Miracle-dz/UniFAR-FTUs huggingface dataset:
Provable Overlapping Community Detection in Weighted Graphs

Community detection is a widely-studied unsupervised learning problem in which the task is to group similar entities together based on observed pairwise entity interactions. This problem has applications in diverse domains such as social network analysis and computational biology. There is a significant amount of literature studying this problem under the assumption that the communities do not overlap. When the communities are allowed to overlap, often a pure nodes assumption is made, i.e. each community has a node that belongs exclusively to that community. This assumption, however, may not always be satisfied in practice. In this paper, we provide a provable method to detect overlapping communities in weighted graphs without explicitly making the pure nodes assumption. Moreover, contrary to most existing algorithms, our approach is based on convex optimization, for which many useful theoretical properties are already known. We demonstrate the success of our algorithm on artificial and real-world datasets.
Online Information Search During COVID-19

Public information search data from sources such as Google Trends affords researchers a perspective on what society does not know, or what society wants to find out prompted by, or in response to, developments in societal communication and news media events. In times of crisis, online public information search thus offers a window into the most urgent concerns of, and demands by society. By extension, such data offers a window into upcoming, and currently pressing demands on businesses, policymakers and researchers. This research note aims to illustrate some of these themes with a visual-based overview of the current COVID-19 / Coronavirus crisis.
Self-supervised training for blind multi-frame video denoising

We propose a self-supervised approach for training multi-frame video denoising networks. These networks predict each frame from a stack of frames around it. Our self-supervised approach benefits from the temporal consistency in the video by minimizing a loss that penalizes the difference between the predicted frame and a neighboring one, after aligning them using an optical flow. We use the proposed strategy to denoise a video contaminated with an unknown noise type, by fine-tuning a pre-trained denoising network on the noisy video. The proposed fine-tuning reaches and sometimes surpasses the performance of state-of-the-art networks trained with supervision. We demonstrate this by showing extensive results on video blind denoising of different synthetic and real noises. In addition, the proposed fine-tuning can be applied to any parameter that controls the denoising performance of the network. We show how this can be expoited to perform joint denoising and noise level estimation for heteroscedastic noise.
Large deviation principles via spherical integrals

In this article, we develop a framework to study the large deviation principle for matrix models and their quantized versions, by tilting the measures using the limits of spherical integrals obtained in [46,47]. As examples, we obtain 1. the large deviation principle for the empirical distribution of the diagonal entries of $UB_NU^*$, for a sequence of $N\times N$ diagonal matrices $B_N$ and unitary Haar distributed matrices $U$; 2. a large deviation upper bound for the empirical eigenvalue distribution of $A_N+UB_NU^*$, for two sequences of $N\times N$ diagonal matrices $A_N, B_N$, and their complementary lower bounds at "good'' probability distributions; 3. the large deviation principle for the Kostka number $K_{\boldsymbol\lambda_N \boldsymbol\eta_N}$, for two sequences of partitions $\boldsymbol\lambda_N, \boldsymbol\eta_N$ with at most $N$ rows; 4. a large deviation upper bound for the Littlewood-Richardson coefficients $c_{\boldsymbol\lambda_N \boldsymbol\eta_N}^{\boldsymbol\kappa_N}$, for three sequences of partitions $\boldsymbol\lambda_N, \boldsymbol\eta_N, \boldsymbol\kappa_N$ with at most $N$ rows, and their complementary lower bounds at "good" probability distributions.
Channel Feedback for Reconfigurable Intelligent Surface Assisted Wireless Communications

Reconfigurable intelligent surface (RIS) has recently received increasing interest due to the superiority of changing the wireless propagation environments intelligently. Channel feedback is essential in frequency division duplex (FDD) RIS-assisted wireless communications, since the downlink channel state information (CSI) has to be acquired by the base station (BS) for the joint beamforming at the BS and the RIS. In this paper, we first investigate the single-structured sparsity of RIS channel, which means that the angular-domain sparse channels of different users share the same non-zero element indices only in the column dimension, but not in the row dimension. Based on this channel characteristic, we then propose a dimension-reduced channel feedback scheme with a low overhead for channel feedback. Specifically, the downlink CSI can be decomposed into the structured CSI and unstructured CSI. The structured CSI of the multi-user channels can be fed back to the BS by only one user according to the single-structured sparsity, while the unstructured CSI can be fed back with a fairly low overhead by different users, respectively. Moreover, by utilizing the angle coherence time, the per-user overhead can be reduced further, while the near-optimal per-user rate can still be guaranteed.
Exploring Probabilistic Soft Logic as a framework for integrating top-down and bottom-up processing of language in a task context

