Miracle-dz/UniFAR-FTUs huggingface dataset:
Training Deep Learning Based Image Denoisers From Undersampled Measurements Without Ground Truth and Without Image Prior

Compressive sensing is a method to recover the original image from undersampled measurements. In order to overcome the ill-posedness of this inverse problem, image priors are used such as sparsity, minimal total-variation, or self-similarity of images. Recently, deep learning based compressive image recovery methods have been proposed and have yielded state-of-the-art performances. They used data-driven approaches instead of hand-crafted image priors to regularize ill-posed inverse problems with undersampled data. Ironically, training deep neural networks (DNNs) for them requires "clean" ground truth images, but obtaining the best quality images from undersampled data requires well-trained DNNs. To resolve this dilemma, we propose novel methods based on two well-grounded theories: denoiser-approximate message passing (D-AMP) and Stein's unbiased risk estimator (SURE). Our proposed methods were able to train deep learning based image denoisers from undersampled measurements without ground truth images and without additional image priors, and to recover images with state-of-the-art qualities from undersampled data. We evaluated our methods for various compressive sensing recovery problems with Gaussian random, coded diffraction pattern, and compressive sensing MRI measurement matrices. Our proposed methods yielded state-of-the-art performances for all cases without ground truth images. Our methods also yielded comparable performances to the methods with ground truth data.
Physical Attribute Prediction Using Deep Residual Neural Networks

Images taken from the Internet have been used alongside Deep Learning for many different tasks such as: smile detection, ethnicity, hair style, hair colour, gender and age prediction. After witnessing these usages, we were wondering what other attributes can be predicted from facial images available on the Internet. In this paper we tackle the prediction of physical attributes from face images using Convolutional Neural Networks trained on our dataset named FIRW. We crawled around 61, 000 images from the web, then use face detection to crop faces from these real world images. We choose ResNet-50 as our base network architecture. This network was pretrained for the task of face recognition by using the VGG-Face dataset, and we finetune it by using our own dataset to predict physical attributes. Separate networks are trained for the prediction of body type, ethnicity, gender, height and weight; our models achieve the following accuracies for theses tasks, respectively: 84.58%, 87.34%, 97.97%, 70.51%, 63.99%. To validate our choice of ResNet-50 as the base architecture, we also tackle the famous CelebA dataset. Our models achieve an averagy accuracy of 91.19% on CelebA, which is comparable to state-of-the-art approaches.
PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation

Deep convolutional networks have demonstrated the state-of-the-art performance on various medical image computing tasks. Leveraging images from different modalities for the same analysis task holds clinical benefits. However, the generalization capability of deep models on test data with different distributions remain as a major challenge. In this paper, we propose the PnPAdaNet (plug-and-play adversarial domain adaptation network) for adapting segmentation networks between different modalities of medical images, e.g., MRI and CT. We propose to tackle the significant domain shift by aligning the feature spaces of source and target domains in an unsupervised manner. Specifically, a domain adaptation module flexibly replaces the early encoder layers of the source network, and the higher layers are shared between domains. With adversarial learning, we build two discriminators whose inputs are respectively multi-level features and predicted segmentation masks. We have validated our domain adaptation method on cardiac structure segmentation in unpaired MRI and CT. The experimental results with comprehensive ablation studies demonstrate the excellent efficacy of our proposed PnP-AdaNet. Moreover, we introduce a novel benchmark on the cardiac dataset for the task of unsupervised cross-modality domain adaptation. We will make our code and database publicly available, aiming to promote future studies on this challenging yet important research topic in medical imaging.
MID-Fusion: Octree-based Object-Level Multi-Instance Dynamic SLAM

We propose a new multi-instance dynamic RGBD SLAM system using an object-level octree-based volumetric representation. It can provide robust camera tracking in dynamic environments and at the same time, continuously estimate geometric, semantic, and motion properties for arbitrary objects in the scene. For each incoming frame, we perform instance segmentation to detect objects and refine mask boundaries using geometric and motion information. Meanwhile, we estimate the pose of each existing moving object using an object-oriented tracking method and robustly track the camera pose against the static scene. Based on the estimated camera pose and object poses, we associate segmented masks with existing models and incrementally fuse corresponding colour, depth, semantic, and foreground object probabilities into each object model. In contrast to existing approaches, our system is the first system to generate an object-level dynamic volumetric map from a single RGB-D camera, which can be used directly for robotic tasks. Our method can run at 2-3 Hz on a CPU, excluding the instance segmentation part. We demonstrate its effectiveness by quantitatively and qualitatively testing it on both synthetic and real-world sequences.
Network Utility Maximization Under Maximum Delay Constraints and Throughput Requirements

