found that the improvement of both rejection and pure water flux could be achieved by using a small dosage of DES-lignin (0.25 wt %) in membrane fabrication. The membranes incorporated with DES-lignin showed higher DPPH (2,2-diphenyl-1-picrylhydrazyl) and ABTS (2,2-Azino-bis (3-ethylbenzothiazoline-6-sulfonic acid)) scavenging activity compared to the pure membrane, where 2.6 and 1.1 times higher DPPH and ABTS scavenging activity was observed with the highest DES-lignin content (1 wt %). Thus, the results of this study demonstrate well the feasibility of utilizing DES-lignin as an antioxidant bio-based hydrophilicity promoter in the fabrication of ultrafiltration membranes.
Dense Convolution for Semantic Segmentation

State-of-the-art semantic segmentation methods adopt fully convolutional neural networks(FCNs) to solve this dense prediction problem. However, replacing fully connected layers with the standard 2D convolution layer is straightforward yet not optimal in generating segmentation results. In this paper we develop a dense convolution scheme that is more suitable for semantic segmentation. Instead of generating a single output, dense convolution produces the same number of output as its input and introduces spatial overlaps into current convolutions. Then each activation is obtained from multiple overlapped dense convolutions with learnable weights. Such dense convolution helps to reinforce local connections between activations and provide more flexible receptive fields for predictions. Experiments on benchmark dataset demonstrate the effectiveness of the proposed approach in semantic segmentation tasks.
Object Localization Without Bounding Box Information Using Generative Adversarial Reinforcement Learning

Object localization can be defined as the task of finding the bounding boxes of objects in a scene. Most of the state-of-the-art approaches utilize meticulously handcrafted training datasets. In this work, we are aiming to create a generative adversarial reinforcement learning framework, which can work without having any explicit bounding box information. Instead of relying on bounding boxes, our framework uses tightly cropped object images as training data. Our image localization framework consists of two parts: a reinforcement learning agent (RL agent) and a discriminator. The RL agent takes input scenes and crops them with the objective of creating a tightly cropped object image. The discriminator tries to distinguish whether the image is generated by the RL agent or it comes from a tightly cropped object database. Experiments indicate that it is possible to achieve a promising localization performance without having explicit bounding box data. It can be concluded that generative adversarial reinforcement learning is an important tool in dealing with other learning problems where explicit input/output paired data is not available.
Comparative effectiveness of first-line biological monotherapy use in rheumatoid arthritis: a retrospective analysis of the RECord-linkage On Rheumatic Diseases study on health care administrative databases

Objective These analyses aim to comparatively evaluate the persistence on treatment of different biological disease-modifying antirheumatic drugs (bDMARDs) when administered in monotherapy compared with combination with conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) in rheumatoid arthritis (RA) patients receiving first-line biologics. Design This is a retrospective observational study on Administrative Healthcare Databases. Methods Data were extracted from healthcare databases of the Lombardy Region, Italy (2004–2013), as a part of the RECord-linkage On Rheumatic Diseases study, on behalf of the Italian Society for Rheumatology. Analyses included patients with RA starting first-line approved course of bDMARDs and evaluated drug survival by using Cox proportional hazard models. Results are presented as HRs and 95% CI, crude and adjusted for prespecified confounders (age, sex, disease duration, Charlson Comorbidity Index (CCI), previous infections, use of concomitant glucocorticoids or non-steroidal anti-inflammatory drugs (NSAIDs)). Results 4478 patients with RA were included (17.84% monotherapy). Etanercept, adalimumab and infliximab were the most prescribed first-line biologics. bDMARD monotherapy was associated with longer disease duration, higher CCI, lower glucocorticoids and NSAIDs use. Compared with monotherapy, combination associated with a lower risk of failure (adjusted HR 0.79, 95% CI 0.72 to 0.88). Among monotherapies, considering etanercept as reference, adalimumab (1.28, 95% CI 1.03 to 1.59) and infliximab (2.41, 95% CI 1.85 to 3.15) had higher risk of failure. Concomitant methotrexate (0.78, 95% CI 0.70 to 0.87), leflunomide (0.80, 95% CI 0.65 to 0.98) or csDMARD combinations (0.77, 95% CI 0.68 to 0.87) reduced the risk of bDMARD withdrawal. Conclusion Adalimumab and infliximab monotherapies show lower retention rate compared with etanercept. The relatively small number of therapeutic courses different from tumour necrosis factor (TNF) inhibitors makes more difficult to achieve conclusive results with other biologics. Concomitant methotrexate, leflunomide and csDMARDs combination associate with longer survival on bDMARD. Our data confirm the effectiveness of the current practices in the choice of etanercept as first-line anti-TNF monotherapy and strengthen the currently recommended use of bDMARDs in combination with csDMARDs.
Certifiably Globally Optimal Extrinsic Calibration From Per-Sensor Egomotion