This technical report describes a new prototype architecture designed to integrate top-down and bottom-up analysis of non-standard linguistic input, where a semantic model of the context of an utterance is used to guide the analysis of the non-standard surface forms, including their automated normalization in context. While the architecture is generally applicable, as a concrete use case of the architecture we target the generation of semantically-informed target hypotheses for answers written by German learners in response to reading comprehension questions, where the reading context and possible target answers are given. The architecture integrates existing NLP components to produce candidate analyses on eight levels of linguistic modeling, all of which are broken down into atomic statements and connected into a large graphical model using Probabilistic Soft Logic (PSL) as a framework. Maximum a posteriori inference on the resulting graphical model then assigns a belief distribution to candidate target hypotheses. The current version of the architecture builds on Universal Dependencies (UD) as its representation formalism on the form level and on Abstract Meaning Representations (AMRs) to represent semantic analyses of learner answers and the context information provided by the target answers. These general choices will make it comparatively straightforward to apply the architecture to other tasks and other languages.
Stealthy MTD Against Unsupervised Learning-Based Blind FDI Attacks in Power Systems

This paper examines how moving target defenses (MTD) implemented in power systems can be countered by unsupervised learning-based false data injection (FDI) attack and how MTD can be combined with physical watermarking to enhance the system resilience. A novel intelligent attack, which incorporates dimensionality reduction and density-based spatial clustering, is developed and shown to be effective in maintaining stealth in the presence of traditional MTD strategies. In resisting this new type of attack, a novel implementation of MTD combining with physical watermarking is proposed by adding Gaussian watermark into physical plant parameters to drive detection of traditional and intelligent FDI attacks, while remaining hidden to the attackers and limiting the impact on system operation and stability.
RAPD-based polymorphism specific to fuscous blight bacterium in grain legume

Three bacterial blights, namely common, fuscous and halo blight are the major biotic threats in grain legumes production. The polymorphism in genomic fingerprints of the three blight bacteria was studied using random primers. Among the primers tested, SBSB02 specifically amplified two fragments of ~300 bp and ~1600 bp in fuscous blight bacterium, but not in common and halo blight bacterium. The polymorphic bands were found in all 8 isolates of X. c. pv. phaseoli var. fuscans , suggesting a high degree of conservation of the amplified regions.
Explaining Regression Based Neural Network Model

Several methods have been proposed to explain Deep Neural Network (DNN). However, to our knowledge, only classification networks have been studied to try to determine which input dimensions motivated the decision. Furthermore, as there is no ground truth to this problem, results are only assessed qualitatively in regards to what would be meaningful for a human. In this work, we design an experimental settings where the ground truth can been established: we generate ideal signals and disrupted signals with errors and learn a neural network that determines the quality of the signals. This quality is simply a score based on the distance between the disrupted signals and the corresponding ideal signal. We then try to find out how the network estimated this score and hope to find the time-step and dimensions of the signal where errors are present. This experimental setting enables us to compare several methods for network explanation and to propose a new method, named AGRA for Accurate Gradient, based on several trainings that decrease the noise present in most state-of-the-art results. Comparative results show that the proposed method outperforms state-of-the-art methods for locating time-steps where errors occur in the signal.
Graph-theory treatment of one-dimensional strongly repulsive fermions

One-dimensional atomic mixtures of fermions can effectively realize spin chains and thus constitute a clean and controllable platform to study quantum magnetism. Such strongly correlated quantum systems are also of sustained interest to quantum simulation and quantum computation due to their computational complexity. In this article, we exploit spectral graph theory to completely characterize the symmetry properties of one-dimensional fermionic mixtures in the strong interaction limit. We also develop a powerful method to obtain the so-called Tan contacts associated with certain symmetry classes. In particular, compared to brute force diagonalization that is already virtually impossible for a moderate number of fermions, our analysis enables us to make unprecedented efficient predictions about the energy gap of complex spin mixtures. Our theoretical results are not only of direct experimental interest, but also provide important guidance for the design of adiabatic control protocols in strongly correlated fermion mixtures.
UvA-DARE ( Digital Academic Repository ) Asymptotic-bound-state model for Feshbach resonances

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Fatty Acid Content and Enzymes of Fatty Acid Metabolism in Overwintering Cold-Hardy Gall Insects