We consider a multi-path routing problem of maximizing the aggregate user utility over a multi-hop network, subject to link capacity constraints, maximum end-to-end delay constraints, and user throughput requirements. A user’s utility is a concave function of the achieved throughput or the experienced maximum delay. The problem is important for supporting real-time multimedia traffic and is uniquely challenging due to the need of simultaneously considering maximum delay constraints and throughput requirements. In this paper, we first show that it is NP-complete either (i) to construct a feasible solution strictly meeting all constraints, or (ii) to obtain an optimal solution after relaxing either the maximum delay constraints or the throughput requirements. We then develop a polynomial-time approximation algorithm named PASS. The design of PASS leverages a novel understanding between non-convex maximum-delay-aware problems and their convex average-delay-aware counterparts, which can be of independent interest and suggests a new avenue for solving maximum-delay-aware network optimization problems. We prove that PASS always obtains approximate solutions (i.e., with theoretical performance guarantees), at the cost of violating both the maximum delay constraints and the throughput requirements by up to constant ratios. We also develop two variants of PASS, named PASS-M and PASS-T, to generate approximate solutions at the cost of violating either the maximum delay constraints or the throughput requirements by up to problem-dependent ratios. We evaluate our solutions using extensive simulations on Amazon EC2 datacenters supporting video-conferencing traffic. Compared to the existing algorithms and a conceivable baseline, our solutions obtain up to 100% improvement of utilities, by meeting the throughput requirements but relaxing the maximum delay constraints to the extent acceptable for practical video conferencing applications.
Relationship marketing for SMEs in Uganda

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Automatic Detection of Reflective Thinking in Mathematical Problem Solving Based on Unconstrained Bodily Exploration

For technology (like serious games) that aims to deliver interactive learning, it is important to address relevant mental experiences such as reflective thinking during problem solving. To facilitate research in this direction, we present the weDraw-1 Movement Dataset of body movement sensor data and reflective thinking labels for 26 children solving mathematical problems in unconstrained settings where the body (full or parts) was required to explore these problems. Further, we provide qualitative analysis of behaviours that observers used in identifying reflective thinking moments in these sessions. The body movement cues from our compilation informed features that led to average F1 score of 0.73 for binary classification of problem-solving episodes by reflective thinking based on Long Short-Term Memory neural networks. We further obtained 0.79 average F1 score for end-to-end classification, i.e., based on raw sensor data. Finally, the algorithms resulted in 0.64 average F1 score for subsegments of these episodes as short as 4 seconds. Overall, our results show the possibility of detecting reflective thinking moments from body movement behaviours of a child exploring mathematical concepts bodily, such as within serious game play.
The 5′-3′ Exoribonuclease XRN4 Regulates Auxin Response via the Degradation of Auxin Receptor Transcripts

Auxin is a major hormone which plays crucial roles in instructing virtually all developmental programs of plants. Its signaling depends primarily on its perception by four partially redundant receptors of the TIR1/AFB2 clade (TAARs), which subsequently mediate the specific degradation of AUX/IAA transcriptional repressors to modulate the expression of primary auxin-responsive genes. Auxin homeostasis depends on complex regulations at the level of synthesis, conjugation, and transport. However, the mechanisms and principles involved in the homeostasis of its signaling are just starting to emerge. We report that xrn4 mutants exhibit pleiotropic developmental defects and strong auxin hypersensitivity phenotypes. We provide compelling evidences that these phenotypes are directly caused by improper regulation of TAAR transcript degradation. We show that the cytoplasmic 5′-3′ exoribonuclease XRN4 is required for auxin response. Thus, our work identifies new targets of XRN4 and a new level of regulation for TAAR transcripts important for auxin response and for plant development.
Chrysanthemum Stunt Viroid Resistance in Chrysanthemum

Chrysanthemum stunt viroid (CSVd) is one of the most severe threats in Chrysanthemum morifolium production. Over the last decade, several studies have reported the natural occurrence of CSVd resistance in chrysanthemum germplasms. Such CSVd-resistant germplasms are desirable for the stable production of chrysanthemum plants. Current surveys include finding new resistant chrysanthemum cultivars, breeding, and revealing resistant mechanisms. We review the progress, from discovery to current status, of CSVd-resistance studies, while introducing information on the improvement of associated inoculation and diagnostic techniques.
Methodology of Drought Stress Research: Experimental Setup and Physiological Characterization

Drought is one of the major stress factors affecting the growth and development of plants. In this context, drought-related losses of crop plant productivity impede sustainable agriculture all over the world. In general, plants respond to water deficits by multiple physiological and metabolic adaptations at the molecular, cellular, and organism levels. To understand the underlying mechanisms of drought tolerance, adequate stress models and arrays of reliable stress markers are required. Therefore, in this review we comprehensively address currently available models of drought stress, based on culturing plants in soil, hydroponically, or in agar culture, and critically discuss advantages and limitations of each design. We also address the methodology of drought stress characterization and discuss it in the context of real experimental approaches. Further, we highlight the trends of methodological developments in drought stress research, i.e., complementing conventional tests with quantification of phytohormones and reactive oxygen species (ROS), measuring antioxidant enzyme activities, and comprehensively profiling transcriptome, proteome, and metabolome.
Effects of an entertaining, culturally targeted narrative and an appealing expert interview on the colorectal screening intentions of African American women.