We present a certifiably globally optimal algorithm for determining the extrinsic calibration between two sensors that are capable of producing independent egomotion estimates. This problem has been previously solved using a variety of techniques, including local optimization approaches that have no formal global optimality guarantees. We use a quadratic objective function to formulate calibration as a quadratically constrained quadratic program (QCQP). By leveraging recent advances in the optimization of QCQPs, we are able to use existing semidefinite program solvers to obtain a certifiably global optimum via the Lagrangian dual problem. Our problem formulation can be globally optimized by existing general-purpose solvers in less than a second, regardless of the number of measurements available and the noise level. This enables a variety of robotic platforms to rapidly and robustly compute and certify a globally optimal set of calibration parameters without a prior estimate or operator intervention. We compare the performance of our approach with a local solver on extensive simulations and multiple real datasets. Finally, we present necessary observability conditions that connect our approach to recent theoretical results and analytically support the empirical performance of our system.
Gender-Specific Associations of Sensory Impairments with Depression and Cognitive Impairment in Later Life

Objective Sensory impairments, such as vision and hearing impairments, increase with age, and studies have shown that self-reported vision and hearing impairments are associated with adverse mental health outcomes in later life. Although gender differences may exist in the associations between sensory impairments and geriatric mental health outcomes, little research has been done examining gender differences in the above associations. Therefore, the purpose of this study was to investigate the relationship between self-reported sensory impairments and geriatric mental health outcomes with the aim of determining whether any association differs by gender. Methods The study sample was drawn from a nationally representative data set, the 2012 Korean Longitudinal Study of Ageing, and the final sample consisted of 2,167 females and 1,664 males aged 65 and over. Self-reported sensory impairments were categorized into four groups: no sensory impairment (reference), vision impairment only, hearing impairment only, and dual sensory impairment. Depression and cognitive impairment were used as dependent variables in separate analyses. Results Adjusted for confounding variables, results from a multivariate analysis showed that vision impairment was significantly associated with depression only among women. Moreover, hearing impairment was significantly associated with cognitive impairment for women, but not for men. Having dual sensory impairment was associated with depression only among men, while having dual sensory impairment was associated with cognitive impairment only among women. Conclusion These findings suggest that the associations between sensory impairments and geriatric mental health outcomes vary according to gender. Therefore, gender-specific strategies in healthcare policies are needed.
Term Extraction via Neural Sequence Labeling a Comparative Evaluation of Strategies Using Recurrent Neural Networks

Traditionally systems for term extraction use a two stage approach of ﬁrst identifying candiate terms, and the scoring them in a second process for identifying actual terms. Thus, research in this ﬁeld has often mainly focused on reﬁning and improving the scoring process of term candidates, which commonly are identiﬁed using linguistic and statistical features. Machine learning techniques and especially neural networks are currently only used in the second stage, that is to score candidates and classify them. In contrast to that we have built a system that identiﬁes terms via directly performing sequence-labeling with a BILOU scheme on word sequences. To do so we have worked with different kinds of recurrent neural networks and word embeddings. In this paper we describe how one can built a state-of-the-art term extraction systems with this single-stage technique and compare different network types and topologies and also examine the inﬂuence of the type of input embedding used for the task. We further investigated which network types and topologies are best suited when applying our term extraction systems to other domains than that of the training data of the networks.
Cosine Metric Learning for Speaker Verification in the I-vector Space

It is known that the equal-error-rate (EER) performance of a speaker veriﬁcation system is determined by the overlap region of the decision scores of true and imposter trials. Also, the co-sine similarity scores of the true or imposter trials produced by the state-of-the-art i-vector front-end approximate to a Gaussian distribution, and the overlap region of the two classes of trials depends mainly on their between-class distance. Motivated by the above facts, this paper presents a cosine similarity learning (CML) framework for speaker veriﬁcation, which combines classical compensation techniques and the cosine similarity scoring for improving the EER performance. CML minimizes the overlap region by enlarging the between-class distance while introducing a regularization term to control the with-in class variance, which is initialized by a traditional channel compensation technique such as linear discriminant analysis. Experiments are carried out to compare the proposed CML framework with several traditional channel compensation base-lines on the NIST speaker recognition evaluation data sets. The results show that CML outperforms all the studied initialization compensation techniques.
A Probability Weighted Beamformer for Noise Robust ASR