Fatty acid content and enzymes of fatty acid metabolism were studied in overwintering larvae of two cold-hardy gall insects, the freeze-tolerant fly Eurosta solidaginis and the freeze-avoiding moth Epiblema scudderiana. Both species increased the proportion of unsaturated fatty acids during the winter. Whereas total lipid content did not change in Eurosta solidaginis, a decrease in total lipids over the winter in Epiblema scudderiana suggested the use of fat reserves to maintain basal metabolism. Changes in the activities of enzymes of fat oxidation correlated with these observations in Eurosta solidaginis: hydroxyacyl-CoA dehydrogenase, carnitine-palmitoyl transferase, and acetoacetyl-CoA thiolase activities all decreased during overwintering. In Epiblema scudderiana the same activities were constant, decreased, or increased. These activities were, however, higher in the fat-oxidizing, freeze-avoiding species than in the freeze-tolerant larvae. Lipid content in Epiblema scudderiana increased again by early spring, possibly indicating this pool as the fate of carbon derived from the spring clearance of the cryoprotectant glycerol pool. Decreased activities of malic enzyme and ATP-citrate lyase suggested decreased potential for fatty acid synthesis in both species over the winter, consistent with the cessation offeeding in the fall. The potential for ketone body metabolism, measured as the activity of P-hydroxybutyrate dehydrogenase, increased greatly in both species during overwintering; however, levels ofβ-hydroxybutyrate remained less than 0.35 μmol/g wet mass throughout the study period. These data indicate that changes to storage lipid profiles in order to maintain fluidity and to lipid-metabolizing enzyme activities may play important roles in insect cold hardiness.
First Place: A prospective, randomized controlled trial of the impact of written discharge instructions for postoperative opioids on patient pain satisfaction and on minimizing opioid risk exposure in orthopaedic surgery

Background: Prescription opioid misuse is a significant issue with multiple consequences for our patients and society. The purpose of this study was to evaluate the role of a postoperative pain guideline pamphlet on patient pain satisfaction, on number of patients seeking a renewal prescription, and on appropriate disposal of leftover prescription medication. Methods: This prospective, randomized controlled trial included patients aged 18 to 65 yr undergoing elective foot and ankle surgery, who were opioid naïve and had no preexisting chronic pain conditions. Patients consenting to participate were divided into low-, medium-, and high-use groups according to anticipated postoperative prescription narcotic usage. Patients in each group were randomized to the control group that received no written discharge instructions, or the intervention group that received written instructions outlining postoperative pain expectations and recommendations for opioid medication usage and disposal. Both groups received equivalent prescriptions targeted to the use group (low, medium, or high). At the 4-week postoperative mark, a telephone interview was conducted to evaluate the primary outcome of pain satisfaction using a modified brief pain inventory. Secondary outcomes included renewal of opioid prescription and disposal method of leftover medication. Results: A total of 80 patients were recruited for this study. Of the 80 patients, 22 were classified as low use, 45 moderate use, and 13 high use. There was no difference among group demographics, other than the surgery being performed. Of the 80 patients, 41 received postoperative pain instructions and 39 did not. On average, pain satisfaction postoperatively was 8.19/10, where 10/10 represented completely satisfied. Interference scores averaged 27.18/70. Interestingly, only seven patients (six moderate use and one high use) used the entirety of their prescription, and only five patients required a renewal of their prescription. Only five patients returned their surplus medication to the pharmacy despite 41 patients receiving direct instructions to do so. Conclusions: Written discharge instructions in this patient population did not appear to improve postoperative pain scores. Interestingly, this study captured that a small percentage of postoperative pain medication prescriptions are completely utilized. Even more concerning is the fact that very little of the leftover pain medication is disposed of appropriately. Overprescribing narcotic medication may contribute to the issue of opioid abuse, and we should consider our role in minimizing opioid prescriptions postoperatively.
Holistic sexuality post gynaecological cancer treatment: A review of recent literature