Universal screening for colorectal cancer (CRC) is recommended for individuals 50-75 years of age, but screening uptake is suboptimal and African Americans have suffered persistent racial disparities in CRC incidence and deaths. We compared a culturally tailored fictional narrative and an engaging expert interview on the ability to increase intentions to be screened for CRC among African American women. In a post-only experiment, women (N = 442) in face-to-face listening groups in African American churches heard audio recordings of either a narrative or an expert interview. Questionnaires were completed immediately afterward and 30 days later. Women who heard narratives reported stronger intentions to be screened with a home stool blood test than women who heard the interview; the effect lasted at least 30 days. Culturally tailored, fictional narratives appear to be an effective persuasive strategy for reducing racial disparities in CRC outcomes.
Choroidal changes in human myopia: insights from optical coherence tomography imaging

The choroid is a vascular tissue which plays a range of critical roles in the normal physiology of the eye, such as supplying the outer retina with oxygen and nutrients and the regulation of intraocular pressure. There is also substantial evidence, particularly from animal studies, that the choroid plays an important role in the regulation of eye growth and the development of common refractive errors like myopia. In recent years, advances in optical coherence tomography technology have improved our ability to image and measure the choroid in the human eye. Research using this technology over the past decade has dramatically improved our knowledge of the normal choroid, and its potential role in the regulation of eye growth and refractive error development. This review aims to provide an overview of recent work examining the normal human choroid, its changes with myopia and the possible role of the choroid in the mechanism regulating eye growth. Studies have demonstrated that choroidal thinning accompanies the development and progression of myopia, and have established a close link between eye growth and choroidal thickness changes. Dramatic thinning of the choroid is seen with high myopia, and associations are also observed between choroidal thinning and reduced vision, and the development of retinal pathology associated with high myopia. In the short‐term, environmental factors known to be associated with myopia development and more rapid eye growth typically lead to a thinning of the choroid, whereas factors linked to a slowing of eye growth are typically associated with short‐term choroidal thickening. Collectively, these findings suggest that the choroid is an important biomarker of eye growth in the human eye, and additional research to better understand the human choroid is likely to further our knowledge of the signals and pathways regulating eye growth, myopia development and progression.
An efficient heterogeneous signcryption for smart grid

A smart grid, considered the next-generation type of power grid, combines a traditional power grid with information and communication technologies to effectively facilitate power generation and ensure transmission security and reliability in real-time. Only authorized consumers should be able to access the smart grid because the information gathered by smart meters includes users’ private information. However, smart grid security is still a challenge. Motivated by this challenge, in this paper, we propose a heterogeneous signcryption (HSC) scheme for secure communication between smart meters and the utility. We demonstrate that this scheme is indistinguishable against adaptive chosen-ciphertext attacks (IND-CCA2), existentially unforgeable against adaptive chosen-message attacks (EUF-CMA) and ciphertext-anonymous against adaptive chosen ciphertext attacks (ANON-CCA2) under the computational Diffie-Hellman (CDH) problem in the random oracle model. Our scheme simultaneously achieves confidentiality, integrity, authentication, non-repudiation and ciphertext anonymity in a single logical step. It supports heterogeneous systems, allowing a meter in an identity-based cryptography (IBC) environment to transmit electrical usage data to a utility in a public key infrastructure (PKI) environment. Compared with other existing related schemes, our scheme has the lowest communication overhead and energy consumption for the smart grid. Based on these features, our scheme is highly suitable for secure power transmissions in a smart grid.
Adolescent Bullying and Weapon Carrying: A Longitudinal Investigation.

Using four data waves from an ongoing longitudinal study, we examined the longitudinal association between weapon carrying and bullying (i.e., bully, victim, bully-victim). Participants were 1,042 adolescents (55.9% female) recruited from Texas high schools, including Hispanic (31.7%), White (30.3%), African American (26.6%), and other (11.4%) with a mean age of 15.1 years at baseline. Multivariate logistic regression revealed that only baseline bully-victim was linked to weapon carrying and only 1 year later (Adjusted odds ratio = 1.76, 95% confidence interval: 1.08, 2.85), after controlling for baseline weapon carrying, age, gender, race, and parent education. No significant longitudinal association was found with bully or victim. Findings emphasize the importance of interventions targeting both bullying perpetrators and victims, while also highlighting the necessity of longitudinal research.
Estimating contributions of pelagic and benthic pathways to consumer production in coupled marine food webs