We investigate a novel approach to spatial ﬁltering that is adaptive to conditions at different time-frequency (TF) points for noise removal by taking advantage of speech sparsity. Our approach combines a noise reduction beamformer with a minimum variance distortionless response (MVDR) beamformer or Generalized Eigenvalue (GEV) beamformer through TF posterior probabilities of speech presence (PPSP). To estimate PPSP, we study both statistical model-based and neural network based methods, where in the former, we use complex Gaussian mixture modeling (CGMM) on temporally augmented spatial spectral features, and in the latter, we use neural network (NN) based TF masks to initialize speech and noise covariance matrices in CGMM. We have conducted experiments on CHiME-3 task. On its real noisy speech test set, our methods of feature augmentation, TF dependent spatial ﬁlter, and NN-based mask initialization on covariances for CGMM have yielded relative word error rate (WER) reductions cumulatively by 8%, 16%, and 25% over the original CGMM based MVDR. On the real test data, the three methods have also produced consistent WER reductions when replacing MVDR by GEV.
Error Modeling via Asymmetric Laplace Distribution for Deep Neural Network Based Single-Channel Speech Enhancement

The minimum mean squared error (MMSE) as a conventional training criterion for deep neural network (DNN) based speech enhancement has been found many problems. In our recent work, a maximum likelihood (ML) approach to parameter learning by modeling the prediction error vector as a Gaussian density was proposed. In this study, our preliminary statistical analysis reveals the super-Gaussianity and asymmetricity of the prediction error distribution. Consequently, we adopt the asymmetric Laplace distribution (ALD) instead of the Gaussian distribution (GD) to model the prediction error vectors. Then the new derivation for optimizing the the proposed ML-ALD-DNN with both DNN and ALD parameters is presented. Moreover, we can well interpret the asymmetry parameter of ALD as the balance control between noise reduction and speech preservation from both formulations and experiments. This implies that the customization of DNN models for the different noise types and levels is possible by the setting of the asymmetry parameter. Finally, our ML-ALD-DNN approach achieves better STOI and SSNR measures over both MMSE-DNN and ML-GD-DNN approaches.
Lexical and Acoustic Deep Learning Model for Personality Recognition

Deep learning has been very successful on labeling tasks such as image classiﬁcation and neural network modeling, but there has not yet been much work on using deep learning for automatic personality recognition. In this study, we propose two deep learning structures for the task of personality recognition using acoustic-prosodic, psycholinguistic, and lexical features, and present empirical results of several experimental conﬁgurations, including a cross-corpus condition to evaluate robustness. Our best models match or outperform state-of-the-art on the well-known myPersonality corpus, and also set a new state-of-the-art performance on the more difﬁcult CXD corpus.
The kinases HipA and HipA7 phosphorylate different substrate pools in Escherichia coli to promote multidrug tolerance

Differences in the targets of HipA and its variant HipA7 may explain why these kinases have different effects on bacterial persistence. Balancing bacterial growth with drug resistance Within some bacterial populations, a subset of cells grows more slowly than the rest, which decreases the competitive fitness of these cells under favorable growth conditions but enables them to survive exposure to antibiotics. The kinase HipA is important for the survival of such Escherichia coli persister cells because it targets the glutamate-tRNA ligase GltX, thus halting translation and slowing cell growth. A variant of this kinase that is associated with some clinical isolates, HipA7, is more efficient than HipA in inducing persistence, although it is less effective at reducing cell growth. Through phosphoproteomic analyses, Semanjski et al. found that although both HipA and HipA7 targeted GltX, HipA also targeted additional substrates, which likely account for the potency of HipA in reducing cell growth and may explain why HipA7, despite being more effective at promoting persistence, is less toxic than HipA. The bacterial serine-threonine protein kinase HipA promotes multidrug tolerance by phosphorylating the glutamate-tRNA ligase (GltX), leading to a halt in translation, inhibition of growth, and induction of a physiologically dormant state (persistence). The HipA variant HipA7 substantially increases persistence despite being less efficient at inhibiting cell growth. We postulated that this phenotypic difference was caused by differences in the substrates targeted by both kinases. We overproduced HipA and HipA7 in Escherichia coli and identified their endogenous substrates by SILAC-based quantitative phosphoproteomics. We confirmed that GltX was the main substrate of both kinase variants and likely the primary determinant of persistence. When HipA and HipA7 were moderately overproduced from plasmids, HipA7 targeted only GltX, but HipA phosphorylated several additional substrates involved in translation, transcription, and replication, such as ribosomal protein L11 (RplK) and the negative modulator of replication initiation, SeqA. HipA7 showed reduced kinase activity compared to HipA and targeted a substrate pool similar to that of HipA only when produced from a high–copy number plasmid. The kinase variants also differed in autophosphorylation, which was substantially reduced for HipA7. When produced endogenously from the chromosome, HipA showed no activity because of inhibition by the antitoxin HipB, whereas HipA7 phosphorylated GltX and phage shock protein PspA. Initial testing did not reveal a connection between HipA-induced phosphorylation of RplK and persistence or growth inhibition, suggesting that other HipA-specific substrates were likely responsible for growth inhibition. Our results contribute to the understanding of HipA7 action and present a resource for elucidating HipA-related persistence.