Background: Sexual difficulties post gynaecological cancer treatment are commonplace and can impact multiple aspects of a woman’s life. Yet, health care practitioners remain reluctant to discuss sexuality from a holistic perspective with their patients.Aim: This article reviewed the literature on sexuality post gynaecological cancer treatment from the last 10 years. The aim was to understand research trends as well as identify gaps in the field.Methods: The review was framed using the neo-theoretical framework of sexuality. A comprehensive literature search, using the electronic databases EBSCOHost, Ovid, Clinical Key and PubMed, was conducted for articles concentrating on sexuality after gynaecological cancer treatment, published from 2007 to 2017 in English.Results: The results showed that the majority of the literature still approaches sexuality post treatment from a biomedical perspective with a focus on physical sexual functioning. However, there has been a slight shift towards understanding sexuality from a comprehensive standpoint, although there is a dearth of research relating to the psychological and relational aspects of sexuality.Conclusion: Comprehensive sexuality post treatment should be given further consideration in South Africa, and our unique socio-cultural context ought to be taken into consideration. Additionally, interventions at multiple levels should be explored, such as broadening sexual health training within the medical curriculum, considering the development of holistic cancer clinics and widening the scope of research relating to gynaecological cancer care.
Genetic analysis of potato tuber quality traits

In this thesis the results of the four year project P5 within the Centre for BioSystems Genomics ( www.cbsg.nl ) are described. The intention of this project was to gain understanding of the genetic principles underlying a series of different potato tuber quality traits and the development of molecular markers linked to the traits of interest. Molecular markers that can be used in breeding programs with the aim of producing potato varieties with improved quality. To provide a window in which the research of this thesis can be placed, a review of the current status of quality genetics in potato is given in chapter 1. Here the different quality traits applicable for potato are described and different breeding methodologies discussed. Enzymatic discoloration (ED) of potato tubers was investigated in chapter 2. QTL analysis of the trait itself an it’s potential substrates was performed in combination with an allele specificity study (including expression analysis) on the candidate genes PPO. In chapter 3, potato starch related traits and the underlying starch biosynthesis and degradation are investigated by a QTL analysis in combination with a candidate gene approach. Furthermore, in chapter 4 we analyzed potato tuber metabolic content through liquid chromatography-time of flight mass spectrometry (LC-QTOF MS). This resulted in a large set of mass peaks. Clustering of the signals through a multivariate mass spectra reconstruction strategy revealed reconstructed metabolites (ea centrotypes). Quantitative Trait Locus (QTL) analysis of the centrotypes revealed over 800 metabolite QTLs (mQTLs), distributed over all 12 chromosomes. To investigate the contribution of the metabolome to phenotypic traits, the mQTL results were compared with the QTL results for tuber flesh colour (raw and after-cooking) and total tuber protein content. Finally in chapter 5, the general discussion, the results obtained in the preceding chapters are discussed and placed in the bigger perspective of finding tools for breeding for quality traits in potato. Prospects on the basis of these results and an outlook for breeding for quality are presented as well.
Origin of apparent viscosity in yield stress fluids below yielding

For more than 20 years it has been debated if yield stress fluids are solid below the yield stress or actually flow; whether true yield stress fluids exist or not. Advocates of the true yield stress picture have demonstrated that the effective viscosity increases very rapidly as the stress is decreased towards the yield stress. Opponents have shown that this viscosity increase levels off, and that the material behaves as a Newtonian fluid of very high viscosity below the yield stress. In this paper, we demonstrate experimentally (on four different materials, using three different rheometers, five different geometries, and two different measurement methods) that the low-stress Newtonian viscosity is an artifact that arises in non–steady-state experiments. For measurements as long as 104 seconds we find that the value of the “Newtonian viscosity” increases indefinitely. This proves that the yield stress exists and marks a sharp transition between flowing states and states where the steady-state viscosity is infinite —a solid!
Varying Cognitive Scars – Differential Associations Between Types of Childhood Maltreatment and Facial Emotion Processing

Background Distorted cognitive processing has been found among survivors of child maltreatment. However, different types of abuse and neglect may bring about differences in emotion and attention processing. The present study aimed to detect differential associations between various types of childhood maltreatment and attentional biases in facial emotion processing. Methods A non-clinical sample was recruited on University campus and consisted of 67 individuals with varying degrees of maltreatment. In an evaluative conditioning task, images of faces with neutral emotional expressions were either associated with short videos of intense negative statements, or associated with neutral videos. Subsequently, these faces were used as stimuli in a face in the crowd recognition task in which the familiar faces had to be recognized within a crowd of unfamiliar neutral faces. Results In multiple linear regression analyses controlling for the intercorrelatedness of types of maltreatment, differential relationships between types of maltreatment and attentional bias were found. While emotional abuse was associated with faster detection of negatively associated faces, emotional neglect was associated with an impaired recognition of familiar stimuli regardless of the emotional content. Conclusion Results indicated that interindividual differences in cognitive biases may be due to the activation of diverse cognitive schemas based on differential experiences of maltreatment.