Pelagic and benthic systems usually interact, but their dynamics and production rates differ. Such differences influence the distribution, reproductive cycles, growth rates, stability and productivity of the consumers they support. Consumer preferences for, and dependence on, pelagic or benthic production are governed by the availability of these sources of production and consumer life history, distribution, habitat, behavioural ecology, ontogenetic stage and morphology. Diet studies may demonstrate the extent to which consumers feed on prey in pelagic or benthic environments. But they do not discriminate benthic production directly supported by phytoplankton from benthic production recycled through detrital pathways. The former will track the dynamics of phytoplankton production more closely than the latter. We develop and apply a new analytical method that uses carbon (C) and sulphur (S) natural abundance stable isotope data to assess the relative contribution of pelagic and benthic pathways to fish consumer production. For 13 species of fish that dominate community biomass in the northern North Sea (estimated >90% of total biomass), relative modal use of pelagic pathways ranged from <25% to >85%. Use of both C and S isotopes as opposed to just C reduced uncertainty in relative modal use estimates. Temporal comparisons of relative modal use of pelagic and benthic pathways revealed similar ranking of species dependency over 4 years, but annual variation in relative modal use within species was typically 10%-40%. For the total fish consumer biomass in the study region, the C and S method linked approximately 70% and 30% of biomass to pelagic and benthic pathways, respectively. As well as providing a new method to define consumers' links to pelagic and benthic pathways, our results demonstrate that a substantial proportion of fish biomass, and by inference production, in the northern North Sea is supported by production that has passed through transformations on the seabed.
A critical analysis of recent data on the long-term outcome of antipsychotic treatment

Abstract New studies of long-term outcomes claim to show that taking antipsychotics on a continuous and indefinite basis is the best approach for people diagnosed with a first episode of psychosis or schizophrenia. A 10-year follow-up of a trial of quetiapine maintenance, for example, found a higher proportion of people with a poor composite outcome in the group initially randomised to placebo. However, most people classified as showing poor outcome were rated as having a mild score on a single psychotic symptom; there were no differences in overall symptoms, positive or negative symptoms or level of functioning. Moreover, 16% of participants did not have a follow-up interview and data from the end of the original trial were used instead. A study using a Finnish database suggested that mortality and readmission were higher in people who did not start long-term antipsychotic treatment or who discontinued it as compared with long-term continuous users. However, the analysis did not control for important confounders and is likely to reflect the fact that people who do not comply with treatment are at higher risk of death due to underlying health risks and behaviours. The analysis showed a slightly higher risk of readmission among non-users of antipsychotics compared with long-term users and a more substantial increased risk among people who discontinued treatment. However, follow-up ceased at the first readmission and therefore eventual, long-term outcome was not assessed. Speed of reduction and whether it was done with or without clinical support were also not distinguished.
Cancer Risk in Myotonic Dystrophy Type I: Evidence of a Role for Disease Severity.

Background Myotonic dystrophy type 1 (DM1) is an inherited trinucleotide repeat disorder in which specific cancers have been implicated as part of the disease phenotype. This study aimed to assess whether cancer risk in DM1 patients is modified by disease severity. Methods Using the United Kingdom Clinical Practice Research Datalink (primary care electronic medical records), we identified a cohort of 927 DM1 and a matched cohort of 13 085 DM1-free individuals between January 1, 1988 and February 29, 2016. We used Cox regression models to calculate the hazard ratios (HRs) and 95% confidence intervals (CIs) of organ-specific cancer risks. Analyses were stratified by age at DM1 diagnosis as a surrogate for disease severity. Statistical tests were two-sided. Results Patients with classic DM1 (age at diagnosis: 11-40 years) were at elevated risk of cancer overall (HR = 1.81; 95% CI = 1.12 to 2.93); cancers of the thyroid (HR = 15.93; 95% CI = 2.45 to 103.64), uterus (HR = 26.76; 95% CI = 2.32 to 309.26), and cutaneous melanoma (HR = 5.98; 95% CI = 1.24 to 28.79) accounted for the excess. In late-onset DM1 patients (age at diagnosis >40 years), a reduced overall cancer risk was observed (HR = 0.53; 95% CI = 0.32 to 0.85), possibly driven by the deficit in hematological malignancies (DM1 = 0 cases, DM1-free = 54 cases; P = .02). The difference between the observed HR for classic and late-onset DM1 was statistically significant (P < .001). Conclusions The observed difference in relative cancer risk between classic and late-onset DM1 patients compared with their DM1-free counterparts provides the first evidence that disease severity modifies DM1-related cancer susceptibility. This novel finding may guide clinical management and scientific investigations for the underlying molecular mechanisms in DM-related carcinogenesis.